PERFORMANCE DEGRADATION DETECTION IN HYBRID DETECTION AND RANGING SYSTEM

A Hybrid Detection and Ranging (HyDAR) system configured for detecting signals with multiple wavelengths is provided. The system comprises: a laser light source providing laser light signals; an aperture window; one or more steering mechanisms configured to perform: directing the laser light signals toward the aperture window, receiving first return light signals formed based on at least a portion of the laser light signals provided by the laser light source, and receiving second return light signals formed from light provided by one or more light sources external to the HyDAR system. The system further includes a multimodal sensor including a Light Detection and Ranging (LiDAR) sensor and an image sensor. The point cloud data and the image data are at least partially time-and-space synchronized at the hardware-level of the HyDAR system. The system further includes a controller configured to detect one or more degradation factors affecting the HyDAR system's performance.

FIELD OF THE TECHNOLOGY

This disclosure relates generally to light detection and, more particularly, to detecting performance degradation related to a hybrid detection and ranging (HyDAR) system configured to detect signals with multiple wavelengths.

BACKGROUND

Light detection and ranging (LiDAR) systems use light pulses to create an image or point cloud of the external environment. A LiDAR system may be a scanning or non-scanning system. Some typical scanning LiDAR systems include a light source, a light transmitter, a light steering system, and a light detector. The light source generates a light beam that is directed by the light steering system in particular directions when being transmitted from the LiDAR system. When a transmitted light beam is scattered or reflected by an object, a portion of the scattered or reflected light returns to the LiDAR system to form a return light pulse. The light detector detects the return light pulse. Using the difference between the time that the return light pulse is detected and the time that a corresponding light pulse in the light beam is transmitted, the LiDAR system can determine the distance to the object based on the speed of light. This technique of determining the distance is referred to as the time-of-flight (ToF) technique. The light steering system can direct light beams along different paths to allow the LiDAR system to scan the surrounding environment and produce images or point clouds. A typical non-scanning LiDAR system illuminates an entire field-of-view (FOV) rather than scanning through the FOV. An example of the non-scanning LiDAR system is a flash LiDAR, which can also use the ToF technique to measure the distance to an object. LiDAR systems can also use techniques other than time-of-flight and scanning to measure the surrounding environment.

A hybrid detection and ranging (HyDAR) system may include a LiDAR system and one or more other types of sensors, such as one or more cameras, one or more Radar sensors, one or more ultrasonic sensors, and/or other sensors. The LiDAR system and the one or more other types of sensors may be integrated in the HyDAR system to form a compact multimodal sensor.

SUMMARY

A multimodal sensor of a HyDAR system may be configured to be compact so that it can be easily mounted to a moveable platform like a vehicle. When a vehicle operates over time, there may be one or more degradation factors that affect the HyDAR system's performance. The degradation factors may include, for example, at least a partial aperture window blockage, interference signals from one or more interference light sources, HyDAR system extrinsic calibration degradation, and HyDAR system intrinsic calibration degradation. The degradation factors may affect the LiDAR sensor in the HyDAR system. Thus, in this disclosure, the techniques and methods are described for degradation factors with respect to the HyDAR system including the LiDAR sensor. With respect to window blockage, the technologies described herein can determine the type of blockage on a high level in real-time with limited computational requirements. One embodiment of the method uses machine learning algorithms to detect blockage and trigger cleaning and clearing actions either by the HyDAR system or external accessories.

Interference signals are another degradation factor for the HyDAR system. To avoid such interference signals, the corresponding part of the point cloud can be discarded, a laser source in the HyDAR system can be powered off, the laser power may be increased to increase the light intensity such that it is greater than the interference light signals; and/or the steering mechanism of the LiDAR sensor can be adjusted in the direction of the interference light sources for tuning the FOV of the LiDAR sensor. This disclosure also provides methods involving the image sensor and the LiDAR sensor in a HyDAR system to improve detection performance. The methods described herein can be applied for detecting interference light signals (e.g., direct sunlight, laser interference, oncoming vehicle high beams, etc.) and adaptively control the HyDAR system to minimize performance deterioration.

Another HyDAR performance degradation factor relates to extrinsic calibration of the HyDAR system including the LiDAR sensor. This disclosure provides technologies and methods for dynamically detecting and monitoring extrinsic calibration degradation. The HyDAR system provided for such purposes require no synchronization or data fusing above the hardware level. Using the disclosed HyDAR system, the point cloud data provided by the LiDAR sensor and the image data provided by the image sensor are at least partially time-and-space synchronized at the hardware level of the HyDAR system, therefore requiring no significant software-based computation or no software computation, transformation, or data fusion at all. Furthermore, if the degradation is within a pre-defined threshold, the HyDAR system can adjust itself while the moveable platform continues to operate.

Another performance degradation factor for a HyDAR system relates to the intrinsic calibration degradation over time. The present disclosure provides techniques and methods that use the LiDAR sensor and the image sensor in the HyDAR system to detect and monitor such an intrinsic calibration degradation associated with misaligned internal components. In some examples, the HyDAR system can trigger warnings and self-correction of the intrinsic calibration. This can also help determining the root cause of the performance degradation through trained data sets from the HyDAR system data. Benefit from the performance degradation detection, at least the following major intrinsic calibration parameter degradation can be monitored, corrected, and/or reported by the methods described herein: distance correction; oscillation mirror offsets; polygon mirror offsets; and geometric parameters.

Embodiments of present invention are described below. In various embodiments of the present invention, a Hybrid Detection and Ranging (HyDAR) system configured for detecting signals with multiple wavelengths is provided. The HyDAR system comprises a laser light source providing laser light signals; an aperture window; and one or more steering mechanisms configured to perform: directing the laser light signals toward the aperture window, receiving first return light signals formed based on at least a portion of the laser light signals provided by the laser light source, and receiving second return light signals formed from light provided by one or more light sources external to the HyDAR system. The HyDAR system further comprises a multimodal sensor including a Light Detection and Ranging (LiDAR) sensor and an image sensor. The LiDAR sensor is configured to detect the first return light signals to obtain one or more frames of point cloud data. The image sensor is configured to detect the second return light signals to obtain one or more frames of image data. The point cloud data and the image data are at least partially time-and-space synchronized at the hardware-level of the HyDAR system. The HyDAR system further includes a controller configured to perform: detecting one or more degradation factors affecting the HyDAR system's performance, and in response to detecting the one or more degradation factors, causing adjustment of a device configuration or an operational condition of the HyDAR system to remove or reduce effects of the degradation factors.

DETAILED DESCRIPTION

To provide a more thorough understanding of various embodiments of the present invention, the following description sets forth numerous specific details, such as specific configurations, parameters, examples, and the like. It should be recognized, however, that such description is not intended as a limitation on the scope of the present invention but is intended to provide a better description of the exemplary embodiments.

Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise:

The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. Thus, as described below, various embodiments of the disclosure may be readily combined, without departing from the scope or spirit of the invention.

As used herein, the term “or” is an inclusive “or” operator and is equivalent to the term “and/or,” unless the context clearly dictates otherwise.

The term “based on” is not exclusive and allows for being based on additional factors not described unless the context clearly dictates otherwise.

As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously. Within the context of a networked environment where two or more components or devices are able to exchange data, the terms “coupled to” and “coupled with” are also used to mean “communicatively coupled with”, possibly via one or more intermediary devices. The components or devices can be optical, mechanical, and/or electrical devices.

Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first light signal could be termed a second light signal and, similarly, a second light signal could be termed a first light signal, without departing from the scope of the various described examples. The first light signal and the second light signal can both be light signal and, in some cases, can be separate and different light signals.

In addition, throughout the specification, the meaning of “a”, “an”, and “the” includes plural references, and the meaning of “in” includes “in” and “on”.

Although some of the various embodiments presented herein constitute a single combination of inventive elements, it should be appreciated that the inventive subject matter is considered to include all possible combinations of the disclosed elements. As such, if one embodiment comprises elements A, B, and C, and another embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly discussed herein. Further, the transitional term “comprising” means to have as parts or members, or to be those parts or members. As used herein, the transitional term “comprising” is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.

As used in the description herein and throughout the claims that follow, when a system, engine, server, device, module, or other computing element is described as being configured to perform or execute functions on data in a memory, the meaning of “configured to” or “programmed to” is defined as one or more processors or cores of the computing element being programmed by a set of software instructions stored in the memory of the computing element to execute the set of functions on target data or data objects stored in the memory.

As described above, a HyDAR system may include an integrated LiDAR sensor and one or more other sensors to form a multimodal sensor. The multimodal sensor may be configured to be compact so that it can be easily mounted to a moveable platform like a vehicle. A HyDAR system may have one or more degradation factors that affect its performance over time. The degradation factors may include, for example, at least a partial aperture window blockage, interference signals from one or more interference light sources, HyDAR system extrinsic calibration degradation, and HyDAR system intrinsic calibration degradation. It is understood that these examples are not limiting and are just examples of degradation factors commonly encountered by the HyDAR system.

Beginning with the window blockage as a performance degradation factor, for most of LiDAR systems on the market today, the aperture window is susceptible to undesired blockage. This is especially true for a LiDAR sensor mounted on a vehicle. For instance, the aperture window may be exposed to rain drops, fog condensation, debris falling onto the aperture window, etc. The present disclosure describes technologies that uses the multimodal sensor of a HyDAR system to detect at least a partial aperture window blockage of the LiDAR sensor, the image sensor, or the HyDAR system. The technologies described herein can also determine the type of blockage on a high level in real-time with limited computational requirements. One embodiment of the method uses machine learning algorithms to detect blockage and trigger cleaning and clearing actions either by the HyDAR system or its external accessories.

Turning to the next performance degradation factor, a LiDAR sensor in the HyDAR system can be sensitive to external interference light signals including, for example, direct sunlight, light from other LiDAR systems, vehicle headlights, streetlights, etc. Interference light signals from the external interference light source may cause performance degradation of the HyDAR system. For example, interference light signals can lead to abnormal behavior and cause harm to both the sensor itself and the agents around the sensor. This disclosure provides examples configurations of a multimodal sensor in a HyDAR system for reducing or preventing misdetection or performance degradation. For example, different kinds of interference light sources, even malicious laser scrambler in the near field, can be determined by using an image sensor of the multimodal sensor in the HyDAR system. In one example, to avoid such interference signals, the corresponding part of the point cloud can be discarded, a laser source in the HyDAR system can be powered off, the laser power may be increased to increase the transmission light intensity such that the signal-to-noise (SNR) ratio is improved; and/or the steering mechanism of the LiDAR sensor can be adjusted in the direction of the interference light sources for tuning the FOV of the LiDAR sensor. The present disclosure also provides methods involving the image sensor and the LiDAR sensor in a HyDAR system to improve detection performance. The method can be applied for detecting interference light signals (e.g., direct sunlight, laser interference, oncoming vehicle high beams, etc.) and adaptively control the HyDAR system to minimize performance deterioration.

Another HyDAR performance degradation factor relates to extrinsic calibration of the HyDAR system including the LiDAR sensor and an image sensor. In autonomous industry, extrinsic calibration of a forward-looking camera and LiDAR sensor can be a difficult issue to solve since it requires accurate synchronization between the two sensors and specific calibration setup. The extrinsic calibration parameters may also degrade as the moveable platform (e.g., a vehicle) operates in harsh environmental conditions throughout its life. The extrinsic calibration measures the relation between the HyDAR system and the moveable platform to which the HyDAR system is mounted. When the HyDAR system is first manufactured and mounted to the moveable platform, the HyDAR system is calibrated to have the correct position and orientation such that it can operate to accurately detect objects in its FOV. Overtime, the extrinsic calibration of the HyDAR system may change due to various factors like environmental conditions, wear and tear, user induced errors, etc. Accordingly, the extrinsic calibration of the HyDAR system may degrade overtime, and in turn the performance of the HyDAR system may be negatively affected.

The present disclosure provides technologies and methods for dynamically detecting and monitoring extrinsic calibration degradation. The HyDAR system provided for such purposes require no synchronization or data fusing above the hardware level. Using the disclosed HyDAR system, the point cloud data provided by the LiDAR sensor and the image data provided by the image sensor are at least partially time-and-space synchronized at the hardware level of the HyDAR system, therefore requiring no significant software computation, transformation, or data fusion, or no software-level synchronization at all. Furthermore, if the degradation is within a pre-defined threshold, the HyDAR system can adjust itself while the moveable platform continues to operate.

Another performance degradation factor for a HyDAR system relates to the intrinsic calibration degradation over time. The intrinsic calibration of a HyDAR system relates to the calibration of the alignment of the internal components of the HyDAR system including the LiDAR sensor and the image sensor. The HyDAR system operates under outdoor all-weather environments where they may encounter vibration, wear and external damage. These harsh environmental conditions may sometimes cause the HyDAR system's intrinsic calibration to tilt off and in turn cause the performance degradation of the HyDAR system. The present disclosure uses the LiDAR sensor and image sensor in the HyDAR system to detect and monitor such an intrinsic calibration degradation associated with misaligned internal components. In some examples, the HyDAR system can trigger warnings and self-correction of the intrinsic calibration. This can also help determining the root cause of the performance degradation through trained data sets from the HyDAR system data. Benefitted from the performance degradation detection, at least the following major intrinsic calibration parameter degradation can be monitored, corrected, and reported by the methods described herein: distance correction; oscillation mirror offsets; polygon mirror offsets; and geometric parameters of the HyDAR system components.

Embodiments of present invention are described below. In various embodiments of the present invention, a Hybrid Detection and Ranging (HyDAR) system configured for detecting signals with multiple wavelengths is provided. The HyDAR system comprises a laser light source providing laser light signals; an aperture window; and one or more steering mechanisms configured to perform: directing the laser light signals toward the aperture window, receiving first return light signals formed based on at least a portion of the laser light signals provided by the laser light source, and receiving second return light signals formed from light provided by one or more light sources external to the HyDAR system. The HyDAR system further comprises a multimodal sensor including a LiDAR sensor and an image sensor. The LiDAR sensor is configured to detect the first return light signals to obtain one or more frames of point cloud data. The image sensor is configured to detect the second return light signals to obtain one or more frames of image data. The point cloud data and the image data are at least partially time-and-space synchronized at the hardware-level of the HyDAR system. The HyDAR system further includes a controller configured to perform: detecting one or more degradation factors affecting the HyDAR system's performance, in response to detecting the one or more degradation factors, and causing adjustment of a device configuration or an operational condition of the HyDAR system to remove or reduce effects of the degradation factors. Example HyDAR systems and various technologies for detecting performance degradations are described below in greater detail, beginning with description of a LiDAR system, which is often included in a HyDAR system.

FIG.1illustrates one or more example LiDAR systems110and120A-120I disposed or included in a motor vehicle100. Vehicle100can be a car, a sport utility vehicle (SUV), a truck, a train, a wagon, a bicycle, a motorcycle, a tricycle, a bus, a mobility scooter, a tram, a ship, a boat, an underwater vehicle, an airplane, a helicopter, an unmanned aviation vehicle (UAV), a spacecraft, etc. Motor vehicle100can be a vehicle having any automated level. For example, motor vehicle100can be a partially automated vehicle, a highly automated vehicle, a fully automated vehicle, or a driverless vehicle. A partially automated vehicle can perform some driving functions without a human driver's intervention. For example, a partially automated vehicle can perform blind-spot monitoring, lane keeping and/or lane changing operations, automated emergency braking, smart cruising and/or traffic following, or the like. Certain operations of a partially automated vehicle may be limited to specific applications or driving scenarios (e.g., limited to only freeway driving). A highly automated vehicle can generally perform all operations of a partially automated vehicle but with less limitations. A highly automated vehicle can also detect its own limits in operating the vehicle and ask the driver to take over the control of the vehicle when necessary. A fully automated vehicle can perform all vehicle operations without a driver's intervention but can also detect its own limits and ask the driver to take over when necessary. A driverless vehicle can operate on its own without any driver intervention.

In typical configurations, motor vehicle100comprises one or more LiDAR systems110and120A-120I. Each of LiDAR systems110and120A-120I can be a scanning-based LiDAR system and/or a non-scanning LiDAR system (e.g., a flash LiDAR). A scanning-based LiDAR system scans one or more light beams in one or more directions (e.g., horizontal and vertical directions) to detect objects in a field-of-view (FOV). A non-scanning based LiDAR system transmits laser light to illuminate an FOV without scanning. For example, a flash LiDAR is a type of non-scanning based LiDAR system. A flash LiDAR can transmit laser light to simultaneously illuminate an FOV using a single light pulse or light shot.

A LiDAR system is a frequently-used sensor of a vehicle that is at least partially automated. In one embodiment, as shown inFIG.1, motor vehicle100may include a single LiDAR system110(e.g., without LiDAR systems120A-120I) disposed at the highest position of the vehicle (e.g., at the vehicle roof). Disposing LiDAR system110at the vehicle roof facilitates a 360-degree scanning around vehicle100. In some other embodiments, motor vehicle100can include multiple LiDAR systems, including two or more of systems110and/or120A-120I. As shown inFIG.1, in one embodiment, multiple LiDAR systems110and/or120A-120I are attached to vehicle100at different locations of the vehicle. For example, LiDAR system120A is attached to vehicle100at the front right corner; LiDAR system120B is attached to vehicle100at the front center position; LiDAR system120C is attached to vehicle100at the front left corner; LiDAR system120D is attached to vehicle100at the right-side rear view mirror; LiDAR system120E is attached to vehicle100at the left-side rear view mirror; LiDAR system120F is attached to vehicle100at the back center position; LiDAR system120G is attached to vehicle100at the back right corner; LiDAR system120H is attached to vehicle100at the back left corner; and/or LiDAR system120I is attached to vehicle100at the center towards the backend (e.g., back end of the vehicle roof). It is understood that one or more LiDAR systems can be distributed and attached to a vehicle in any desired manner andFIG.1only illustrates one embodiment. As another example, LiDAR systems120D and120E may be attached to the B-pillars of vehicle100instead of the rear-view mirrors. As another example, LiDAR system120B may be attached to the windshield of vehicle100instead of the front bumper.

In some embodiments, LiDAR systems110and120A-120I are independent LiDAR systems having their own respective laser sources, control electronics, transmitters, receivers, and/or steering mechanisms. In other embodiments, some of LiDAR systems110and120A-120I can share one or more components, thereby forming a distributed sensor system. In one example, optical fibers are used to deliver laser light from a centralized laser source to all LiDAR systems. For instance, system110(or another system that is centrally positioned or positioned anywhere inside the vehicle100) includes a light source, a transmitter, and a light detector, but has no steering mechanisms. System110may distribute transmission light to each of systems120A-120I. The transmission light may be distributed via optical fibers. Optical connectors can be used to couple the optical fibers to each of system110and120A-120I. In some examples, one or more of systems120A-120I include steering mechanisms but no light sources, transmitters, or light detectors. A steering mechanism may include one or more moveable mirrors such as one or more polygon mirrors, one or more single plane mirrors, one or more multi-plane mirrors, or the like. Embodiments of the light source, transmitter, steering mechanism, and light detector are described in more detail below. Via the steering mechanisms, one or more of systems120A-120I scan light into one or more respective FOVs and receive corresponding return light. The return light is formed by scattering or reflecting the transmission light by one or more objects in the FOVs. Systems120A-120I may also include collection lens and/or other optics to focus and/or direct the return light into optical fibers, which deliver the received return light to system110. System110includes one or more light detectors for detecting the received return light. In some examples, system110is disposed inside a vehicle such that it is in a temperature-controlled environment, while one or more systems120A-120I may be at least partially exposed to the external environment.

FIG.2is a block diagram200illustrating interactions between vehicle onboard LiDAR system(s)210and multiple other systems including a vehicle perception and planning system220. LiDAR system(s)210can be mounted on or integrated to a vehicle. LiDAR system(s)210include sensor(s) that scan laser light to the surrounding environment to measure the distance, angle, and/or velocity of objects. Based on the scattered light that returned to LiDAR system(s)210, it can generate sensor data (e.g., image data or 3D point cloud data) representing the perceived external environment.

LiDAR system(s)210can include one or more of short-range LiDAR sensors, medium-range LiDAR sensors, and long-range LiDAR sensors. A short-range LiDAR sensor measures objects located up to about 20-50 meters from the LiDAR sensor. Short-range LiDAR sensors can be used for, e.g., monitoring nearby moving objects (e.g., pedestrians crossing street in a school zone), parking assistance applications, or the like. A medium-range LiDAR sensor measures objects located up to about 70-200 meters from the LiDAR sensor. Medium-range LiDAR sensors can be used for, e.g., monitoring road intersections, assistance for merging onto or leaving a freeway, or the like. A long-range LiDAR sensor measures objects located up to about 200 meters and beyond. Long-range LiDAR sensors are typically used when a vehicle is travelling at a high speed (e.g., on a freeway), such that the vehicle's control systems may only have a few seconds (e.g., 6-8 seconds) to respond to any situations detected by the LiDAR sensor. As shown inFIG.2, in one embodiment, the LiDAR sensor data can be provided to vehicle perception and planning system220via a communication path213for further processing and controlling the vehicle operations. Communication path213can be any wired or wireless communication links that can transfer data.

With reference still toFIG.2, in some embodiments, other vehicle onboard sensor(s)230are configured to provide additional sensor data separately or together with LiDAR system(s)210. Other vehicle onboard sensors230may include, for example, one or more camera(s)232, one or more radar(s)234, one or more ultrasonic sensor(s)236, and/or other sensor(s)238. Camera(s)232can take images and/or videos of the external environment of a vehicle. Camera(s)232can take, for example, high-definition (HD) videos having millions of pixels in each frame. A camera includes image sensors that facilitate producing monochrome or color images and videos. Color information may be important in interpreting data for some situations (e.g., interpreting images of traffic lights). Color information may not be available from other sensors such as LiDAR or radar sensors. Camera(s)232can include one or more of narrow-focus cameras, wider-focus cameras, side-facing cameras, infrared cameras, fisheye cameras, or the like. The image and/or video data generated by camera(s)232can also be provided to vehicle perception and planning system220via communication path233for further processing and controlling the vehicle operations. Communication path233can be any wired or wireless communication links that can transfer data. Camera(s)232can be mounted on, or integrated to, a vehicle at any location (e.g., rear-view mirrors, pillars, front grille, and/or back bumpers, etc.).

Other vehicle onboard sensor(s)230can also include radar sensor(s)234. Radar sensor(s)234use radio waves to determine the range, angle, and velocity of objects. Radar sensor(s)234produce electromagnetic waves in the radio or microwave spectrum. The electromagnetic waves reflect off an object and some of the reflected waves return to the radar sensor, thereby providing information about the object's position and velocity. Radar sensor(s)234can include one or more of short-range radar(s), medium-range radar(s), and long-range radar(s). A short-range radar measures objects located at about 0.1-30 meters from the radar. A short-range radar is useful in detecting objects located near the vehicle, such as other vehicles, buildings, walls, pedestrians, bicyclists, etc. A short-range radar can be used to detect a blind spot, assist in lane changing, provide rear-end collision warning, assist in parking, provide emergency braking, or the like. A medium-range radar measures objects located at about 30-80 meters from the radar. A long-range radar measures objects located at about 80-200 meters. Medium- and/or long-range radars can be useful in, for example, traffic following, adaptive cruise control, and/or highway automatic braking. Sensor data generated by radar sensor(s)234can also be provided to vehicle perception and planning system220via communication path233for further processing and controlling the vehicle operations. Radar sensor(s)234can be mounted on, or integrated to, a vehicle at any location (e.g., rear-view mirrors, pillars, front grille, and/or back bumpers, etc.).

Other vehicle onboard sensor(s)230can also include ultrasonic sensor(s)236. Ultrasonic sensor(s)236use acoustic waves or pulses to measure objects located external to a vehicle. The acoustic waves generated by ultrasonic sensor(s)236are transmitted to the surrounding environment. At least some of the transmitted waves are reflected off an object and return to the ultrasonic sensor(s)236. Based on the return signals, a distance of the object can be calculated. Ultrasonic sensor(s)236can be useful in, for example, checking blind spots, identifying parking spaces, providing lane changing assistance into traffic, or the like. Sensor data generated by ultrasonic sensor(s)236can also be provided to vehicle perception and planning system220via communication path233for further processing and controlling the vehicle operations. Ultrasonic sensor(s)236can be mounted on, or integrated to, a vehicle at any location (e.g., rear-view mirrors, pillars, front grille, and/or back bumpers, etc.).

In some embodiments, one or more other sensor(s)238may be attached in a vehicle and may also generate sensor data. Other sensor(s)238may include, for example, global positioning systems (GPS), inertial measurement units (IMU), or the like. Sensor data generated by other sensor(s)238can also be provided to vehicle perception and planning system220via communication path233for further processing and controlling the vehicle operations. It is understood that communication path233may include one or more communication links to transfer data between the various sensor(s)230and vehicle perception and planning system220.

In some embodiments, as shown inFIG.2, sensor data from other vehicle onboard sensor(s)230can be provided to vehicle onboard LiDAR system(s)210via communication path231. LiDAR system(s)210may process the sensor data from other vehicle onboard sensor(s)230. For example, sensor data from camera(s)232, radar sensor(s)234, ultrasonic sensor(s)236, and/or other sensor(s)238may be correlated or fused with sensor data LiDAR system(s)210, thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system220. It is understood that other configurations may also be implemented for transmitting and processing sensor data from the various sensors (e.g., data can be transmitted to a cloud or edge computing service provider for processing and then the processing results can be transmitted back to the vehicle perception and planning system220and/or LiDAR system210).

With reference still toFIG.2, in some embodiments, sensors onboard other vehicle(s)250are used to provide additional sensor data separately or together with LiDAR system(s)210. For example, two or more nearby vehicles may have their own respective LiDAR sensor(s), camera(s), radar sensor(s), ultrasonic sensor(s), etc. Nearby vehicles can communicate and share sensor data with one another. Communications between vehicles are also referred to as V2V (vehicle to vehicle) communications. For example, as shown inFIG.2, sensor data generated by other vehicle(s)250can be communicated to vehicle perception and planning system220and/or vehicle onboard LiDAR system(s)210, via communication path253and/or communication path251, respectively. Communication paths253and251can be any wired or wireless communication links that can transfer data.

Sharing sensor data facilitates a better perception of the environment external to the vehicles. For instance, a first vehicle may not sense a pedestrian that is behind a second vehicle but is approaching the first vehicle. The second vehicle may share the sensor data related to this pedestrian with the first vehicle such that the first vehicle can have additional reaction time to avoid collision with the pedestrian. In some embodiments, similar to data generated by sensor(s)230, data generated by sensors onboard other vehicle(s)250may be correlated or fused with sensor data generated by LiDAR system(s)210(or with other LiDAR systems located in other vehicles), thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system220.

In some embodiments, intelligent infrastructure system(s)240are used to provide sensor data separately or together with LiDAR system(s)210. Certain infrastructures may be configured to communicate with a vehicle to convey information and vice versa.

Communications between a vehicle and infrastructures are generally referred to as V2I (vehicle to infrastructure) communications. For example, intelligent infrastructure system(s)240may include an intelligent traffic light that can convey its status to an approaching vehicle in a message such as “changing to yellow in 5 seconds.” Intelligent infrastructure system(s)240may also include its own LiDAR system mounted near an intersection such that it can convey traffic monitoring information to a vehicle. For example, a left-turning vehicle at an intersection may not have sufficient sensing capabilities because some of its own sensors may be blocked by traffic in the opposite direction. In such a situation, sensors of intelligent infrastructure system(s)240can provide useful data to the left-turning vehicle. Such data may include, for example, traffic conditions, information of objects in the direction the vehicle is turning to, traffic light status and predictions, or the like. These sensor data generated by intelligent infrastructure system(s)240can be provided to vehicle perception and planning system220and/or vehicle onboard LiDAR system(s)210, via communication paths243and/or241, respectively. Communication paths243and/or241can include any wired or wireless communication links that can transfer data. For example, sensor data from intelligent infrastructure system(s)240may be transmitted to LiDAR system(s)210and correlated or fused with sensor data generated by LiDAR system(s)210, thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system220. V2V and V2I communications described above are examples of vehicle-to-X (V2X) communications, where the “X” represents any other devices, systems, sensors, infrastructure, or the like that can share data with a vehicle.

With reference still toFIG.2, via various communication paths, vehicle perception and planning system220receives sensor data from one or more of LiDAR system(s)210, other vehicle onboard sensor(s)230, other vehicle(s)250, and/or intelligent infrastructure system(s)240. In some embodiments, different types of sensor data are correlated and/or integrated by a sensor fusion sub-system222. For example, sensor fusion sub-system222can generate a 360-degree model using multiple images or videos captured by multiple cameras disposed at different positions of the vehicle. Sensor fusion sub-system222obtains sensor data from different types of sensors and uses the combined data to perceive the environment more accurately. For example, a vehicle onboard camera232may not capture a clear image because it is facing the Sun or a light source (e.g., another vehicle's headlight during nighttime) directly. A LIDAR system210may not be affected as much and therefore sensor fusion sub-system222can combine sensor data provided by both camera232and LiDAR system210, and use the sensor data provided by LiDAR system210to compensate the unclear image captured by camera232. As another example, in a rainy or foggy weather, a radar sensor234may work better than a camera232or a LiDAR system210. Accordingly, sensor fusion sub-system222may use sensor data provided by the radar sensor234to compensate the sensor data provided by camera232or LiDAR system210.

In other examples, sensor data generated by other vehicle onboard sensor(s)230may have a lower resolution (e.g., radar sensor data) and thus may need to be correlated and confirmed by LiDAR system(s)210, which usually has a higher resolution. For example, a sewage cover (also referred to as a manhole cover) may be detected by radar sensor234as an object towards which a vehicle is approaching. Due to the low-resolution nature of radar sensor234, vehicle perception and planning system220may not be able to determine whether the object is an obstacle that the vehicle needs to avoid. High-resolution sensor data generated by LiDAR system(s)210thus can be used to correlated and confirm that the object is a sewage cover and causes no harm to the vehicle.

Vehicle perception and planning system220further comprises an object classifier223. Using raw sensor data and/or correlated/fused data provided by sensor fusion sub-system222, object classifier223can use any computer vision techniques to detect and classify the objects and estimate the positions of the objects. In some embodiments, object classifier223can use machine-learning based techniques to detect and classify objects. Examples of the machine-learning based techniques include utilizing algorithms such as region-based convolutional neural networks (R-CNN), Fast R-CNN, Faster R-CNN, histogram of oriented gradients (HOG), region-based fully convolutional network (R-FCN), single shot detector (SSD), spatial pyramid pooling (SPP-net), and/or You Only Look Once (Yolo).

Vehicle perception and planning system220further comprises a road detection sub-system224. Road detection sub-system224localizes the road and identifies objects and/or markings on the road. For example, based on raw or fused sensor data provided by radar sensor(s)234, camera(s)232, and/or LiDAR system(s)210, road detection sub-system224can build a 3D model of the road based on machine-learning techniques (e.g., pattern recognition algorithms for identifying lanes). Using the 3D model of the road, road detection sub-system224can identify objects (e.g., obstacles or debris on the road) and/or markings on the road (e.g., lane lines, turning marks, crosswalk marks, or the like).

Vehicle perception and planning system220further comprises a localization and vehicle posture sub-system225. Based on raw or fused sensor data, localization and vehicle posture sub-system225can determine position of the vehicle and the vehicle's posture. For example, using sensor data from LiDAR system(s)210, camera(s)232, and/or GPS data, localization and vehicle posture sub-system225can determine an accurate position of the vehicle on the road and the vehicle's six degrees of freedom (e.g., whether the vehicle is moving forward or backward, up or down, and left or right). In some embodiments, high-definition (HD) maps are used for vehicle localization. HD maps can provide highly detailed, three-dimensional, computerized maps that pinpoint a vehicle's location. For instance, using the HD maps, localization and vehicle posture sub-system225can determine precisely the vehicle's current position (e.g., which lane of the road the vehicle is currently in, how close it is to a curb or a sidewalk) and predict vehicle's future positions.

Vehicle perception and planning system220further comprises obstacle predictor226. Objects identified by object classifier223can be stationary (e.g., a light pole, a road sign) or dynamic (e.g., a moving pedestrian, bicycle, another car). For moving objects, predicting their moving path or future positions can be important to avoid collision. Obstacle predictor226can predict an obstacle trajectory and/or warn the driver or the vehicle planning sub-system228about a potential collision. For example, if there is a high likelihood that the obstacle's trajectory intersects with the vehicle's current moving path, obstacle predictor226can generate such a warning. Obstacle predictor226can use a variety of techniques for making such a prediction. Such techniques include, for example, constant velocity or acceleration models, constant turn rate and velocity/acceleration models, Kalman Filter and Extended Kalman Filter based models, recurrent neural network (RNN) based models, long short-term memory (LSTM) neural network based models, encoder-decoder RNN models, or the like.

With reference still toFIG.2, in some embodiments, vehicle perception and planning system220further comprises vehicle planning sub-system228. Vehicle planning sub-system228can include one or more planners such as a route planner, a driving behaviors planner, and a motion planner. The route planner can plan the route of a vehicle based on the vehicle's current location data, target location data, traffic information, etc. The driving behavior planner adjusts the timing and planned movement based on how other objects might move, using the obstacle prediction results provided by obstacle predictor226. The motion planner determines the specific operations the vehicle needs to follow. The planning results are then communicated to vehicle control system280via vehicle interface270. The communication can be performed through communication paths227and271, which include any wired or wireless communication links that can transfer data.

Vehicle control system280controls the vehicle's steering mechanism, throttle, brake, etc., to operate the vehicle according to the planned route and movement. In some examples, vehicle perception and planning system220may further comprise a user interface260, which provides a user (e.g., a driver) access to vehicle control system280to, for example, override or take over control of the vehicle when necessary. User interface260may also be separate from vehicle perception and planning system220. User interface260can communicate with vehicle perception and planning system220, for example, to obtain and display raw or fused sensor data, identified objects, vehicle's location/posture, etc. These displayed data can help a user to better operate the vehicle. User interface260can communicate with vehicle perception and planning system220and/or vehicle control system280via communication paths221and261respectively, which include any wired or wireless communication links that can transfer data. It is understood that the various systems, sensors, communication links, and interfaces inFIG.2can be configured in any desired manner and not limited to the configuration shown inFIG.2.

FIG.3is a block diagram illustrating an example LiDAR system300. LiDAR system300can be used to implement LiDAR systems110,120A-120I, and/or210shown inFIGS.1and2. In one embodiment, LiDAR system300comprises a light source310, a transmitter320, an optical receiver and light detector330, a steering system340, and control circuitry350. These components are coupled together using communications paths312,314,322,332,342,352,362, and372. These communications paths include communication links (wired or wireless, bidirectional or unidirectional) among the various LiDAR system components, but need not be physical components themselves. While the communications paths can be implemented by one or more electrical wires, buses, or optical fibers, the communication paths can also be wireless channels or free-space optical paths so that no physical communication medium is present. For example, in one embodiment of LiDAR system300, communication path314between light source310and transmitter320may be implemented using one or more optical fibers. Communication paths332and352may represent optical paths implemented using free space optical components and/or optical fibers. And communication paths312,322,342, and362may be implemented using one or more electrical wires that carry electrical signals. The communications paths can also include one or more of the above types of communication mediums (e.g., they can include an optical fiber and a free-space optical component, or include one or more optical fibers and one or more electrical wires).

In some embodiments, LiDAR system300can be a coherent LiDAR system. One example is a frequency-modulated continuous-wave (FMCW) LiDAR. Coherent LiDARs detect objects by mixing return light from the objects with light from the coherent laser transmitter. Thus, as shown inFIG.3, if LiDAR system300is a coherent LiDAR, it may include a route372providing a portion of transmission light from transmitter320to optical receiver and light detector330. Route372may include one or more optics (e.g., optical fibers, lens, mirrors, etc.) for providing the light from transmitter320to optical receiver and light detector330. The transmission light provided by transmitter320may be modulated light and can be split into two portions. One portion is transmitted to the FOV, while the second portion is sent to the optical receiver and light detector330of the LiDAR system300. The second portion is also referred to as the light that is kept local (LO) to the LiDAR system300. The transmission light is scattered or reflected by various objects in the FOV and at least a portion of it forms return light. The return light is subsequently detected and interferometrically recombined with the second portion of the transmission light that was kept local. Coherent LiDAR provides a means of optically sensing an object's range as well as its relative velocity along the line-of-sight (LOS).

LiDAR system300can also include other components not depicted inFIG.3, such as power buses, power supplies, LED indicators, switches, etc. Additionally, other communication connections among components may be present, such as a direct connection between light source310and optical receiver and light detector330to provide a reference signal so that the time from when a light pulse is transmitted until a return light pulse is detected can be accurately measured.

Light source310outputs laser light for illuminating objects in a field of view (FOV). The laser light can be infrared light having a wavelength in the range of 700 nm to 1 mm. Light source310can be, for example, a semiconductor-based laser (e.g., a diode laser) and/or a fiber-based laser. A semiconductor-based laser can be, for example, an edge emitting laser (EEL), a vertical cavity surface emitting laser (VCSEL), an external-cavity diode laser, a vertical-external-cavity surface-emitting laser, a distributed feedback (DFB) laser, a distributed Bragg reflector (DBR) laser, an interband cascade laser, a quantum cascade laser, a quantum well laser, a double heterostructure laser, or the like. A fiber-based laser is a laser in which the active gain medium is an optical fiber doped with rare-earth elements such as erbium, ytterbium, neodymium, dysprosium, prascodymium, thulium, and/or holmium. In some embodiments, a fiber laser is based on double-clad fibers, in which the gain medium forms the core of the fiber surrounded by two layers of cladding. The double-clad fiber allows the core to be pumped with a high-power beam, thereby enabling the laser source to be a high power fiber laser source.

In some embodiments, light source310comprises a master oscillator (also referred to as a seed laser) and power amplifier (MOPA). The power amplifier amplifies the output power of the seed laser. The power amplifier can be a fiber amplifier, a bulk amplifier, or a semiconductor optical amplifier. The seed laser can be a diode laser (e.g., a Fabry-Perot cavity laser, a distributed feedback laser), a solid-state bulk laser, or a tunable external-cavity diode laser. In some embodiments, light source310can be an optically pumped microchip laser. Microchip lasers are alignment-free monolithic solid-state lasers where the laser crystal is directly contacted with the end mirrors of the laser resonator. A microchip laser is typically pumped with a laser diode (directly or using a fiber) to obtain the desired output power. A microchip laser can be based on neodymium-doped yttrium aluminum garnet (Y3Al5O12) laser crystals (i.e., Nd:YAG), or neodymium-doped vanadate (i.e., ND:YVO4) laser crystals. In some examples, light source310may have multiple amplification stages to achieve a high power gain such that the laser output can have high power, thereby enabling the LiDAR system to have a long scanning range. In some examples, the power amplifier of light source310can be controlled such that the power gain can be varied to achieve any desired laser output power. An example of light source310is described in more detail below.

ReferencingFIG.3, typical operating wavelengths of light source310comprise, for example, about 850 nm, about 905 nm, about 940 nm, about 1064 nm, and about 1550 nm. For laser safety, the upper limit of maximum usable laser power is set by the U.S. FDA (U.S. Food and Drug Administration) regulations. The optical power limit at 1550 nm wavelength is much higher than those of the other aforementioned wavelengths. Further, at 1550 nm, the optical power loss in a fiber is low. There characteristics of the 1550 nm wavelength make it more beneficial for long-range LiDAR applications. The amount of optical power output from light source310can be characterized by its peak power, average power, pulse energy, and/or the pulse energy density. The peak power is the ratio of pulse energy to the width of the pulse (e.g., full width at half maximum or FWHM). Thus, a smaller pulse width can provide a larger peak power for a fixed amount of pulse energy. A pulse width can be in the range of nanosecond or picosecond. The average power is the product of the energy of the pulse and the pulse repetition rate (PRR). As described in more detail below, the PRR represents the frequency of the pulsed laser light. In general, the smaller the time interval between the pulses, the higher the PRR. The PRR typically corresponds to the maximum range that a LiDAR system can measure. Light source310can be configured to produce pulses at high PRR to meet the desired number of data points in a point cloud generated by the LiDAR system. Light source310can also be configured to produce pulses at medium or low PRR to meet the desired maximum detection distance. Wall plug efficiency (WPE) is another factor to evaluate the total power consumption, which may be a useful indicator in evaluating the laser efficiency. For example, as shown inFIG.1, multiple LiDAR systems may be attached to a vehicle, which may be an electrical-powered vehicle or a vehicle otherwise having limited fuel or battery power supply. Therefore, high WPE and intelligent ways to use laser power are often among the important considerations when selecting and configuring light source310and/or designing laser delivery systems for vehicle-mounted LiDAR applications.

It is understood that the above descriptions provide non-limiting examples of a light source310. Light source310can be configured to include many other types of light sources (e.g., laser diodes, short-cavity fiber lasers, solid-state lasers, and/or tunable external cavity diode lasers) that are configured to generate one or more light signals at various wavelengths. In some examples, light source310comprises amplifiers (e.g., pre-amplifiers and/or booster amplifiers), which can be a doped optical fiber amplifier, a solid-state bulk amplifier, and/or a semiconductor optical amplifier. The amplifiers are configured to receive and amplify light signals with desired gains.

With reference back toFIG.3, LiDAR system300further comprises a transmitter320. Light source310provides laser light (e.g., in the form of a laser beam) to transmitter320. The laser light provided by light source310can be amplified laser light with a predetermined or controlled wavelength, pulse repetition rate, and/or power level. Transmitter320receives the laser light from light source310and transmits the laser light to steering mechanism340with low divergence. In some embodiments, transmitter320can include, for example, optical components (e.g., lens, fibers, mirrors, etc.) for transmitting one or more laser beams to a field-of-view (FOV) directly or via steering mechanism340. WhileFIG.3illustrates transmitter320and steering mechanism340as separate components, they may be combined or integrated as one system in some embodiments. Steering mechanism340is described in more detail below.

Laser beams provided by light source310may diverge as they travel to transmitter320. Therefore, transmitter320often comprises a collimating lens or a lens group configured to collect the diverging laser beams and produce more parallel optical beams with reduced or minimum divergence. The collimated optical beams can then be further directed through various optics such as mirrors and lens. A collimating lens may be, for example, a single plano-convex lens or a lens group. The collimating lens can be configured to achieve any desired properties such as the beam diameter, divergence, numerical aperture, focal length, or the like. A beam propagation ratio or beam quality factor (also referred to as the M2factor) is used for measurement of laser beam quality. In many LiDAR applications, it is important to have good laser beam quality in the generated transmitting laser beam. The M2factor represents a degree of variation of a beam from an ideal Gaussian beam. Thus, the M2factor reflects how well a collimated laser beam can be focused on a small spot, or how well a divergent laser beam can be collimated. Therefore, light source310and/or transmitter320can be configured to meet, for example, a scan resolution requirement while maintaining the desired M2factor.

One or more of the light beams provided by transmitter320are scanned by steering mechanism340to a FOV. Steering mechanism340scans light beams in multiple dimensions (e.g., in both the horizontal and vertical dimension) to facilitate LiDAR system300to map the environment by generating a 3D point cloud. A horizontal dimension can be a dimension that is parallel to the horizon or a surface associated with the LiDAR system or a vehicle (e.g., a road surface). A vertical dimension is perpendicular to the horizontal dimension (i.e., the vertical dimension forms a 90-degree angle with the horizontal dimension). Steering mechanism340will be described in more detail below. The laser light scanned to an FOV may be scattered or reflected by an object in the FOV. At least a portion of the scattered or reflected light forms return light that returns to LiDAR system300.FIG.3further illustrates an optical receiver and light detector330configured to receive the return light. Optical receiver and light detector330comprises an optical receiver that is configured to collect the return light from the FOV. The optical receiver can include optics (e.g., lens, fibers, mirrors, etc.) for receiving, redirecting, focusing, amplifying, and/or filtering return light from the FOV. For example, the optical receiver often includes a collection lens (e.g., a single plano-convex lens or a lens group) to collect and/or focus the collected return light onto a light detector.

A light detector detects the return light focused by the optical receiver and generates current and/or voltage signals proportional to the incident intensity of the return light. Based on such current and/or voltage signals, the depth information of the object in the FOV can be derived. One example method for deriving such depth information is based on the direct TOF (time of flight), which is described in more detail below. A light detector may be characterized by its detection sensitivity, quantum efficiency, detector bandwidth, linearity, signal to noise ratio (SNR), overload resistance, interference immunity, etc. Based on the applications, the light detector can be configured or customized to have any desired characteristics. For example, optical receiver and light detector330can be configured such that the light detector has a large dynamic range while having a good linearity. The light detector linearity indicates the detector's capability of maintaining linear relationship between input optical signal power and the detector's output. A detector having good linearity can maintain a linear relationship over a large dynamic input optical signal range.

To achieve desired detector characteristics, configurations or customizations can be made to the light detector's structure and/or the detector's material system. Various detector structures can be used for a light detector. For example, a light detector structure can be a PIN based structure, which has an undoped intrinsic semiconductor region (i.e., an “i” region) between a p-type semiconductor and an n-type semiconductor region. Other light detector structures comprise, for example, an APD (avalanche photodiode) based structure, a PMT (photomultiplier tube) based structure, a SiPM (Silicon photomultiplier) based structure, a SPAD (single-photon avalanche diode) based structure, and/or quantum wires. For material systems used in a light detector, Si, InGaAs, and/or Si/Ge based materials can be used. It is understood that many other detector structures and/or material systems can be used in optical receiver and light detector330.

A light detector (e.g., an APD based detector) may have an internal gain such that the input signal is amplified when generating an output signal. However, noise may also be amplified due to the light detector's internal gain. Common types of noise include signal shot noise, dark current shot noise, thermal noise, and amplifier noise. In some embodiments, optical receiver and light detector330may include a pre-amplifier that is a low noise amplifier (LNA). In some embodiments, the pre-amplifier may also include a transimpedance amplifier (TIA), which converts a current signal to a voltage signal. For a linear detector system, input equivalent noise or noise equivalent power (NEP) measures how sensitive the light detector is to weak signals. Therefore, they can be used as indicators of the overall system performance. For example, the NEP of a light detector specifies the power of the weakest signal that can be detected and therefore it in turn specifies the maximum range of a LiDAR system. It is understood that various light detector optimization techniques can be used to meet the requirement of LiDAR system300. Such optimization techniques may include selecting different detector structures, materials, and/or implementing signal processing techniques (e.g., filtering, noise reduction, amplification, or the like). For example, in addition to, or instead of, using direct detection of return signals (e.g., by using ToF), coherent detection can also be used for a light detector. Coherent detection allows for detecting amplitude and phase information of the received light by interfering the received light with a local oscillator. Coherent detection can improve detection sensitivity and noise immunity.

FIG.3further illustrates that LiDAR system300comprises steering mechanism340. As described above, steering mechanism340directs light beams from transmitter320to scan an FOV in multiple dimensions. A steering mechanism is also referred to as a raster mechanism, a scanning mechanism, or simply a light scanner. Scanning light beams in multiple directions (e.g., in both the horizontal and vertical directions) facilitates a LiDAR system to map the environment by generating an image or a 3D point cloud. A steering mechanism can be based on mechanical scanning and/or solid-state scanning. Mechanical scanning uses rotating mirrors to steer the laser beam or physically rotate the LiDAR transmitter and receiver (collectively referred to as transceiver) to scan the laser beam. Solid-state scanning directs the laser beam to various positions through the FOV without mechanically moving any macroscopic components such as the transceiver. Solid-state scanning mechanisms include, for example, optical phased arrays based steering and flash LiDAR based steering. In some embodiments, because solid-state scanning mechanisms do not physically move macroscopic components, the steering performed by a solid-state scanning mechanism may be referred to as effective steering. A LiDAR system using solid-state scanning may also be referred to as a non-mechanical scanning or simply non-scanning LiDAR system (a flash LiDAR system is an example non-scanning LiDAR system).

Steering mechanism340can be used with a transceiver (e.g., transmitter320and optical receiver and light detector330) to scan the FOV for generating an image or a 3D point cloud. As an example, to implement steering mechanism340, a two-dimensional mechanical scanner can be used with a single-point or several single-point transceivers. A single-point transceiver transmits a single light beam or a small number of light beams (e.g., 2-8 beams) to the steering mechanism. A two-dimensional mechanical steering mechanism comprises, for example, polygon mirror(s), oscillating mirror(s), rotating prism(s), rotating tilt mirror surface(s), single-plane or multi-plane mirror(s), or a combination thereof. In some embodiments, steering mechanism340may include non-mechanical steering mechanism(s) such as solid-state steering mechanism(s). For example, steering mechanism340can be based on tuning wavelength of the laser light combined with refraction effect, and/or based on reconfigurable grating/phase array. In some embodiments, steering mechanism340can use a single scanning device to achieve two-dimensional scanning or multiple scanning devices combined to realize two-dimensional scanning.

As another example, to implement steering mechanism340, a one-dimensional mechanical scanner can be used with an array or a large number of single-point transceivers. Specifically, the transceiver array can be mounted on a rotating platform to achieve 360-degree horizontal field of view. Alternatively, a static transceiver array can be combined with the one-dimensional mechanical scanner. A one-dimensional mechanical scanner comprises polygon mirror(s), oscillating mirror(s), rotating prism(s), rotating tilt mirror surface(s), or a combination thereof, for obtaining a forward-looking horizontal field of view. Steering mechanisms using mechanical scanners can provide robustness and reliability in high volume production for automotive applications.

As another example, to implement steering mechanism340, a two-dimensional transceiver can be used to generate a scan image or a 3D point cloud directly. In some embodiments, a stitching or micro shift method can be used to improve the resolution of the scan image or the field of view being scanned. For example, using a two-dimensional transceiver, signals generated at one direction (e.g., the horizontal direction) and signals generated at the other direction (e.g., the vertical direction) may be integrated, interleaved, and/or matched to generate a higher or full resolution image or 3D point cloud representing the scanned FOV.

Some implementations of steering mechanism340comprise one or more optical redirection elements (e.g., mirrors or lenses) that steer return light signals (e.g., by rotating, vibrating, or directing) along a receive path to direct the return light signals to optical receiver and light detector330. The optical redirection elements that direct light signals along the transmitting and receiving paths may be the same components (e.g., shared), separate components (e.g., dedicated), and/or a combination of shared and separate components. This means that in some cases the transmitting and receiving paths are different although they may partially overlap (or in some cases, substantially overlap or completely overlap).

With reference still toFIG.3, LiDAR system300further comprises control circuitry350. Control circuitry350can be configured and/or programmed to control various parts of the LiDAR system300and/or to perform signal processing. In a typical system, control circuitry350can be configured and/or programmed to perform one or more control operations including, for example, controlling light source310to obtain the desired laser pulse timing, the pulse repetition rate, and power; controlling steering mechanism340(e.g., controlling the speed, direction, and/or other parameters) to scan the FOV and maintain pixel registration and/or alignment; controlling optical receiver and light detector330(e.g., controlling the sensitivity, noise reduction, filtering, and/or other parameters) such that it is an optimal state; and monitoring overall system health/status for functional safety (e.g., monitoring the laser output power and/or the steering mechanism operating status for safety).

Control circuitry350can also be configured and/or programmed to perform signal processing to the raw data generated by optical receiver and light detector330to derive distance and reflectance information, and perform data packaging and communication to vehicle perception and planning system220(shown inFIG.2). For example, control circuitry350determines the time it takes from transmitting a light pulse until a corresponding return light pulse is received; determines when a return light pulse is not received for a transmitted light pulse; determines the direction (e.g., horizontal and/or vertical information) for a transmitted/return light pulse; determines the estimated range in a particular direction; derives the reflectivity of an object in the FOV, and/or determines any other type of data relevant to LiDAR system300. Control circuitry350may include digital and/or analog circuitry (e.g., ADC, amplifier, filter, etc.) for processing data representing return light signals received by a LiDAR system or a HyDAR system.

LiDAR system300can be disposed in a vehicle, which may operate in many different environments including hot or cold weather, rough road conditions that may cause intense vibration, high or low humidities, dusty areas, etc. Therefore, in some embodiments, optical and/or electronic components of LiDAR system300(e.g., optics in transmitter320, optical receiver and light detector330, and steering mechanism340) are disposed and/or configured in such a manner to maintain long term mechanical and optical stability. For example, components in LiDAR system300may be secured and sealed such that they can operate under all conditions a vehicle may encounter. As an example, an anti-moisture coating and/or hermetic sealing may be applied to optical components of transmitter320, optical receiver and light detector330, and steering mechanism340(and other components that are susceptible to moisture). As another example, housing(s), enclosure(s), fairing(s), and/or window can be used in LiDAR system300for providing desired characteristics such as hardness, ingress protection (IP) rating, self-cleaning capability, resistance to chemical and resistance to impact, or the like. In addition, efficient and economical methodologies for assembling LiDAR system300may be used to meet the LiDAR operating requirements while keeping the cost low.

It is understood by a person of ordinary skill in the art thatFIG.3and the above descriptions are for illustrative purposes only, and a LiDAR system can include other functional units, blocks, or segments, and can include variations or combinations of these above functional units, blocks, or segments. For example, LiDAR system300can also include other components not depicted inFIG.3, such as power buses, power supplies, LED indicators, switches, etc. Additionally, other connections among components may be present, such as a direct connection between light source310and optical receiver and light detector330so that light detector330can accurately measure the time from when light source310transmits a light pulse until light detector330detects a return light pulse.

These components shown inFIG.3are coupled together using communications paths312,314,322,332,342,352,362, and372. These communications paths represent communication (bidirectional or unidirectional) among the various LiDAR system components but need not be physical components themselves. While the communications paths can be implemented by one or more electrical wires, buses, or optical fibers, the communication paths can also be wireless channels or open-air optical paths so that no physical communication medium is present. For example, in one example LiDAR system, communication path314includes one or more optical fibers; communication path352represents an optical path; and communication paths312,322,342, and362are all electrical wires that carry electrical signals. The communication paths can also include more than one of the above types of communication mediums (e.g., they can include an optical fiber and an optical path, or one or more optical fibers and one or more electrical wires).

FIG.4is a block diagram illustrating an exemplary multimodal detection system400with integrated sensors, according to various embodiments. Multimodal detection system400can be a part of a LiDAR system (e.g., system300) or includes a part of a LiDAR system (e.g., system300). System400can also include one or more other sensors such as cameras. In one example where system400includes a LiDAR sensor and an image sensor (or includes a LiDAR sensor and one or more other types of sensors), it can be referred to as a Hybrid Detection and Ranging (HyDAR) system. As shown inFIG.4, in some embodiments, on the transmission side, system400can include a light source402, a transmitter404, and a steering mechanism406. These components can form a transmission light path. On the receiver side, system400can include an optical receiver and light detector430, which comprises one or more of a light collection and distribution device410, a signal separation device440, and a multimodal sensor450. In some examples, steering mechanism406is also used for receiving light signals from the FOV470. Therefore, steering mechanism406and optical receiver and light detector430can form a receiving light path. Light source402, transmitter404, and steering mechanism406can be substantially the same or similar as light source310, transmitter320, and steering mechanism340, respectively, as described above in connection withFIG.3.

In some examples, light source402is an internal light source that generates light for the multimodal detection system400. Examples of internal light sources include active illumination devices such as laser (e.g., fiber laser or semiconductor based laser used in one or more LiDAR transmission channels of system400), light emitting diodes, headlights/taillights, etc. An example of light source402is described below in more detail in connection withFIGS.5A and5B. In some examples, system400also receives light from light sources that are external to system400. These external light sources may not be a part of the system400. Examples of external light sources include sunlight, streetlight, and other illuminations from light sources external to system400(e.g., light from other LiDARs).

As illustrated inFIG.4, light generated by light source402(e.g., laser from a LiDAR system) is provided to transmitter404. The light generated by light source402can include visible light, near infrared (NIR) light, short wavelength IR (SWIR) light, medium wavelength IR (MWIR) light, long wavelength IR (LWIR) light, and/or light in any other wavelengths. The visible light has a wavelength range of about 400 nm-700 nm; the near infrared (NIR) light has a wavelength range of about 700 nm-1.4 μm; the short-wavelength infrared (SWIR) has a wavelength range of about 1.4 μm-3 μm; the mid-wavelength infrared (MWIR) has a wavelength range of about 3 μm-8 μm; and a long-wavelength infrared (LWIR) has a wavelength range of about 8 μm-15 μm.

FIG.5Ais a block diagram illustrating an example fiber-based laser source500for implementing light source310depicted inFIG.3and/or light source402depicted inFIG.4. Fiber-based laser source500has a seed laser and one or more pumps (e.g., laser diodes) for pumping desired output power. In some embodiments, fiber-based laser source500comprises a seed laser502configured to generate initial light pulses of one or more wavelengths (e.g., infrared wavelengths such as 1550 nm), which are provided to a wavelength-division multiplexor (WDM)504via an optical fiber503. Fiber-based laser source500further comprises a pump506for providing laser power (e.g., of a different wavelength, such as 980 nm) to WDM504via an optical fiber505. WDM504multiplexes the light pulses provided by seed laser502and the laser power provided by pump506onto a single optical fiber507. The output of WDM504can then be provided to one or more pre-amplifier(s)508via optical fiber507. Pre-amplifier(s)508can be optical amplifier(s) that amplify optical signals (e.g., with about 10-30 dB gain). In some embodiments, pre-amplifier(s)508are low noise amplifiers. Pre-amplifier(s)508output to an optical combiner510via an optical fiber509. Combiner510combines the output laser light of pre-amplifier(s)508with the laser power provided by pump512via an optical fiber511. Combiner510can combine optical signals having the same wavelength or different wavelengths. One example of a combiner is a WDM. Combiner510provides combined optical signals to a booster amplifier514, which produces output light pulses via optical fiber515. The booster amplifier514provides further amplification of the optical signals (e.g., another 20-40 dB). The output light pulses can then be transmitted to transmitter320, transmitter404, steering mechanism340, and/or steering mechanism406(shown inFIGS.3and4). It is understood thatFIG.5Aillustrates one example configuration of fiber-based laser source500. Laser source500can have many other configurations using different combinations of one or more components shown inFIG.5Aand/or other components not shown inFIG.5A(e.g., other components such as power supplies, lens(es), filters, splitters, combiners, etc.).

In some variations, fiber-based laser source500can be controlled (e.g., by control circuitry350) to produce pulses of different amplitudes based on the fiber gain profile of the fiber used in fiber-based laser source500. Communication path312couples fiber-based laser source500to control circuitry350(shown inFIG.3) so that components of fiber-based laser source500can be controlled by or otherwise communicate with control circuitry350. Alternatively, fiber-based laser source500may include its own dedicated controller. Instead of control circuitry350communicating directly with components of fiber-based laser source500, a dedicated controller of fiber-based laser source500communicates with control circuitry350and controls and/or communicates with the components of fiber-based laser source500. Fiber-based laser source500can also include other components not shown, such as one or more power connectors, power supplies, and/or power lines.

FIG.5Bis a block diagram illustrating an example semiconductor-based laser source540. Semiconductor-based laser source540is an example of light source310depicted inFIG.3and/or light source402depicted inFIG.4. In the example shown inFIG.5B, laser source540is a Vertical-Cavity Surface-Emitting Laser (VCSEL), which is a type of semiconductor laser diode with a distinctive structure that allows it to emit light vertically from the surface of the chip, rather than through the edge of the chip like the edge-emitting laser (EEL) diodes. VCSELs have advantages like high-speed operation and easy integration into semiconductor devices.FIG.5Bshows a cross-sectional view of an example VCSEL540. In this example, the VCSEL540includes a metal contact layer542, an upper Bragg reflector544, an active region546, a lower Bragg reflector548, a substrate550, and another metal contact552. In the VCSEL540, the metal contacts542and552are for making electrical contacts so that electrical current and/or voltage can be provided to VCSEL540for generating laser light. The substrate layer550is a semiconductor substrate, which can be, for example, a gallium arsenide (GaAs) substrate. VCSEL540uses a laser resonator, which includes two distributed Bragg reflector (DBR) reflectors (i.e., upper Bragg reflector544and lower Bragg reflector548) with an active region546sandwiched between the DBR reflectors. The active region546includes, for example, one or more quantum wells for the laser light generation. The planar DBR-reflectors can be mirrors having layers with alternating high and low refractive indices. Each layer has a thickness of a quarter of the laser wavelength in the material, yielding intensity reflectivities above e.g., 99%. High reflectivity mirrors in VCSELs can balance the short axial length of the gain region. In one example of VCSEL540, the upper and lower DBR reflectors544and548can be doped as p-type and n-type materials, forming a diode junction. In another example, the p-type and n-type regions may be embedded between the reflectors, requiring a more complex semiconductor process to make electrical contact to the active region, but eliminating electrical power loss in the DBR structure. The active region546is sandwiched between the DBR reflectors544and548of the VCSEL540. The active region is where the laser light generation occurs. The active region546typically has a quantum well or quantum dot structure, which contains the gain medium responsible for light amplification. When an electric current is applied to the active region546, it generates photons by stimulated emission. The distance between the upper and lower DBR reflectors544and548defines the cavity length of the VCSEL540. The cavity length in turn determines the wavelength of the emitted light and influences the laser's performance characteristics. When an electrical current is applied to the VCSEL540, it generates light that bounces between the DBR reflectors544and548and exits the VCSEL540through, for example, the lower DBR reflector548, producing a highly coherent and vertically emitted laser beam554. VCSEL540can provide an improved beam quality, low threshold current, and the ability to produce single-mode or multi-mode output.

In some variations, VCSEL540can be controlled (e.g., by control circuitry350) to produce pulses of different amplitudes. Communication path312couples VCSEL540to control circuitry350(shown inFIG.3) so that components of VCSEL540can be controlled by or otherwise communicate with control circuitry350. Alternatively, VCSEL540may include its own dedicated controller. Instead of control circuitry350communicating directly with components of VCSEL540, a dedicated controller of VCSEL540communicates with control circuitry350and controls and/or communicates with the components of VCSEL540. VCSEL540can also include other components not shown, such as one or more power connectors, power supplies, and/or power lines.

VCSEL540can be used to generate laser pulses or continuous wave (CW) lasers. To generate laser pulses, control circuitry350modulates the current supplied to the VCSEL540. By rapidly turning the supply current on and off, pulses of laser light can be generated. The duration, repetition rate, and shape of the pulses can be controlled by adjusting the modulation parameters. As another example, VCSEL540can also be a mode-locked VCSEL that uses a combination of current modulation and optical feedback to obtain ultra-short pulses. The mode-locked VCSEL may also be controlled to synchronize the phases of the laser modes to produce very short and high-intensity pulses. As another example, VCSEL540can use Q-Switching techniques, which includes an optical switch in the laser cavity, temporarily blocking the lasing action and allows energy to build up in the cavity. When the switch is opened, a high-intensity pulse is emitted. As another example, VCSEL540can also have external modulation performed by an external modulator (not shown), such as an electro-optic or acousto-optic modulator. The external modulation can be used in combination with the VCSEL itself to create pulsed output. The external modulator can be used to control the pulse duration and repetition rate. The type of VCSEL used as at least a part of light source310or light source402depends on the application and the required pulse characteristics, such as pulse duration, repetition rate, and peak power.

With reference back toFIG.4, multimodal detection system400includes a transmitter404. In some examples, transmitter404can include one or more transmission channels, each carrying a light beam. The transmitter404may also include one or more optics (e.g., mirrors, lens, fiber arrays, etc.) and/or electrical components (e.g., PCB board, power supply, actuators, etc.) to form the transmission channels. Transmitter404can transmit the light from each channel to a steering mechanism406, which scans the light from each channel to an FOV470. Steering mechanism406may include one or more optical or electrical scanners configured to perform at least one of a point scan or a line scan of the FOV470.

Light source402, transmitter404, and steering mechanism406may be a part of a LiDAR or HyDAR system that scans light into FOV470. The scanning performed by steering mechanism406can include, for example, line scanning and/or point scanning. For example, the steering mechanism406can be configured to scan all points in lines or an area; scan some points in certain lines or an area, while skip scanning other points; or scan certain lines while skipping other lines. As another example, the steering mechanism406of multimodal detection system400can be configured to scan certain points/lines in higher resolution while scan other points/lines in lower resolution. For instance, the high resolution scanning may be applied to regions of interest (ROIs) while the low resolution scanning or no scanning may be applied to other regions of the FOV. In some embodiments, to scan an ROI, the steering mechanism406containing one or more optical or electrical scanners can be controlled to have different characteristics than those for scanning a non-ROI. For instance, for scanning the ROI, a scanner may be controlled to have slower scanning rate and/or a smaller scanning step, thereby increasing the scanning resolution. Furthermore, the light source402may also be configured to increase the pulse repetition rate, thereby increasing the scanning resolution.

With reference toFIG.4, in some embodiments, if a sensor in a multimodal detection system400does not require actively transmitting light and/or scanning the light, one or more of light source402, transmitter404, and steering mechanism406may not be required for that particular sensor. For example, if system400includes a passive image sensor or video sensor (e.g., a camera), it may not require actively sending out light and/or scanning light to the FOV in order to form an image of the FOV. In this disclosure, the terms “image sensor” and “video sensor” are used interchangeably, both referring to a passive sensor that can capture images and/or videos. As a passive sensor, the image sensor may just sense light from the FOV and use the sensed light to form an image. It may not transmit light out to the FOV itself. In some other examples, the image sensor may require a light source (e.g., a flash light or other illuminations) to provide sufficient light conditions for sensing (e.g., capturing an image with enough brightness). In some examples, an image sensor can also perform a point scan or a line scan to obtain better performance such as an improved detection limit and a larger dynamic range. Such an image sensor may have a high image resolution and complex imaging structures and may thus be expensive. However, as described below, integrating such an image sensor with, for example, a LiDAR sensor in the multimodal detection system400can reduce the overall cost as compared to two discrete sensors.

FIG.4further illustrates that system400includes an optical receiver and light detector430to receive and detect light from FOV470. As described above, the transmission side of system400may transmit light to FOV470. A portion of the light transmitted may be reflected or scattered by objects in the FOV470to form return light signals. The return light signals may be received by optical receiver and light detector430. In addition, optical receiver and light detector430may also receive light signals from other external light sources, including, for example, sunlight, ambient light, streetlight, and/or other sources of illuminations such as light from other LiDAR or HyDAR systems. The various light signals received by optical receiver and light detector430are collectively referred to as the received light signals or collected light signals. The received or collected light signals may include both return light signals formed based on transmitted light of system400and other light signals from other light sources. The received light signals may have a narrow or wide spectral range comprising, for example, one or more of visible light, NIR light, SWIR light, MWIR light, and/or LWIR light, etc. One or more of these received light signals can be detected by different types of light detectors (e.g., a LiDAR sensor for detecting IR light signals, and an image sensor for detecting visible light signals). In the present disclosure, one or more of these light detectors can be integrated to form a hybrid detector. The light collection distribution device410, signal separation device440, and multimodal sensor450of optical receivers and light detector430are described in greater detail below.

FIG.6illustrates an example light collection and distribution device410. Light collection and distribution device410can be configured to perform at least one of collecting light signals from a field-of-view (FOV) and distributing the light signals to a plurality of sensors of a multimodal sensor (e.g., sensor450). The light signals collected and distributed by device410may have a plurality of wavelengths. At least one wavelength is different from one or more other wavelengths. As illustrated inFIG.6, device410can include light collection optics602, refraction optics610, diffractive optics620, reflection optics630, and/or optical fibers640. WhileFIG.4illustrates that steering mechanism406is a separate device from light collection and distribution device410, in some embodiments, steering mechanism406may be integrated with, or a part of, device410. For example, steering mechanism406may be shared between the transmitter404and the optical receiver and light detector430for both transmitting light signals to the FOV and for receiving/redirecting light signals from the FOV. This type of configuration is also referred to as a coaxial configuration because the transmitting light path and the receiving light path share some common optical components. Thus, whileFIG.6does not explicitly illustrate, light collection optics602may include a steering mechanism that is shared between the transmitter and receiver.

With reference toFIG.6, light signals from the FOV can be received or collected by light collection optics602(e.g., by a steering mechanism406). Light collection optics602include optics that are configured to collect and focus received light signals. Light collection optics602can be optimized to maximize the number of light signals collected from the FOV and direct the light signals toward a specific target, such as one of the refraction optics, diffractive optics, reflection optics, a detector, a sensor, and/or an imaging system. Light collection optics602may include one or more types of light collection optics, including one or more lenses, one or more lens groups, one or more mirrors, and one or more optical fibers. For instance, a collection lens or a lens group can be used to collect light signals from a distant object in the FOV and focus the light signals onto another optical components or a detector. Mirrors are another optical component that can be used in light collection optics602. They can be used to reflect and redirect light toward a specific target. Mirrors can be used alone or in combination with lenses to form complex optical structures for collecting light signals.

In some embodiments as shown inFIG.6, light collection optics602directs the collected light signals to one or more of refraction optics610, diffractive optics620, reflection optics630, and/or optical fibers640. In some embodiments, light collection optics602may be optional or integrated with refraction optics610, diffractive optics620, reflection optics630, and/or optical fibers640. For instance, the collected light signals can be directed (with or without light collection optics602) to refraction optics610. Refraction optics610can include optics that bend the light signals as they pass from one medium (e.g., air) to another medium (e.g., glass) with a different refractive index. A refractive index is a measure of how much a medium can bend light signals. When light signals pass from a medium with a high refractive index to a medium with a lower refractive index, the light signals bend away from the normal direction (e.g., the direction perpendicular to the surface at the point where the light enters the second medium). When the light signals pass from a medium with a low refractive index to a medium with a higher refractive index, the light bends toward the normal direction. The amount of bending depends on the angle of incidence (the angle between the incoming light signals and the normal direction of a surface of the medium) and the refractive indices of the two media. The relationship between these variables is described by the Snell's law, which states that the ratio of the sine of the angle of incidence to the sine of the angle of refraction is equal to the ratio of the refractive indices of the two media.

In some embodiments, refraction optics610can be implemented using a beam splitter, which can be configured to perform optical refraction such that it transmits a first portion of the incident light signals from the FOV (e.g., received directly or via light collection optics) to a first sensor and reflects a second portion of the received light signals to a second sensor. The first sensor and second sensor can be different sensors located at two different positions.

With continued reference toFIG.6, light collection and distribution device410may also include diffractive optics620configured to separate the incident light signals to portions having different wavelengths, intensities, or polarizations. Diffractive optics620may include optics having diffractive structures such as a diffractive gratings. Diffractive structures can be made of thin layers of materials that contain features, such as grooves, ridges, or other microstructures, that are configured to manipulate the phase of the incident light signals. These diffractive structures can be used to manipulate the properties of light signals, such as the direction, intensity, polarization, and wavelength. In some examples, diffractive optics620can include diffractive gratings, which is a periodic structure that separates light into its spectral components based on its wavelength. In some examples, diffractive optics620may also include diffractive lenses, beam splitters, and polarizers. Diffractive lenses can be configured to correct for chromatic aberration and other types of optical distortion, and can be used to provide lightweight and compact optical systems. Diffractive optics620can be used to create complex optical elements with a high degree of precision. As a result, they can be used in the multimodal detection system to precisely separate and direct light signals having different properties (e.g., wavelengths, intensities, polarizations, etc.) to different sensors.

FIG.6also illustrates that light collection and distribution device410may include reflective optics630. Reflection optics630comprises one or more optical components that can reflect the incident light signals. The angle of incidence determines the angle of reflection. The properties of the surface of reflection optics, such as the roughness, shape, and material, can affect the reflection of the incident light signals. In one example, reflection optics630comprises a Schmidt-Cassegrain based reflection device configured to direct a portion of the incident light signals to a first sensor and direct another portion of the incident light signals to a second sensor. In some examples, reflection optics630includes a Newtonian-based reflection device configured to direct a portion of the incident light signals to a first sensor and direct another portion of the incident light signals to a second sensor. The first sensor and second sensor can be different sensors located at different physical positions. They can also be different types of sensors (e.g., a LiDAR sensor and an image sensor).

In another embodiments, the incident light signals collected by light collection optics602can be directed to different sensors by using optical fibers640. Optical fibers640can be flexible and have any desired lengths. Therefore, using optical fibers640, the incident light signals can be directed to different sensors located at different physical positions.

As described above and shown inFIG.4, multimodal detection system400may include a signal separation device440.FIG.7illustrates an example of such a signal separation device440. Signal separation device440is configured to separate the incident light signals to form separated light signals having a plurality of different light characteristics. The signal separation device440can perform a variety of separations including spatial separation, intensity separation, spectrum separation, polarization separation, etc. WhileFIG.4illustrates that signal separation device440and light collection and distribution device410are two different devices, in some embodiments, signal separation device440may be at least partially combined with light collection and distribution device410. For instance, as described above, light collection and distribution device410can include one or more of refraction optics, diffractive optics, reflection optics, etc., to perform spatial distribution of the incident light signals. Thus, these optical components may form a part of the signal separation device440(e.g., as spatial separation device) to separate incident light signals to different portions and direct the different portions to different detectors at different physical locations.

With reference toFIG.7, signal separation device440may include a spatial separation device706, a spectrum separation device704, a polarization separation device708, and/or other separation devices (not shown). Spatial separation device706is configured to separate light signals to form separated light signals corresponding to at least one of different spatial positions of the plurality of sensors or different angular directions of the light signals. Thus, the light signals from spatial separation device706can have different physical locations and/or different angular directions. The spectrum separation device704is configured to separate the light signals to form separated light signals having different wavelengths (e.g., NIR light, visible light, SWIR light, etc.). The polarization separation device708is configured to separate the light signals to form the separated light signals having different polarizations (e.g., horizontal or vertical).

The devices included in signal separation device440can be configured and structured in any desired manner. In one embodiment, spatial separation device706may be disposed upstream to receive the incident light signals702and to direct the spatially separated light signals to spectrum separation device704and/or polarization separation device708. In another embodiment, spectrum separation device704may be disposed upstream to receive the incident light signals702and to direct the spectrally separated light signals to spatial separation device706and/or polarization separation device708. Similarly, polarization separation device706can be disposed upstream. In other words, signal separation device440can be configured such that the spectrum separation, spatial separation, polarization separation, and/or any other separations can be performed in any desired order. In other embodiments, two or more types of separations can be performed together. For example, as described above, a prism or a beam splitter may separate light signals both spectrally and spatially. Each of the devices704,706, and708is described in greater detail below.

One example of a spatial separation device706is a fiber bundle. The incident light signals702are coupled to the optical fiber bundle, which may include many optical fibers bundled together such that they are physically located close to each other at one end of the fiber bundle. Different optical fibers of the fiber bundle can then be routed to different sensors located at different physical locations. Another example of a spatial separation device706shown inFIG.7comprises a micro lens array configured to separate the incident light signals to form the separated light signals and direct the separated light signals to respective sensors. The micro lens array is an optical component comprising an array of small lenses. These small lenses typically have diameters ranging from tens to hundreds of micrometers. Each lens in the micro lens array focuses light signals onto a specific point or a sensor, and the overall effect of the array is to shape or manipulate the light signals in a particular way. A micro lens array can be used to enhance the resolution and sensitivity of imaging systems by focusing light signals onto a detector array or improving light collection efficiency. A micro lens array can also be used to shape light into specific patterns or distributions for use in applications such as image sensing or depth sensing. A micro lens array can also be used to couple light between optical fibers or to improve the coupling efficiency between optical components. A micro lens array can be made from materials such as glass, silicon, or plastic, and can be customized in terms of lens size, shape, and spacing to achieve the desired optical performance.

With continued reference toFIG.7, signal separation device440may also include a spectrum separation device704, which is configured to separate light signals to form the separated light signals having different wavelengths or colors. Spectrum separation device704comprises one or more of a Dichroic mirror, a dual-band mirror, a dual-wavelength mirror, a Dichroic reflector, a red-green-blue (RGB) filter, an infrared light filter, a colored glass filter, an interference filter, a diffractive optics, a prism, diffraction gratings, blazed gratings, holographic gratings, and a Cezrny-Turner monochromator. For example, a prism can refract light signals at different angles depending on the wavelength of the light signals. Using the visible light as an example, when a beam of incident light signals is passed through a prism, the light signals may be separated to different colors for different channels including a red channel, a green channel, and a blue channel. As another example, diffraction gratings can also be used for spectrum separation. They include a series of closely spaced parallel lines or slits that diffract light at different angles depending on the wavelength of the light. Using diffraction gratings, incident light signals can similarly be separated into a red channel, a green channel, and a blue channel. The separated light signals have different wavelengths, which may carry different information that can be more easily processed by a computer vision algorithm.

FIG.7also illustrates that signal separation device440can include a polarization separation device708, which is configured to separate light signals to form the separated light signals having different polarizations. In one embodiment, the polarization separation device708comprises one or more of absorptive polarizers including crystal-based polarizers, beam-splitting polarizers, Fresnel reflection based polarizers, Birefringent polarizers, thin film based polarizers, wire-grid polarizers, and circular polarizers. For instance, polarization separation can be achieved using polarizing filters, which are optical filters that only transmit light waves with a specific polarization orientation. Polarizing filters can be made from materials such as polarizing films, wire grids, or birefringent crystals. When unpolarized light is passed through a polarizing filter, only the component of the light with the same polarization orientation as the filter is transmitted, while the other polarization component is blocked. This results in polarized light with a specific polarization orientation. For instance, when light signals pass through the polarization separation device708, the light signals can be separated to light signals having a horizontal polarization, light signals having a vertical polarization, and light signals having all polarizations. Image data formed by light signals having different polarizations can include different information such as different contrast, brightness, color, etc.

Using one or more of the above types of separation devices and other types of separation/processing devices (e.g., an image sensor such as a CCD array), signal separation device440can process the incident light signals to differentiate light intensities and/or reflectivity. Light signals reflected or received at different angles by an optical receiver may have different light intensities. The different light intensities may be sensed and represented by signal separation device440by, for example, different brightness/colors of the image captured.

With reference back toFIG.4, as described above, in some embodiments, light collection and distribution device410and signal separation device440may be two separate devices. For example, device410is configured to collect light signals from the FOV470and spatially distribute the received light signals, while device440is configured to spectrally separate the received light signals. In some embodiments, light collection and distribution device410and signal separation device440may be combined together, at least partially, to perform one of more of spatial separation, spectrum separation, polarization separation, etc. In another embodiment, light collection and distribution device410can directly distribute the light signals to multimodal sensor450without using a signal separation device440.

With continued reference toFIG.4, when the received light signals are processed by light collection and distribution device410and optionally signal separation device440, they are passed onto multimodal sensor450. In some embodiments, multimodal sensor450includes a plurality of sensors that are positioned corresponding to the respective light emitters to improve the light collection effectiveness. For example, each sensor of the plurality of sensors may be angularly positioned differently corresponding to the different angular positions of a plurality of transmitter channels directing a plurality of transmission light beams to different directions. As such, the receiving aperture for receiving return light signals formed by different transmission light beams can be maximized. Each sensor of multimodal sensor450may include one or more detectors or detector elements. The plurality of sensors may have different types. For instance, the plurality of sensors may comprise at least a light sensor of a first type and a light sensor of a second type. The light sensor of the first type can be configured to detect light signals having a first light characteristic, where the light sensor of the second type is configured to detect light signals having a second light characteristic. The first light characteristic can be different from the second light characteristic. For instance, the light sensor of the first type can include a sensor configured to detect light signals having an NIR wavelength for a LiDAR system. The light sensor of the second type can include a sensor configured to detect light signals having the visible light wavelength for a camera. As described above, the NIR wavelength signals can be used by the LiDAR sensor to generate point cloud data for distance measurements; while the visible light can be used by an image sensor to generate image data for visual computing.

In some embodiments, the plurality of sensors of multimodal sensor450can be combined or integrated together.FIG.8illustrates example configurations of integrated detectors of a multimodal sensor450, according to various embodiments of the present disclosure. As shown inFIG.8, two or more sensors of a multimodal sensor can be integrated in a single device package, detector assembly, a semiconductor chip, or a single printed circuit board (PCB). For instance, a semiconductor chip800may include many dies sharing a semiconductor substrate. The dies can be located in the same wafer. At least a part of semiconductor chip800may be used as sensors for a multimodal sensor450. In the embodiment shown inFIG.8, chip800may include four sensors802,804,806, and808. Sensors802and804may be disposed in a respective die of chip800(one die in chip800is illustrated as a small square). Sensors806and808may be disposed in multiple dies. For example, sensor806may include 4 detectors that are disposed across4dies horizontally, while sensor808may include 4 detectors that are disposed across4dies both horizontally and vertically forming a 2×2 array. It is understood that a sensor can be disposed in any desired manner across any number dies. The chip800may also include other sensors or circuits. For instance, readout circuits for processing the sensor generated signals can be integrated in chip800, thereby improving the degree of integration of multimodal sensor450and reducing cost.

Sensors that can be integrated in chip800may include photodiode-based detectors, avalanche photodiodes (APDs) based detectors, charge-coupled devices (CCDs) based detectors, etc. For example, photodiodes based detectors may be made from Silicon or Germanium materials; APD-based detectors may be made from Silicon, Germanium, Indium Gallium Arsenide (InGaAs), Mercury Cadmium Telluride (MCT); and CCD-based detectors can be made from Silicon, Gallium Arsenide (GaAs), Indium Phosphide (InP), and MCT. In some examples, APDs can be used for sensing infrared light for a LiDAR device, and CCD can be used for sensing visible light for a camera. Therefore, multiple sensors can be integrated together on chip800by using semiconductor chip fabrication techniques. It is understood that a sensor included in multimodal sensor450can also use other suitable semiconductor materials such as Silicon Germanium (SiGe).

With continued reference toFIG.8, in some embodiments, chip800may also integrate a photonic crystal structure, which is a type of artificial periodic structure that can manipulate the flow of light in a similar way to how crystals manipulate the flow of electrons in solid-state materials. Photonic crystals are made by creating a pattern of periodic variations in the refractive index of a material. This pattern creates a photonic band gap, which is a range of frequencies of light that cannot propagate through the material. The photonic band gap arises from the interference of waves reflected by the periodic structure, leading to destructive interference at certain frequencies and constructive interference at others. The result is a range of frequencies where light cannot propagate, similar to how electronic band gaps prevent the flow of electrons in semiconductors. Photonic crystals can be made from a variety of materials, including semiconductors, metals, and polymers. A photonic crystal structure can be used to implement optical filters, detectors, waveguides, and laser emitters. For instance, the photonic band gap can be used to create optical filters; and the sensitivity of photonic crystals to changes in refractive index can be used to create highly sensitive sensors. Therefore, by using photonic crystal structure, chip800can integrate not only sensors or detectors, but also other optical components such as filters, waveguides, and light sources, thereby further improving the degree of integration. Various dies or modules disposed in chip800can thus implement different functions. Chip800can be bonded to other components (e.g., a readout circuitry, a PCB) using wire bonding, flip-chip bonding, BGA bonding, or any other suitable packaging techniques.

As described above, a multimodal sensor450(shown inFIGS.4and8) may include multiple sensors. A sensor includes one or more detectors, one or more other optical elements (e.g., lens, filter, etc.) and/or electrical elements (e.g., ADC, DAC, processors, etc.). In the example shown inFIG.8, multiple sensors can be integrated or disposed together to form a multimodal sensor450. Multimodal sensor450may be included in a detector assembly, a device package, a device module, or a PCB. The multiple sensors are mounted to the same assembly, device package, device module, or PCB. In other embodiments, multimodal sensor450may include two or more assemblies, device packages, modules, or PCBs. Each of the multiple sensors may be mounted to a different assembly, device package, device module, or PCB. The different assemblies, device packages, modules, or PCBs may be disposed close to each other or in a housing to form an integrated multimodal sensor package.

In the example shown inFIG.8, the multiple sensors included in multimodal sensor450comprise an imaging sensor812, an illuminance sensor814, a LiDAR sensor816, and one or more other sensors818. An imaging sensor812can include a detector that detects light signals and converts the light signals to electrical signals to form images. Therefore, imaging sensor812can be used as a part of cameras. The imaging sensor812can be a CCD sensor, a CMOS sensor, an active-pixel sensor, a thermal-imaging sensor, etc. An illuminance sensor814can include a detector that facilitates measuring the amount of light falling on a surface per unit area, referred to as illuminance. Illuminance can be represented for example, by the amount of lumen per square meter. Illuminance sensor814can include detectors comprising photodiodes, phototransistors, photovoltaic cells, photoresistors, etc. Illuminance sensor814can be used for lighting control, brightness control, environmental monitoring, etc.

LiDAR sensor816can include detectors that detect laser light (e.g., in the infrared wavelength range). The detected laser light can be used to determine the distance of an object from the LiDAR sensor. LiDAR sensor816can be used to generate a 3D point cloud of the surrounding area. The detectors used for a LiDAR sensor can be an avalanche photodiode, Mercury-Cadmium-Telluride (HgCdTe) based infrared detectors, Indium Antimonide (InSb) based detectors, etc. LiDAR sensor816can be implemented using one or more components of LiDAR system300described above.FIG.8also illustrates that multimodal sensor450may include one or more other sensors818. These other sensors818can facilitate temperature sensing, chemical sensing, pressure sensing, motion sensing, light sensing, proximity sensing, etc. One or more sensors818can include detectors such as light emitting diodes (LEDs), photoresistors, photodiodes, phototransistors, pinned photodiodes, quantum dot photoconductors/photodiodes, silicon drift detectors, photovoltaic based detectors, avalanche photodiode (APD), thermal based detectors, Bolometers, microbolometers, cryogenic detectors, pyroelectric detectors, thermopiles, Golay cells, photoreceptor cells, chemical-based detectors, polarization-sensitive photodetectors, and graphene/silicon photodetectors, etc.

With reference toFIGS.4and8, The plurality of sensors of multimodal sensor450can include multiple types of sensors integrated or mounted together to share, for example, a semiconductor wafer, a module, a printed circuit board, and/or a semiconductor package. The sensors may also share one or more components the transmission light path (e.g., light source402, transmitter404, and/or steering mechanism406) and/or in the receiving light path (e.g., light collection and distribution device410, signal separation device440). As a result, the multimodal sensor450can have a compact dimension, thereby enabling the multimodal detection system to be also compact. A compact multimodal detection system can be disposed in, or mounted to, any location within a moveable platform such as a motor vehicle. For instance, comparing to mounting multiple discreate sensors like one or more cameras, one or more LiDARs, one or more thermal imaging devices, one or more ultrasonic devices, etc., mounting a compact multimodal detection system can significantly reduce the complexity of integration of the multiple sensing capabilities into a vehicle, and/or also reduce the cost. As illustrated inFIGS.4and8, a multimodal detection system (e.g., system400) that includes a multimodal sensor (e.g., sensor450) is also sometimes referred to as a hybrid detection and ranging system (HyDAR).

With continued reference toFIGS.4and8, in some embodiments, the plurality of detectors or sensors of a multimodal sensor450can be configured to detect light signals received from the same FOV. For instance,FIG.4illustrates that light signals received from the same FOV470may include two or more of: NIR light, visible light, SWIR light, MWIR light, LWIR light, and other light. These light signals are mixed together but can be detected by the same multimodal sensor450. For instance, as described above, the mixed light signals can be collected and distributed by device410, and then separated according to one or more of the light characteristics (e.g., wavelength, polarization, angle of incidence, etc.) by signal separation device440. The separated light signals can then be detected by a corresponding light sensor included in multimodal sensor450. In this manner, multimodal detection system400provides integrated multimodal sensing capabilities, reducing or eliminating the need for multiple discreate or separate sensors like cameras, LiDARs, thermal imaging devices, etc. This will make the sensing device more integrated and compact, reducing the cost, and improving the sensing efficiency. As one example, when discreate sensors are separately mounted to a vehicle (or another moveable platform), data captured by different sensors (e.g., a LiDAR sensor and an image sensor like a camera) often need to be time synchronized and/or converted to use the same coordinate system. This data fusion process can be cumbersome, error prone, inefficient, and power consuming. By integrating multiple sensors together in a multimodal sensor disclosed herein, at least some of the above problems can be solved. For instance, if a LiDAR sensor and an image sensor are integrated together (e.g., disposed in one device package, PCB, and sharing at least a part of the transmitting/receiving light paths), data from the two sensors can be fused together directly without having to perform time synchronization or coordinates conversion first, or with minimum fusion effort.

As described above, multimodal sensor450can include an integrated sensor array comprising multiple sensors having different types.FIG.9illustrates example packaging configurations for integrated sensors, according to various embodiments of the present disclosure. As shown inFIG.9, a multimodal sensor device904may include a plurality of sensors906, each of which is disposed on a heatsink912. The sensors906may be of the same type of different types. Each of the sensors906can be wired bonded to an integrated circuit chip908. The IC chip908can be used to process electrical signals generated by the sensors906, thereby implementing a readout circuitry. The IC chip908can further include other signal processing circuits such as rendering images, performing digital signal processing functions, etc. In this configuration, the sensor array is integrated with the readout circuitry in the same device package (e.g., both IC chip908and sensor array906are disposed on the same PCB914). In other embodiments, the sensor array and the readout circuitry may be individually packaged in separate modules. The two separate modules can then be mounted to a PCB board so that signals can be passed between the two modules.

FIG.9also illustrates another packaging configuration where the readout circuits920are disposed in one semiconductor chip and the integrated sensor array924are disposed in another semiconductor chip926. The two chips920and926are bonded together via flip-chip technologies so that electrical signals can be delivered from the sensor array924to the readout circuits920via solder bumps922. Once bonded, the two chips920and926can be packaged together as a single device930. It is understood that other packaging techniques can also be used, for example, through-hole packaging, surface-mounting packaging, ball grid array packaging, chip-scale packaging, etc.

As described above, some LiDAR or HyDAR systems use the time-of-flight (ToF) of light signals (e.g., light pulses) to determine the distance to objects in a light path. The following description uses LiDAR system1000as an example. It is understood that the LiDAR device or sensor in a HyDAR system may operate similarly. For example, with reference toFIG.10A, an example LiDAR system1000includes a laser light source (e.g., a fiber laser), a steering mechanism (e.g., a system of one or more moving mirrors), and a light detector (e.g., a photodetector with one or more optics). LiDAR system1000can be implemented using, for example, LiDAR system300described above. LiDAR system1000transmits a light pulse1002along light path1004as determined by the steering mechanism of LiDAR system1000. In the depicted example, light pulse1002, which is generated by the laser light source, is a short pulse of laser light. Further, the signal steering mechanism of the LiDAR system1000is a pulsed-signal steering mechanism. However, it should be appreciated that LiDAR systems can operate by generating, transmitting, and detecting light signals that are not pulsed and derive ranges to an object in the surrounding environment using techniques other than time-of-flight. For example, some LiDAR systems use frequency modulated continuous waves (i.e., “FMCW”). It should be further appreciated that any of the techniques described herein with respect to time-of-flight based systems that use pulsed signals also may be applicable to LiDAR systems that do not use one or both of these techniques.

Referring back toFIG.10A(e.g., illustrating a time-of-flight LiDAR system that uses light pulses), when light pulse1002reaches object1006, light pulse1002scatters or reflects to form a return light pulse1008. Return light pulse1008may return to system1000along light path1010. The time from when transmitted light pulse1002leaves LiDAR system1000to when return light pulse1008arrives back at LiDAR system1000can be measured (e.g., by a processor or other electronics, such as control circuitry350, within the LiDAR system). This time-of-flight combined with the knowledge of the speed of light can be used to determine the range/distance from LiDAR system1000to the portion of object1006where light pulse1002scattered or reflected.

By directing many light pulses, as depicted inFIG.10B, LiDAR system1000scans the external environment (e.g., by directing light pulses1002,1022,1026,1030along light paths1004,1024,1028,1032, respectively). As depicted inFIG.10C, LiDAR system1000receives return light pulses1008,1042,1048(which correspond to transmitted light pulses1002,1022,1030, respectively). Return light pulses1008,1042, and1048are formed by scattering or reflecting the transmitted light pulses by one of objects1006and1014. Return light pulses1008,1042, and1048may return to LiDAR system1000along light paths1010,1044, and1046, respectively. Based on the direction of the transmitted light pulses (as determined by LiDAR system1000) as well as the calculated range from LiDAR system1000to the portion of objects that scatter or reflect the light pulses (e.g., the portions of objects1006and1014), the external environment within the detectable range (e.g., the field of view between path1004and1032, inclusively) can be precisely mapped or plotted (e.g., by generating a 3D point cloud or images).

If a corresponding light pulse is not received for a particular transmitted light pulse, then LiDAR system1000may determine that there are no objects within a detectable range of LiDAR system1000(e.g., an object is beyond the maximum scanning distance of LiDAR system1000). For example, inFIG.10B, light pulse1026may not have a corresponding return light pulse (as illustrated inFIG.10C) because light pulse1026may not produce a scattering event along its transmission path1028within the predetermined detection range. LiDAR system1000, or an external system in communication with LiDAR system1000(e.g., a cloud system or service), can interpret the lack of return light pulse as no object being disposed along light path1028within the detectable range of LiDAR system1000.

InFIG.10B, light pulses1002,1022,1026, and1030can be transmitted in any order, serially, in parallel, or based on other timings with respect to each other. Additionally, whileFIG.10Bdepicts transmitted light pulses as being directed in one dimension or one plane (e.g., the plane of the paper), LiDAR system1000can also direct transmitted light pulses along other dimension(s) or plane(s). For example, LiDAR system1000can also direct transmitted light pulses in a dimension or plane that is perpendicular to the dimension or plane shown inFIG.10B, thereby forming a 2-dimensional transmission of the light pulses. This 2-dimensional transmission of the light pulses can be point-by-point, line-by-line, all at once, or in some other manner. That is, LiDAR system1000can be configured to perform a point scan, a line scan, a one-shot without scanning, or a combination thereof. A point cloud or image from a 1-dimensional transmission of light pulses (e.g., a single horizontal line) can generate 2-dimensional data (e.g., (1) data from the horizontal transmission direction and (2) the range or distance to objects). Similarly, a point cloud or image from a 2-dimensional transmission of light pulses can generate 3-dimensional data (e.g., (1) data from the horizontal transmission direction, (2) data from the vertical transmission direction, and (3) the range or distance to objects). In general, a LiDAR system performing an n-dimensional transmission of light pulses generates (n+1) dimensional data. This is because the LiDAR system can measure the depth of an object or the range/distance to the object, which provides the extra dimension of data. Therefore, a 2D scanning by a LiDAR system can generate a 3D point cloud for mapping the external environment of the LiDAR system.

The density of a point cloud refers to the number of measurements (data points) per area performed by the LiDAR system. A point cloud density relates to the LiDAR scanning resolution. Typically, a larger point cloud density, and therefore a higher resolution, is desired at least for the region of interest (ROI). The density of points in a point cloud or image generated by a LiDAR system is equal to the number of pulses divided by the field of view. In some embodiments, the field of view can be fixed. Therefore, to increase the density of points generated by one set of transmission-receiving optics (or transceiver optics), the LiDAR system may need to generate a pulse more frequently. In other words, a light source in the LiDAR system may have a higher pulse repetition rate (PRR). On the other hand, by generating and transmitting pulses more frequently, the farthest distance that the LiDAR system can detect may be limited. For example, if a return signal from a distant object is received after the system transmits the next pulse, the return signals may be detected in a different order than the order in which the corresponding signals are transmitted, thereby causing ambiguity if the system cannot correctly correlate the return signals with the transmitted signals.

To illustrate, consider an example LiDAR system that can transmit laser pulses with a pulse repetition rate between 500 kHz and 1 MHZ. Based on the time it takes for a pulse to return to the LiDAR system and to avoid mix-up of return pulses from consecutive pulses in a typical LiDAR design, the farthest distance the LiDAR system can detect may be 300 meters and 150 meters for 500 kHz and 1 MHz, respectively. The density of points of a LiDAR system with 500 kHz repetition rate is half of that with 1 MHz. Thus, this example demonstrates that, if the system cannot correctly correlate return signals that arrive out of order, increasing the repetition rate from 500 kHz to 1 MHZ (and thus improving the density of points of the system) may reduce the detection range of the system. Various techniques are used to mitigate the tradeoff between higher PRR and limited detection range. For example, multiple wavelengths can be used for detecting objects in different ranges. Optical and/or signal processing techniques (e.g., pulse encoding techniques) are also used to correlate between transmitted and return light signals.

Various systems, apparatus, and methods described herein may be implemented using computers operating in a client-server relationship. Typically, in such a system, the client computers are located remotely from the server computers and interact via a network. The client-server relationship may be defined and controlled by computer programs running on the respective client and server computers. Examples of client computers can include desktop computers, workstations, portable computers, cellular smartphones, tablets, or other types of computing devices.

A high-level block diagram of an example apparatus that may be used to implement systems, apparatus and methods described herein is illustrated inFIG.11. Apparatus1100comprises a processor1110operatively coupled to a persistent storage device1120and a main memory device1130. Processor1110controls the overall operation of apparatus1100by executing computer program instructions that define such operations. The computer program instructions may be stored in persistent storage device1120, or other computer-readable medium, and loaded into main memory device1130when execution of the computer program instructions is desired. For example, processor1110may be used to implement one or more components and systems described herein, such as control circuitry350(shown inFIG.3), vehicle perception and planning system220(shown inFIG.2), and vehicle control system280(shown inFIG.2). Thus, the method steps of at least some ofFIGS.1-23can be defined by the computer program instructions stored in main memory device1130and/or persistent storage device1120and controlled by processor1110executing the computer program instructions. For example, the computer program instructions can be implemented as computer executable code programmed by one skilled in the art to perform an algorithm defined by the method steps discussed herein in connection with at least some ofFIGS.1-23. Accordingly, by executing the computer program instructions, the processor1110executes an algorithm defined by the method steps of these aforementioned figures. Apparatus1100also includes one or more network interfaces1180for communicating with other devices via a network. Apparatus1100may also include one or more input/output devices1190that enable user interaction with apparatus1100(e.g., display, keyboard, mouse, speakers, buttons, etc.).

Processor1110may include both general and special purpose microprocessors and may be the sole processor or one of multiple processors of apparatus1100. Processor1110may comprise one or more central processing units (CPUs), and one or more graphics processing units (GPUs), which, for example, may work separately from and/or multi-task with one or more CPUs to accelerate processing, e.g., for various image processing applications described herein. Processor1110, persistent storage device1120, and/or main memory device1130may include, be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs) and/or one or more field programmable gate arrays (FPGAs).

Input/output devices1190may include peripherals, such as a printer, scanner, display screen, etc. For example, input/output devices1190may include a display device such as a cathode ray tube (CRT), plasma or liquid crystal display (LCD) monitor for displaying information to a user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to apparatus1100.

Any or all of the functions of the systems and apparatuses discussed herein may be performed by processor1110, and/or incorporated in, an apparatus or a system such as LiDAR system300. Further, LiDAR system300and/or apparatus1100may utilize one or more neural networks or other deep-learning techniques performed by processor1110or other systems or apparatuses discussed herein.

One skilled in the art will recognize that an implementation of an actual computer or computer system may have other structures and may contain other components as well, and thatFIG.11is a high-level representation of some of the components of such a computer for illustrative purposes.

FIG.12Ais block diagram illustrating an example HyDAR system1200according to various embodiments.FIG.12Bis a block diagram illustrating the example HyDAR1200system in a side view, according to various embodiments. In some examples, as shown inFIGS.12A and12B, HyDAR system1200includes a LIDAR sensor1202and an image sensor1204. As described above, LiDAR sensor1202and image sensor1204may be integrated together (e.g., disposed in a same semiconductor die or package) or combined together to form a multimodal sensor. The LiDAR sensor1202can be configured to detect first return light signals having a first wavelength (e.g., light signals in the infrared wavelength range) and the image sensor1204can be configured to detect second return light signals having a second wavelength (e.g., light signals in the visible light wavelength range). The LiDAR sensor1202may include, for example, at least a part of optical receiver and light detector330described above in connection withFIG.3and/or at least a part of optical receiver and light detector430of the multimodal detection system400described above in connection withFIG.4. The multimodal sensor formed by the LiDAR sensor1202and the image sensor1204can be substantially the same as the multimodal sensor450described above. The image sensor1204, in some examples, can include a near-infrared (NIR) sensor, a mid-infrared (MIR) sensor, and/or a visible light sensor.

With reference still toFIG.12A, HyDAR system1200also includes, for example, one or more steering mechanisms1206, lens or lens groups1210and1212, an aperture window1208, and a controller1214. Other components of the HyDAR system1200are omitted fromFIG.12Afor simplicity. For example,FIG.12Adoes not show a light source but it is understood that a light source, transmitter, and other components similar to those described above for a multimodal detection system (e.g., system400) can be included in HyDAR system1200.

The LiDAR sensor1202and image sensor1204shown inFIG.12Aform a multimodal sensor and thus share some of the transmitting and receiving light paths. For instance, as shown inFIG.12A, one or more steering mechanisms1206can be shared between the LiDAR sensor1202and image sensor1204. A light source (not shown inFIG.12A) sends laser light signals to the steering mechanisms1206(which can include a polygon mirror, an oscillation mirror, a prism, a lens, and/or any other optical components configured to steer light). The one or more steering mechanisms1206redirect the laser light signals to form transmission light signals1201, which are scanned across the FOV1220in both horizontal and vertical directions to illuminate one or more objects in the FOV1220. The transmission light signals1201may have a wavelength in the infrared wavelength range. When the transmission light signals1201are scattered or reflected by one or more objects in FOV1220, first return light signals1203are formed. The first return light signals1203have the same wavelength as the transmission light signals1201and are directed back to steering mechanisms1206. In turn, steering mechanisms1206redirect the first return light signals1203toward LiDAR sensor1202via one or more receiving optical components such as a collection lens or lens group1210. Thus, one or more steering mechanisms1206is used to perform both transmission of laser light signals toward the aperture window1208and receiving first return light signals1203formed based on at least a portion of the laser light signals provided by the laser light source.

At least a part of the light path described above associated with the LiDAR sensor1202can be shared with the image sensor1204. Image sensor1204may be a camera that senses second return light signals1205(e.g., NIR/MIR/visible light). The second return light signals1205are formed from light provided by one or more external light sources external to the HyDAR system1200. Such light sources may include, for example, sunlight, moonlight, light from vehicle headlights, and/or streetlights. These light sources can directly emit light to the HyDAR system1200. The second return light signals1205can also include reflection, transmission, and/or refraction of the light emitted by the aforementioned light sources. Other visible light sources are also possible, and the second return light signals1205are not limited to the above-described light signals. The second return light signals1205are also received by one or more steering mechanisms1206. Steering mechanisms1206then redirect the second return light signals1205to image sensor1204via one or more receiving optical components like collection lens or lens group1212(FIG.12A). Lens or lens group1212may be a focal lens (FIG.12B). In some examples, collection lens1210and focal lens1212may be the same lens, and other optical components (e.g., a mirror, a wavelength splitter, etc.) be used to separate the first and second return light signals and redirect them to respective LiDAR sensor1202and image sensor1204. The details of light collection and distribution, and signal separation are described above in connection withFIGS.6and7, and are therefore not repeatedly described.

WhileFIG.12Aillustrates that the LiDAR sensor1202and image sensor1204are located at two opposite sides of the one or more steering mechanisms1206, they may be located at the same side.FIG.12Billustrates a side view of such a configuration, where the LiDAR sensor1202and the image sensor1204are located at the same side of steering mechanisms1206and are aligned to receive the respective return light signals. In some examples, because the wavelengths of the return light signals received by the image sensors1204and LiDAR sensors1202are different, a focal lens1212is used to adjust the focal length of the optical path of the second return light signals1205. As shown inFIG.12B, the image sensor1204and LiDAR sensor1202may be placed on a same semiconductor substrate/die/package/PCB, and thus, the focal lens1212can adjust the focal length of the visible light received and redirected by the steering mechanisms1206.

As shown inFIG.12B, in some examples, the LiDAR sensor1202and image sensor1204at least share a part of the receiving light path. In particular, the first return light signals1203and second return light signals1205are both received through the aperture window1208, redirected by steering mechanisms1206and other shared optical components (e.g., a same collection lens). The focal length of one optical path for the second return light signals1205may be adjusted by using a focal lens1212. Therefore, the first return light signals1203and the second return light signals1205may be time and space synchronized already at the hardware level, when they are received by the LiDAR sensor1202and image sensor1204, respectively.

With reference still toFIGS.12A and12B, the LiDAR sensor1202obtains one or more frames of point cloud data based on the received first return light signals1203(e.g., return pulses in the infrared wavelength range). For instance, the LiDAR sensor1202can convert the first return light signals1203to analog electrical signals, which can then be sampled and digitized to form one or more frames of point cloud data1218. Point cloud data1218represents the FOV1220based on the scanning of the FOV1220using the transmission light signals1201in the first wavelength (e.g., infrared light). The image sensor1204obtains one or more frames of image data1216based on the second return light signals1205(e.g., reflected visible light from one or more objects in the FOV1220). For instance, the image sensor1204can be a camera that has a high resolution such that it captures the FOV1220based on the received light signals in a second wavelength (visible/NIR/MIR light). The image sensor1204(e.g., a CMOS camera, a CCD camera) converts the detected light signals to electrical signals to form one or more frames of image data1216.

As described above, if discreate sensors are separately mounted to a vehicle (or another moveable platform), data captured by different sensors (e.g., a LiDAR sensor and an image sensor like a camera) often need to be time synchronized and/or converted to use the same coordinate system. This is because the discreate sensors are not time synchronized so the images captured by the discreate sensors may be shifted in timing. Moreover, the discreate sensors may be mounted to different locations of the vehicle (e.g., a LiDAR sensor mounted to the rooftop of the vehicle, while the camera is mounted to a side mirror of the vehicle). Thus, the angles from which they capture the images are different. To fuse the image data provided by the discreate sensors, coordinate systems need to be transformed. This complex process is often referred to as data fusion. The data fusion process can involve a large number of computational efforts using vectors and matrixes, because both the LiDAR sensor and the image sensor can generate a large amount of data over every unit of time (e.g., per second, minute, or hour). And if the LiDAR sensor and the image sensor operate for an extended period of time (like when they are used for a vehicle and operate for hours), they can generate even a huge amount of data over time. The data fusion process thus typically requires a very large computational capability of the system, and therefore often requires additional hardware (e.g., many GPUs) and/or software support. The data fusion process can thus be cumbersome, error prone, inefficient, costly, and power consuming.

By integrating multiple sensors together in a multimodal sensor (e.g., sensor450) disclosed herein, at least some of the above problems can be solved. For instance, inFIG.12A, a LiDAR sensor1202and an image sensor1204are integrated together. Thus, LiDAR sensor1202and image sensor1204are disposed in one semiconductor die/device package/PCB, and can share at least a part of the transmitting/receiving light paths. As shown inFIGS.12A and12B, the steering mechanisms1206may have an optical scanner (e.g., a polygon mirror, an oscillation mirror, or a combination thereof) that is configured to perform: (1) scanning the laser light signals1201in both horizontal and vertical directions; (2) receiving the first return light signals1203and the second return light signals1205; and (3) directing the first return light signals1203and the second return light signals1205to the LiDAR sensor1202and the image sensor1204, respectively. Because of the hardware-level integration, the point cloud data1218generated by LiDAR sensor1202and the image data1216generated by image sensor1204are time-and-space synchronized. In particular, as shown inFIG.12A, the corresponding frames between the point cloud data1218and the image data1216are already synchronized in time. Moreover, the angles from which the LiDAR sensor1202and the image sensor1204capture the FOV1220are the same. Thus, there is no need to perform coordinate transformation. Data1218and1216from the two sensors1202and1204, respectively, can be fused together directly by controller1214without having to perform time synchronization or coordinate transformation. The data fusion process is thus enabled early by the hardware configurations (e.g., by using the same steering mechanism1206and sharing other optical components). This kind of data fusion is thus referred to as early fusion, as compared to data fusion performed by software by the vehicle planning and perception system at a later stage. The latter type of data fusion requires a computer system to take the point cloud data and the image data separately, perform alignment of the timing between the two sets of data, and perform coordinate transformation between the two sets of data to correlate between them. The early fusion thus greatly improves the data processing efficiency, reduces the computational efforts, and power consumption.

As described above, a HyDAR system1200may include a controller1214. The controller1214may control the operations of the various components of the HyDAR system1200and may also process data generated by the multimodal sensor including the LiDAR sensor1202and the image sensor1204. Based on the data (e.g., the already fused data at the hardware level), the controller1214may also be configured to detect one or more degradation factors affecting the HyDAR system's performance, and cause adjustment of one or more device configurations and/or one or more operational conditions of the HyDAR system to remove or reduce the impact of the degradation factors. In some other cases, other than the controller1214, there may be another circuitry or computer system connected with the HyDAR system1200for performing at least some of the aforementioned operations. As described above, various external and internal factors may affect the HyDAR system's performance, and thus over time, the HyDAR system's performance may degrade. A HyDAR system's degradation factors may include, for example, at least a partial window blockage of the aperture window, interference signals provided by one or more interference light sources, extrinsic calibration degradation measured by a relation between the HyDAR system and a moveable platform to which the HyDAR system is mounted, and intrinsic calibration degradation associated with misaligned internal components of the HyDAR system. Each one of these degradation factors is described in detail below.

FIG.13is a flowchart1300illustrating a method for detecting operation of a HyDAR system (e.g., HyDAR system1200) for detecting one or more of the performance degradation factors, according to various embodiments. As shown inFIG.13, in some examples, a light source or transmitter in the HyDAR system provides laser light signals (block1302). The one or more steering mechanisms of the HyDAR system direct the laser light signals toward an aperture window (block1304). Absent a complete window blockage at the aperture window, the laser light signals go through the aperture window. In block1306, the HyDAR system receives the first return light signals formed based on at least a portion of the laser light signals. In block1312, the HyDAR system receives the second return light signals provided by one or more light sources external to the HyDAR system. The first return light signals and second return light signals may be received by the same steering mechanism of the HyDAR system as described above.

Next, in block1308, a LiDAR sensor in the HyDAR system detects the first return light signals to obtain one or more frames of point cloud data. In block1314, an image sensor of the HyDAR system detects the second return light signals to obtain one or more frames of image data. As described above, the point cloud data and the image data are time-and-space synchronized at the hardware level such that no additional processor (e.g., GPU) or software is needed for synchronizing them. The controller of the HyDAR system, based on one or both of the point cloud data and the image data, can perform detection of one or more performance degradations factors affecting the HyDAR system's performance (block1320). Such detections include detecting at least a partial window blockage of the aperture window (block1330), detecting interference signals provided by one or more interference light sources (block1340), detecting extrinsic calibration degradation measured by a relation between the HyDAR system and a moveable platform to which the HyDAR system is mounted (block1350), and detecting intrinsic calibration degradation associated with misaligned internal components of the HyDAR system (block1360). In some examples, the controller of the HyDAR system may also cause adjustments of device configuration and/or an operational condition of the HyDAR system to remove or reduce the negative impact of the degradation factors (block1322). Blocks1330,1340,1350, and1360are described in greater detail below.

Beginning with block1330, the detection of at least a partial window blockage of the HyDAR system is described in detail.FIG.14Ais a histogram chart illustrating a comparison of return signal intensities between an at least partially blocked aperture window and an unblocked aperture window, according to various embodiments. InFIG.14A, the vertical axis represents the signal intensity, and the horizontal axis represents the number of received return light signals falling in each of the bin of the signal intensity. As shown inFIG.14A, signals1402and1404represent first return light signals that are formed based on transmitted laser signals from the HyDAR system. If an aperture window (e.g., window1208shown inFIGS.12A and12B) is not blocked, the return light signals1402may have the normal intensity, depending on if the return light signals1402are formed by an object located at or proximate to the aperture window or by an object located at a distance far away from the aperture window. Generally, the further away the object is located, the smaller the intensity of the return light signals. In contrast, if an aperture window is at least partially blocked, the return light signal1404has a higher signal intensity compared to that of return light signal1402(which is received based on normal scattering without blockage). This is because the laser light signals from the HyDAR system is scattered more towards the detectors due to the blockage. Thus, the return light signal1404is formed by scattering of the transmitted light signals by the blocked aperture window, the objects that block the aperture window, and/or other internal components of the HyDAR system. As such, the signal intensity of the return light signals1404can be considerably large. An aperture window blockage can therefore be detected based on the signal intensity of the return light signals formed based on the transmitted laser light signals. In other words, in some cases, the window blockage can be detected using the point cloud data generated by the LiDAR sensor alone. In other examples, the window blockage can be detected using both the point cloud data generated by the LiDAR sensor and the image data generated by the image sensor, as described in more detail below. The addition of the image data provides more information of the blockage and helps to improve the blockage detection precision and/or the classification of the blocking objects.

Many types of objects may cause window blockage for an aperture window of a HyDAR system.FIG.14Bis a diagram illustrating at least a partial aperture window blockage for a HyDAR system due to several different types of objects, according to various embodiments. For instance, as shown inFIG.14B, an aperture window1414of a HyDAR system1412may be blocked (or partially blocked) by object1418(e.g., a leaf) or object1422(e.g., water condensations/rain drops). Objects1418and/or1422may be located at or proximate to the aperture window, therefore blocking at least a portion of the window1414. Other types of objects may also block the window1414, like a plastic bag, a paper, debris, dirt, snow, etc. If window1414is at least partially blocked, the transmission light signals emitted from a laser light source of the HyDAR system1412may be blocked or scattered by, e.g., objects1418and/or1422. The return signals generated by objects1418and/or1422may correspond to the signals1404inFIG.14A, which have a high signal intensity.FIG.14Bshows that aperture window1414is partially blocked, and therefore, the transmission light signals can go through window1414and possibly reach an object1416in the FOV. Object1416may be an object located at a distance away from the aperture window1414(near or far), and may form first return light signals corresponding to signals1402inFIG.14A. The below disclosure describes several embodiments of methods for detecting at least a partial window blockage of an aperture window and determining the locations, types, and extent of the blockage.

FIGS.15A-15Dare flowcharts illustrating various methods for detecting aperture window blockage of a HyDAR system, according to various embodiments.FIG.15Aprovides an overview of the methods for detecting aperture window blockage using point cloud data, image data, a combination thereof, and/or fused data. As shown inFIG.15A, method1500can be performed by a controller (e.g., control circuitry350or controller1214described above) or another computing device (e.g., a device shown inFIG.11). A controller may include both analog circuitry for processing analog signals (e.g., analog voltage or current signals converted from return light signals), digital circuitry for processing digital signals (e.g., digitized/sampled analog voltage or current signals), or mixed signal circuitry for processing mixed signals, For simplicity, method1500is described below by using a controller to detect at least a partial window blockage of the aperture window of a HyDAR system. Method1500corresponds to block1330ofFIG.13.

As described above, in a HyDAR system (e.g., system1200), a LiDAR sensor (e.g., sensor1202) can convert first return light signals (e.g., signals1203) to electrical signals, based on which point cloud data (e.g., data1218) are generated. An image sensor (e.g., sensor1204) can convert second return light signals (e.g., signals1205) to electrical signals, based on which image data (e.g., data1216) are generated. The point cloud data may include one or more frames. A frame of point cloud data may correspond to a one complete scan of the FOV. Similarly, the image data may include one or more frames. A frame of the image data may refer to a captured image at a predetermined resolution. The methods described below inFIGS.15A-15Dinclude detecting window blockage using a serial pipeline based on both point cloud data and image data; using a parallel pipeline based on both point cloud data and image data; using fused point cloud data and image data; and using image data only or using point cloud data only.

FIG.15Ais an overview of various methods for blockage detection and thus not all blocks inFIG.15Amay be necessary. For example, if fused point cloud data and image data are used (block1506), then determination blocks1508and1510may not be necessary. With reference toFIG.15A, in block1502of method1500, the controller obtains, based on the one or more frames of the point cloud data, a first deviation of the first return light signals from a first expectation. In block1504of method1500, the controller obtains, based on the one or more frames of the image data, a second deviation of the second return light signals from a second expectation. One or both of blocks1502and1504can be performed. The first return light signals correspond to a first area of the aperture window. The second return light signals correspond to a second area of the aperture window.

The first area and the second area of the aperture window may or may not be the same area. Thus, the LiDAR sensor and the image sensor may or may not capture the same area of the aperture window at the same time. In one example, the two sensors may obtain the first return light signals and second return light signals corresponding to the same area of the aperture window at the same particular time. As such, the point cloud data generated by the LiDAR sensor and the image data generated by the image sensor are automatically synchronized in time and space for the particular time. In other examples, the first area of the aperture window and the second area of the aperture window may overlap at a particular time. Therefore, the point cloud data generated by the LiDAR sensor and the image data generated by the image sensor are automatically and partially synchronized in time and space for the particular time. Point cloud data and image data that are partially synchronized may be useful for applications that may not require strict synchronizations.

As described above usingFIGS.14A and14B, if an aperture window of a LiDAR sensor or HyDAR system is at least partially blocked, the return light signals may deviate from the expected values. For a LIDAR sensor,FIG.14Aillustrates a comparison between the intensities of first return light signals1402where there are no window blockage and first return light signals1404where there is at least a partial window blockage. If there is a blockage, the signal intensity is much higher than expected values or a range values compared to if there is no blockage. For an image sensor, if there is a window blockage, the return signal intensity may be much lower than if there is no window blockage. For instance, if a leaf is covering a part of the aperture window, an image sensor (e.g., a camera) would detect a much lower light intensity.

Signal intensity is only one of the characteristics that can be used for detecting deviations from expectations. Other characteristics may include, but are not limited to, an average signal intensity; a distribution of the signal intensity; a size and/or a shape associated with one or both of the first area and the second area of the aperture window over time; sensitivity to a predetermined wavelength range; a point count of the point cloud data; or a distribution of the distances represented by the point cloud data or the image data. For all these characteristics, there may be predetermined expectations (e.g., normal ranges of intensity, distribution, sensitivity, point count, distribution of distances). Based on the point cloud data, the controller can detect changes in these characteristics, which represent the first deviation. Similarly, based on the image data, the controller can detect changes in some of these characteristics, which represent the second deviation.

With reference still toFIG.15A, in some examples, the controller performs at least one of blocks1508or1510. That is, the controller determines if the first deviation exceeds a first threshold (block1508), if the second deviation exceeds a second threshold (block1510), or both. For any of the first deviation or the second deviation, if the deviation from the expectation is small (e.g., smaller than the respective first or second threshold), it probably means that there is unlikely an aperture window blockage or not a noticeable blockage. For instance, the deviation may be caused by other factors like noise, interference, extrinsic calibration degradations, intrinsic calibration degradations, etc. (described below). As such, no further detection of the window blockage may be performed. If the first deviation, the second deviation, or both exceeds the respective thresholds, it may mean that there is likely a window blockage. If any of the deviation does not exceed the respective threshold, the method1500can loop back to obtain the next frame of point cloud data and/or the next frame of image data.

As shown inFIG.15A, the controller can perform a logic OR operation (block1512) of the outputs of blocks1508and1510, and if the result of the OR operation is positive (or high), the controller determines (block1516) one or more locations, one or more types, and the extent of the at least a partial window blockage of the aperture window. For instance, the point cloud data may have, or be used to derive, the location information (X, Y coordinates), reflectivity information associated with the first return light signals, the distance information, and other information (e.g., speed, orientation, etc. derived from first return light signals). The image data may have a much higher resolution than the point cloud data. The image data may also have information that are not included in the point cloud data. For example, the image data may include color information, brightness, contrast, etc. Therefore, if the controller determines that there is likely an aperture window blockage (e.g., because the first or second deviations, or both, exceed the respective thresholds), the point cloud data and the image data can be used to determine the location, the type, and the extent of the window blockage. For instance, if for a particular area of the aperture window, the first deviation indicates that the signal intensity is abnormally high (because the LiDAR transmission light signals are reflected by the blocking object) and the second deviation indicates that the signal intensity at the same area is abnormally low (because the visible light from an external light source is blocked from entering the image sensor), then the controller can determine that the area has a window blockage.

Based on the point cloud data, the image data, or both, the controller can further determine the shape and size of the area that is blocked. Based on the values of the first deviation and/or the second deviation, the controller may also determine the extent of the window blockage. For instance, if the values of the first deviation and/or the second deviation do not exceed the respective thresholds too much, it may mean that the object blocking the aperture window can still allow some transmission light signals to go out or some return light signals to come in from external of the HyDAR system. This type of objects may include, for example, water condensation, rain drops, plastic bags, or other transparent, or semi-transparent objects. In contrast, if the values of the first deviation and/or the second deviation exceed the respective thresholds a lot, it may mean that the object blocking the aperture window is rather opaque (e.g. a leaf).

The shape or size of the blocking object can be obtained by, for example, obtaining a deviation distribution of the point cloud data and/or the image data. The deviation values corresponding to the area of the aperture window that is blocked may have values that are significantly different from those areas that are not blocked. As such, the shape and size of the blocked area can be derived. In some examples, using the image data, and one or more pattern recognition algorithms (e.g., AI/ML based pattern recognition algorithms), the controller can even identify the type of the blocking object. For example, using a neural network that is trained to recognize objects, the controller may determine that that blocking object is a plastic bag, a leaf, rain drops, etc.

With reference still toFIG.15A, if the controller determines that the first deviation does not exceed a first threshold (block1508), it may just obtain a next frame of point cloud (block1509) for processing, and the process loops back to block1502to obtain a first deviation based on the next frame of point cloud data. Similarly, if the controller determines that the second deviation does not exceed a second threshold (block1510), it may just obtain a next frame of image data (block1511) for processing, and the process loops back to block1504to obtain a second deviation based on the next frame of image data.

As described above, if any or both of the determinations at blocks1508and1510are yes, the controller determines (block1516) that there is at least a partial blockage of the aperture window and further determines the locations, types, and extent of the at least partial window blockage. In method1500shown inFIG.15A, the controller may further determine (block1518) whether the at least a partial window blockage persists over no less than one data collection cycle. In certain scenarios, blockage of the aperture window may not be persistent. For instance, an object (e.g., a leaf, a plastic bag, a flower, etc.) that blocks the window may disappear or move its location the next second. Therefore, the controller can be configured to detect if the window blockage is persistent over no less than one data collection cycle. A data collection cycle may be a time period for collecting one frame of the point cloud data and/or the image data. It may also correspond to be a time period having other predetermined lengths.

In some examples, if the controller determines that the at least partial window blockage is persistent over no less than one data collection cycle, it can perform at least one of: reporting (block1520) the at least partial window blockage; or activating (block1522) a blockage removal mechanism for removing the at least a partial window blockage. For example, the controller may activate one or more nozzles to dispense fluid (e.g., compressed air, water, etc.) to try to blow away the objects that caused the at least partial window blockage. It may activate a window shield wiper or any other mechanisms (e.g., heater for removing condensation, fans to blow away plastic bags or leaves, etc.) to remove the blocking objects. If the partial window blockage cannot be removed after certain removal mechanisms are activated, the controller may report the at least partial window blockage to the user or to a system (e.g., a vehicle planning system or a vehicle control system).

The processes described above use the point cloud data and image data in separate processing pipelines for detecting at least a partial window blockage.FIG.15also illustrates another process of detecting at least a partial window blockage using fused point cloud data and image data. As described above, in the described multimodal sensor of a HyDAR system, when the point cloud data is provided by the LiDAR sensor and the image data is provided by the image sensor, they are already time-and-space synchronized at the hardware level. Therefore, the point cloud data and image data can be easily fused together (block1506) to obtain fused data. The fusing process may not require timing shift and/or coordinate transformation between the point cloud data and the image data, or may require significantly less computational efforts. In one example, for further fusion, the outputs from both the LiDAR sensor and the image sensor are provided to a fusion processor or a data merger (e.g., a part of the controller, a dedicated processor, or any other computers). Based on the fused data, the controller can detect (block1514) a fused blockage detection result. For example, because the fused data incorporate both the point cloud data and the image data, the blockage detection confidence can be enhanced. For example, if the controller only uses the point cloud data from the LiDAR sensor to detect blockage, the confidence of a blockage detection may be only 50%. If the controller uses fused data, which incorporates the image data, the confidence of a blockage detection (or lack thereof) may be increased to 90%. After the blockage detection is performed, the process can proceed to block1518,1520and1522, which are the same as described above.

FIG.15Aprovides an overview of the method1500(corresponding to block1330inFIG.13) for detecting at least a partial window blockage.FIGS.15B-15Dprovide several particular embodiments of method1500. With reference toFIG.15Bfirst, in method1500A, the controller can start (block1531) by obtaining a frame of data including the point cloud data and the image data. The point cloud data represents the first return light signals over a first area (block1532) and the image data represents the second return light signals over a second area (block1534). As described above, the first area and the second area may be the same area, or may be different areas having an overlap. In blocks1538and1540, the controller determines if there is a first deviation of the point cloud data from the first expectation (block1539) and if there is a second deviation of the image data from the second expectation (block1540). The first and second deviations may represent one or more changes in a signal intensity or an average signal intensity, changes in a distribution of the signal intensity; changes in a size and/or a shape associated with one or both of the first area and the second area of the aperture window over time; changes in sensitivity to a predetermined wavelength range; changes in a point count of the point cloud data; and/or changes in a distribution of the distances represented by the point cloud data or the image data.

In some examples, in blocks1538and1540, the controller further determines if the first deviation and second deviation exceed a first threshold and a second threshold, respectively. If so, the outputs from blocks1538and/or1540are “yes”, and vice versa. In some examples, the controller just determines whether there are any deviations from the expectations, and if so, the outputs from blocks1538and/or1540are “yes”, and vice versa. It is understood that the blocks1538and1540can be performed in any order. For example, the determination of the deviations of point cloud data and image data from their respective expectations can be performed in parallel in timing. Alternatively, the determination of deviation of point cloud data from its expectation can be performed before the determination of deviation of image data from its expectation, or vice versa. As shown inFIG.15B, the outputs of the determinations in blocks1538and1540are “ORed” such that if any of the outputs of the determinations is “yes”, the controller calculates the blockage location, the type of the blockage, and the extend of the blockage. And if both of the outputs of the determinations from blocks1538and1540are “no”, the controller can proceed with obtaining the next frame of data (block1549).

In some examples, if the determinations from blocks1538and1540are different, the controller may also determine a priority or confidence levels based on certain rules or settings, environmental conditions, or other factors. For example, the controller may be configured to set that the determination result from block1538(i.e., the determination based on point cloud data provided by the LiDAR sensor) has a higher priority than the determination from block1540(i.e., the determination based on the image data provided by the image sensor), or vice versa. The controller may also be configured to set the confidence level higher for the determination result from block1538than the determination result from block1540, or vice versa. The controller may be further configured to set the priority or confidence level based on factors like environmental conditions. For instance, under certain weather conditions, one sensor may perform better than the other sensor, and therefore, the priority or confidence level can be set higher for the sensor that performs better. As one example, when the image sensor is directly facing the Sun, it may be saturated and not capturing any useful information. In contrast, a LiDAR sensor may perform well under this condition. In this scenario, the determination based on the point cloud data provided by the LiDAR sensor may be set to have a higher priority or higher confidence. It is understood that the controlled can be configured to have other settings of priority and/or confidence levels associated with the determination of deviations based on point cloud data and image data.

In method1500A shown inFIG.15B, the controller can increase a blockage counter by 1 for the detected blockage location of the aperture window (block1547). The blockage counter may correspond to the data collection cycle described above inFIG.15A. Thus, if the blockage persists more than 1 data collection cycle, the blockage counter is greater than 1. In other examples, there may be multiple block counters, each corresponding to a particular location of the aperture window. Thus, values of the block counters in different areas of the aperture window may be different. For instance, a particular location of the aperture window may have a higher counter value, indicating that the particular location may be blocked more than another location (e.g., it may be blocked for many data collection cycles and/or allowing less light to pass through).

With reference still toFIG.15B, in block1548, the controller may compare the counter value to a threshold (e.g., 1). If the counter value is greater than the threshold, the controller may report the window blockage with confidence (block1545). If the counter value is less than the threshold, the controller may report the window blockage with less confidence. (block1543). While inFIG.15B, the threshold value is shown as 1, it can be any other number. The controller can report the window blockage to a user and/or a system (e.g., a vehicle planning and perception system), and may activate one or more mechanisms to remove the blocking object, similar to those described above usingFIG.15A. The method1500A inFIG.15Bdescribed above is also referred to as a parallel pipeline for detecting window blockage.

FIG.15Cillustrates another method1500B for detecting at least a partial window blockage using only the point cloud data and determining the blockage location, type, and extent using the image data. Method1500B starts with obtaining a frame of data (block1551) including point cloud data and image data. The point cloud data represents the first return light signals over a first area (block1552) and the image data represents the second return light signals over a second area (block1554). The first area and the second area may or may not the same area, and may be overlapping with each other. In method1550B, unlike method1550A, the controller uses only the point cloud data to determine if there is a first deviation of the point cloud data from the first expectation (block1558). The controller does not use the image data to determine if there is a second deviation of the image data from the second expectation. The first deviation may represent one or more changes in a signal intensity or an average signal intensity, changes in a distribution of the signal intensity; changes in a size and/or a shape associated with one or both of the first area and the second area of the aperture window over time; changes in sensitivity to a predetermined wavelength range; changes in a point count of the point cloud data; and/or changes in a distribution of the distances represented by the point cloud data or the image data. The controller may use only the point cloud data for the determination in block1558under certain conditions including, e.g., if the point cloud data has a high quality and/or if the environment conditions do not cause many false detections by the LiDAR sensor. In this scenario, it is probably unnecessary, or less necessary, to use the image data for enhancing the confidence of the deviation determination results based only on the point cloud data. As a result, the controller may choose to use only the point cloud data provided by the LiDAR sensor to make the determination in block1558.

In some examples, in block1558, the controller further determines if the first deviation exceeds a first threshold. If so, the output from block1558is “yes”, and method1500B proceeds to block1566. In some examples, the controller just determines whether there are any deviations from the first expectations, and if so, the output from block1558is “yes”. If the output of the determinations from block1558is “no”, the controller can proceed with obtaining the next frame of data (block1569).

As shown inFIG.15C, even if the controller does not use the image data to determine the deviation from an expectation, the controller can use both point cloud data and the image data to calculate (block1566) the blockage location, the type of the blockage, and the extent of the blockage. After such calculations, the method1500B can proceed to blocks1567,1568,1565, and1563. These blocks can be the substantially the same or similar to blocks1547,1548,1545, and1543ofFIG.15B, respectively, and therefore are not repeatedly described. In some examples, the controller can calculate (block1566) the blockage location, the type of the blocking object, and the extent of the blockage without determining the deviation (block1558) or in parallel with determining the deviation. As shown inFIG.15C, if there is no deviation from expectation and/or the deviation from expectation is less than an expectation threshold (block1558), and/or after reporting the blockage (block1563or1565), the controller can proceed to obtain the next frame (block1569), and repeat method1500B. The method1500B described above is also referred to as a LiDAR-only pipeline for detecting window blockage.

FIG.15Dillustrates another method1500C for detecting at least a partial window blockage using fused point cloud data and image data. Method1500C starts with obtaining a frame of data (block1571) including point cloud data and image data. The point cloud data represents the first return light signals over a first area (block1572) and the image data represents the second return light signals over a second area (block1574). The first area and the second area may or may not be the same area, and may be overlapping with each other. As described above, in the described multimodal sensor of a HyDAR system, when the point cloud data is provided by the LiDAR sensor and the image data is provided by the image sensor, they are already time-and-space synchronized at the hardware level. Therefore, the point cloud data and image data can be easily fused together (block1575) by a data merger to obtain fused data. The fusing process may not require timing shift and/or coordinate transformation between the point cloud data and the image data, or may require significantly less computational efforts. In one example, the outputs from both the LiDAR sensor and the image sensor are provided to a fusion processor or a data merger1575(e.g., a part of the controller, a dedicated processor, or any other computers) for further processing. Based on the fused data, the controller can determine (block1578) if there is a deviation from an expectation. The expectation may also be set based on fused expectation data (e.g., fusing the first expectation based on the point cloud data and the second expectation based on the image data). Because the fused data incorporating both the point cloud data and the image data, the blockage detection confidence may be enhanced. For example, if the controller only uses the point cloud data from the LiDAR sensor to detect blockage, the confidence of a blockage detection may be only 50%. If the controller uses fused data, which incorporates the image data, the confidence of a blockage detection (or lack thereof) may be increased to 90%. After the blockage detection is performed, the method1500C can proceed to block1586for calculating the blockage location, the type of the blocking object, and the extent of the blockage. The method1500C can then proceed to blocks1587,1588,1585, and1583. These blocks can be the substantially the same or similar to blocks1547,1548,1545, and1543ofFIG.15B, respectively, and therefore are not repeatedly described. In some examples, the controller can calculate (block1586) the blockage location, the type of the blocking object, and the extent of the blockage without determining the deviation (block1578). As shown inFIG.15D, if there is no deviation from expectation and/or the deviation from expectation is less than an expectation threshold (block1578), and/or after reporting the blockage (block1583or1585), the controller can proceed to obtain the next frame (block1589) and repeat method1500C. The method1500C described above is also referred to as a fused data pipeline for detecting window blockage.

As described above, blockage of an aperture window of a HyDAR system is one degradation factor that may affect the performance of the HyDAR system. Another degradation factor is interference light signals from one or more interference light sources external to the HyDAR system.FIG.16is a diagram illustrating a HyDAR system1600receiving various interference signals provided by one or more interference light sources external to the HyDAR system1600, according to various embodiments. As shown inFIG.16, these external interference light sources may include the Sun1606, the Moon (not shown), headlights from a vehicle1610, streetlights (not shown), and another LiDAR or HyDAR system1612. In other examples, the light from the above-described interference light sources may be reflected, transmitted, and/or refracted by other objects (e.g., object1608, building1604, etc.). These reflected, transmitted, and/or refracted light may be received by HyDAR system1600as interference light signals via aperture window1602. The examples shown inFIG.16are not exclusive and interference light sources are not limited to the examples shown inFIG.16. For example, another interference light source may include a malicious laser scrambler.

A LIDAR sensor in a HyDAR system can be sensitive to external interference light sources. Interference light signals may result in noise in the detection results of the HyDAR system1600, and typically are undesired. In some scenarios, such interference light signals can lead to abnormal behavior of the HyDAR system and may cause harm to the LiDAR sensor and/or users of the HyDAR system. Therefore, the interference light signals need to be detected, reduced, avoided, and/or removed. The methods described herein use both the point cloud data obtained by the LiDAR sensor and/or the image data obtained by the image sensor to detect interference light signals. In some examples, the image sensor integrated in a HyDAR system can improve the system's capabilities of detecting and avoiding interference light signals.

FIGS.17A-17Bare flowcharts illustrating various methods for detecting interference light signals caused by one or more interference light sources, according to various embodiments.FIG.17Aillustrates an example of method1700(corresponding to block1340inFIG.13) for detecting interference signals provided by one or more interference light sources external to the HyDAR system. In method1700, the controller may obtain (block1702) a first light profile representing characteristics of the first return light signals. As described above, the first return light signals are formed based on the transmission light signals emitted by a laser light source. The first return light signals are detected by the LiDAR sensor (e.g., sensor1202). The characteristics of the first return light signals may include, for example, signal intensity, wavelength of the first return light signals, signal distribution of the first return light signals, etc. The characteristics of the first return light signals are characteristics associated with the return light signals received by the LiDAR sensor. The first light profile may be formed or extracted from the point cloud data, from the electrical signals converted from the first return light signals, from the first return light signals directly, and/or derived from the above-described signals. For instance, the first light profile may include signal intensity data in the form of digital signals (sampled from the electrical signals converted from the first return light signals). The first light profile may also include signal distributions, which can be histograms derived from the signal intensity data.

Similarly, the controller can obtain (block1704) a second light profile representing characteristics of the second return light signals. As described above, the second return light signals are formed based on one or more light sources external to the HyDAR system. For example, the second return light signals may be formed by light reflected, scattered, or refracted by one or more objects in the FOV, and are detected by the image sensor (e.g., sensor1204). The characteristics of the second return light signals may include, for example, signal intensity, wavelength of the second return light signals, signal distribution of the second return light signals, etc. The characteristics of the second return light signals are characteristics associated with the return light signals received by the image sensor (e.g., a camera). The second light profile may be formed or extracted from the image data, from the electrical signals converted from the second return light signals, from the second return light signals directly, and/or derived from the above-described signals. For instance, the second light profile may include signal intensity data in the form of digital signals (sampled from the electrical signals converted from the second return light signals). The second light profile may also include signal distributions, which can be histograms derived from the signal intensity data.

With reference toFIG.17A, in some embodiments, the controller can determine (block1706) whether at least one of the first light profile or the second light profile matches with an interference light profile associated with the interference signals provided by the one or more interference light sources. If there is a match, it likely means that the first return light signals and/or the second return light signals include interference light signals, which may affect the LiDAR sensor and/or the image sensor. In turn, it may degrade the HyDAR system's performance. The interference light profiles may be predetermined for one or more known interference light sources. They may be formed or extracted from the point cloud data or the image data obtained based on detections of known interference light sources, from the electrical signals converted from predetermined interference light signals from known interference light sources, from the predetermined interference light signals directly, and/or derived from the above-described signals. For instance, an interference light profile may include signal intensity data in the form of digital signals (sampled from the electrical signals converted from the predetermined interference light signals). The interference light profile may also include signal distributions, which can be histograms derived from the signal intensity data. Several examples of interference light profile are described below.

In some embodiments, for matching between the first/second light profile with the interference light profile, the controller compares data represented by these light profiles. Such data may include a measured direction along which the first return light signals and/or the second return light signals are detected by the HyDAR system; an absolute intensity detected along the measured direction; a relative intensity to different spectral ranges detected along the measured direction; a detectable size or shape of interference signals associated with the at least one of the one or more interference light sources; and a distribution of distances over multiple data collection cycles.

As one example shown inFIG.17C, at any given time of a day, the HyDAR system mounted to a vehicle1751may determine the direction of the Sun (or Moon). Such directions of the Sun (or Moon) can be data included in an interference light profile. Therefore, when the controller obtains the first light profile (based on first return light signals obtained by the LiDAR sensor) and/or the second light profile (based on second return light signals obtained by image sensor), the controller can obtain a measured direction along which the first light profile and/or the second return light signals are detected by the HyDAR system. The controller can thus compare the extracted measured direction with the direction of the Sun (or Moon) included in the interference light profile. If there is a match, it likely means that the first return light signals and/or the second return light signals obtained at the particular direction correspond to interference light signals from the Sun (or the Moon). In some examples, the controller may obtain further information to enhance the confidence of such a determination. For example, the in the direction of the first return light signals and/or the second return light signals, there may be a streetlight or some other light sources (e.g., light reflected from a mirror-surface of a tall building), in addition to the Sun (or the Moon). In this case, the controller may compare the data between the first/second light profiles with the interference light profile for several data collection cycles, may compare wavelengths contained in the first/second light profiles and the interference light profile, and/or may compare absolute light intensities, detected along the measured direction, of the first/second return light signals with the predetermined interference light signals. For instance, if the interference light source is a streetlight, its light intensity may be much smaller than the sunlight, but may be much greater than the moonlight. The wavelength of a streetlight may also be different from that of the sunlight or the moonlight. By comparing multiple types of data included in the first/second light profiles and the interference light profile, the controller can determine if the first/second return light signals contain interference light signals with more confidence.

As another example, based on the first light profile (corresponding to the first return light signals obtained by the LiDAR sensor) and/or the second light profile (corresponding to the second return light signals obtained by image sensor), the controller can extract a relative intensity of different spectral ranges detected along the measured direction for the first light profile and/or the second return light signals. As described above, in a measured direction, there might be object-reflected/scattered light signals that are desired for detection. In the same direction, there might also be other interference lights signals like the streetlight, the sunlight, the moonlight, building reflected light, etc. Therefore, one way to distinguish between desired return light signals and undesired interference light signals is to analyze the relative intensity of the spectral ranges of the light signals. For instance, desired return light signals may have the same wavelength (e.g., 905 nm) as the transmission light signals from the laser light source of the HyDAR system. Therefore, based on the first/second light profiles, the controller of the HyDAR system can analyze the signal intensity of particular return light signals at the particular wavelength (e.g., 905 nm) and at other wavelengths. If the signal intensity of the particular light signals at the particular wavelength falls within a predetermined range with respect to the intensity of other return light signals at the other wavelengths, the controller may determine the particular return light signals are desired return light signals. Otherwise, the controller may determine that they are interference light signals. In other examples, the controller may compare the relative signal intensity of the particular return light signals with predetermined relative signal intensity of known interference light signals. If there is a match, the controller determines that the particular return light signals are interference light signals.

As another example, an object in the FOV of a HyDAR system may reflect visible light from light sources external to the HyDAR system. The reflected visible light may have a normal signal intensity range, depending on the object's reflectivity. Most objects in a HyDAR system's FOV may not have a high reflectivity like a mirror. In some scenarios, there may be buildings having mirror-like surfaces and therefore the signal intensity of the reflected light may have a very high intensity in the visible wavelength range. Such a very high intensity may saturate the image sensor of the HyDAR system and thus, such reflected light signals are interference light signals undesired for generating good image data. The controller may determine that the signal intensity of particular return light signals at the visible light range is higher than a threshold intensity, and determine that the particular return light signals are interference light signals. In other examples, the controller may compare the relative signal intensity of the particular return light signals with predetermined relative signal intensity of known interference light signals. If there is a match, the controller determines that the particular return light signals are interference light signals.

In some examples, based on the first light profile (corresponding to the first return light signals obtained by the LiDAR sensor) and/or the second light profile (corresponding to the second return light signals obtained by image sensor), the controller can detect a size and shape associated with particular return light signals. The controller can compare the detectable size or shape of the particular return light signals with the size and shape of interference signals associated with one or more known interference light sources. As one example, the image sensor of the HyDAR system may receive particular return light signals from the Sun. The particular return light signals, together with other return light signals, may form the second return light signals detected by the image sensor. The controller can obtain a second light profile representing characteristics of the second return light signals. Based on these characteristics (e.g., wavelength, signal intensity, distribution, etc. as described above), the controller can detect a size and shape of possible interference light sources. For example, if a particular part of the second return light signals all have a high signal intensity, a particular wavelength spectrum, and are located at certain direction in the sky, the controller may determine the size and shape of the light source that generated the particular part of the second return light signals. The controller may then compare the detected size and/or shape of the light source with predetermined size and shape of a known interference light source. If there is a match, the controller determines that the particular part of the second return light signals corresponds to interference light signals from a known interference light source (e.g., the Sun).

In some examples, based on the first light profile (corresponding to the first return light signals obtained by the LiDAR sensor) and/or the second light profile (corresponding to the second return light signals obtained by image sensor), the controller can obtain a distribution of distances over multiple data collection cycles. The distribution of distances over multiple data collection cycles can be used for many scenarios where the interference source is not completely in synchronization with the laser emission of the HyDAR system. One example of such an interference source can be sunlight. The sunlight that enters the HyDAR system includes randomly allocated pulses in time. These pulses could cause detections at almost any distance along the direction of the Sun. The distance distribution of the sunlight may thus cover a huge range. The controller of the HyDAR system can therefore use the distance distribution to detect the interference sources, including the Sun and other LiDAR/HyDAR devices.

The example interference light sources described above include one or more of the Sun, vehicle headlights/taillight, street illumination, and a light redistribution mechanism redirecting light from another interference source (e.g., a building having a mirror-like surface or another high reflectivity object). In some examples, the external interference light source may be another HyDAR or LiDAR system that transmits laser light.FIG.17Dillustrates two vehicles1772and1776travelling in opposite directions of a road. The vehicle1772is mounted with a HyDAR or LiDAR system that continuously transmits laser light signals to scan the environment. The transmission laser light signals from vehicle1772may be received by a HyDAR or LiDAR system mounted to vehicle1776, which is travelling in the opposite direction. Thus, the laser light signals from vehicle1772can be interference light signals for a HyDAR or LiDAR system mounted to vehicle1776, and vice versa. Such interference light signals from vehicle1772can be detected by the HyDAR or LiDAR system mounted to vehicle1776based on, for example, a measured direction along with the light signals are detected (e.g., the direction of the light signals is always from the road lane in the opposite direction), a distribution of distances over multiple data collection cycles (e.g., because the light signals emitted from vehicle1772do not correspond to any of the transmission light signals from vehicle1776, the distances may be super large), wavelengths (e.g., two vehicles may have laser sources having different wavelengths), or other data included in a first/second light profile (corresponding to the first return light signals detected by a LiDAR sensor and second return light signals detected by an image sensor).

It is understood that the data included in the first light profile and/or second light profile are not limited to the above-described examples. Other types of data can be extracted or derived from the first/second light profiles and compared to interference light profiles from known interference light sources. By comparing the light profiles generated based on the return light detected by one or both of the LiDAR sensor and the image sensor, the HyDAR system can improve its performance of detecting interference light signals, compared to if only one sensor is used.

With reference back toFIG.17A, in block1706, if the controller determines that at least one of the first light profile or the second light profile matches with the interference light profile (i.e., “yes”), the controller can proceed to cause (block1708) adjustment of at least one of a laser power, a noise filter, or the one or more steering mechanism to reduce or prevent a HyDAR system misdetection.FIG.17Aprovides several non-limiting examples of adjustments, including adjusting (block1710) steering mechanisms to tune at least one of a start location or an end location of a FOV of the HyDAR system to avoid a location of the one or more interference light sources, adjusting (block1712) steering mechanisms to tune a duration of a data collection cycle to avoid a location of the one or more interference light sources; adjusting (block1714) the laser power to a ratio of the first return light signals to the interference light signals to be no less than a signal-to-noise ratio (SNR) threshold; turning off (block1716) the laser power to avoid directing the laser light signals to a location of the one or more interference light sources; adjusting (block1718) the controller to enhance the noise filter to remove data representing the at least a part of the interference signals provided by the one or more interference light sources; and (block1719) adjusting the controller to discard or ignore data representing the at least a part of the interference light signals provided by the one or more interference light sources. Each of the blocks1710-1719are described in greater detail below.

As a first example for adjustment, based on detection of the interference light signals, the controller adjusts (block1710) steering mechanisms to tune at least one of a start location or an end location of a FOV of the HyDAR system to avoid a location of the one or more interference light sources. One such example is illustrated inFIG.17C, which is a diagram illustrating an example process for adjusting a steering mechanism of the HyDAR system to avoid a location of an interference light source like the Sun, according to various embodiments. With reference toFIGS.17C and12A, a HyDAR system1750may be mounted to a vehicle1751(e.g., at the vehicle roof). HyDAR system1750may be substantially the same or similar to HyDAR system1200described above. HyDAR system1750may scan, using a steering mechanism (e.g., steering mechanism1206), an FOV1752in its normal setting. The normal setting may be, for example, a default setting under vehicle's normal operating conditions. As shown inFIG.17C, if vehicle1751is operating during a time period in a late afternoon, the HyDAR system1750may receive sunlight of the Sun1754at a low Sun angle (i.e., the Sun is close to the horizon, such that the angle between the direction of sunlight and the road surface may be very small. In this scenario, the Sun appears in the FOV of the HyDAR system and the sunlight directly enters the detector. As a result, much more sunlight may enter the HyDAR system1750directly (compared to indirectly when a large portion of the visible light received by the HyDAR system1750is only reflected/scattered sunlight). Therefore, the sunlight received by the HyDAR system1750may have a very high intensity. As a result, the sunlight from the Sun1754at this time of the day may affect (e.g., saturate) the image sensor and/or the LiDAR sensor of the HyDAR system1750, and therefore it is desired to avoid the sunlight as interference light signals.

In one embodiment, as shown inFIG.17C, to avoid the interference light source like the Sun1754at a particular time period (e.g., close to sunset time), the controller of HyDAR system1750may adjust at least one of the one or more steering mechanisms to tune at least one of a start location or an end location of a field-of-view of the HyDAR system1750to avoid a location of the interference light source like the Sun1754.FIG.17Cthus illustrates, after tuning the start and end locations of the FOV, the HyDAR system1750scans a new FOV1756, which is different from the FOV1752under the normal setting. The new FOV1756does not overlap with the direction of the Sun1754, and therefore, the LiDAR sensor and/or the image sensor of HyDAR system1750can avoid receiving the direct sunlight or reduce the received direct sunlight. Accordingly, tuning the FOV to avoid the interference light source can significantly reduce the noise or interference light signals received by the HyDAR system1750and thus reduce or prevent the HyDAR system from misdetection.

In some examples, the interference light source of a current HyDAR/LiDAR system may be from another HyDAR or LiDAR system that transmits laser signals, which can be referred to as the interference HyDAR/LiDAR system (e.g., a HyDAR system mounted to vehicle1776is an interference to the HyDAR system mounted to vehicle1772shown inFIG.17D). The interference HyDAR/LiDAR system may transmit laser signals, based on which the return light signals are formed. Such return light signals may be interference light signals with respect to the current HyDAR/LiDAR system. In some examples, if the current HyDAR/LiDAR system and the interference HyDAR/LiDAR system use different light signal wavelengths, the interference light signals from the interference HyDAR/LiDAR system can be distinguished from the return light signals for the current HyDAR/LiDAR system by wavelength. However, in some examples, the HyDAR/LiDAR systems interfering with each other may have the same light signal wavelengths. Thus, the current HyDAR/LiDAR system may need to adjust the steering mechanism (e.g., mechanism1206) to tune a duration of the data collection cycle. As described above, a data collection cycle corresponds to the time for collecting a frame of data (e.g., point cloud data). A frame of data may be obtained when the steering mechanism scans the entire FOV once. Thus, by adjusting the steering mechanism, the data collection cycle can be adjusted. If the data collection cycle of the current HyDAR/LiDAR system is different from the data collection cycle of the interference HyDAR/LiDAR system, the current HyDAR system can avoid the location of the interference source or reduce the likelihood of receiving the interference signals at the current HyDAR/LiDAR system. In one example, the adjustment of the data collection cycle changes the dimensions of the frame (e.g., a frame length). Therefore, the data collection rate and/or the return light directions may be different between the current HyDAR/LiDAR system and the interference HyDAR/LiDAR system (e.g., usually, two systems may not be transmitted light pulses from the same angle or detecting return light pulses at the same rate). As a result, by adjusting the data collection cycle, the possible synchronization between two HyDAR/LiDAR systems is reduced. As a result, the current HyDAR/LiDAR system can reduce the likelihood of receiving the interference signals generated by the interference HyDAR/LiDAR system (e.g., the systems are out of synchronization).

Another way to reduce or eliminate false detection by a current HyDAR or LiDAR system due to interference light pulses is to adjust laser power of the light source (light source310) in the HyDAR system (e.g., system1200) or the LiDAR system (e.g., system300). Adjusting the FOV of the current HyDAR/LiDAR system as described above may be combined with adjusting the laser power, as illustrated inFIG.17E.FIG.17Eare diagrams illustrating an example scenario of adjusting a current HyDAR/LiDAR system to avoid interference light signals and/or to adjust the laser power of the HyDAR system to reduce the impact of the interference light signals, according to various embodiments.FIG.17Euses HyDAR systems as examples, but the process may be applied to a LiDAR system too.FIG.17Ehas four diagrams, named as diagrams1-4. The process shown by diagrams1-4illustrates one example order in which the adjustment of FOV and adjustment of laser power can be applied. It is understood that the order can be changed.

The first diagram (i.e., diagram #1) ofFIG.17Eshows that in one scenario, there may be multiple HyDAR systems (e.g., systems1780A,1780B, and1780C). Each of the HyDAR systems1780A-1780C transmits laser light signals to scan a respective FOV. For instance, HyDAR systems1780A,1780B, and1780C scan FOVs1781A,1781B, and1781C, respectively. In this scenario shown in the first diagram ofFIG.17E, FOVs1781B and1781C at least partially overlap with the FOV1781A. Therefore, with respect to the current HyDAR1780A, HyDAR systems1780B and1780C are interference light sources emitting interference light signals. The interference light signals emitted by HyDAR systems1780B and1780C may be detected by the methods described above (e.g., comparing light profiles of the return light signals received by the LiDAR sensor and/or the image sensor with an interference light profile of a known interference light source). In another example, the HyDAR system1780A may detect the interference light signals emitted by interference HyDAR systems1780B and1780C by using the image data provided by the image sensor (e.g., the image sensor of HyDAR system1780A may capture images of HyDAR system1780B and1780C (and/or the vehicles to which they are mounted), and the controller may compare the images with known HyDAR/LiDAR systems to determine if they are interference HyDAR systems).

The second diagram (i.e., diagram #2) ofFIG.17Eillustrates that, to reduce the impact of the interference light signals generated by the interference HyDAR systems1780B and1780C, the controller of the current HyDAR system1780A may cause the laser power of the laser source to adjust. For instance, the controller of HyDAR system1780A may significantly increase the laser power of its laser light source, such that the signal intensity of the first return light signals is also increased. In turn, when the LiDAR sensor of HyDAR system1780A receives the first return light signals, the signal-to-noise (SNR) ratio is increased. This is because the interference light signals remain unchanged. Thus, with respect to the interference light signals emitted by interference HyDAR systems1784B and1784C, if the signal intensity (or signal power) of the first return light signals increases, the ratio of the signal intensity of the first return light signals to the signal intensity of the interference light signals increases. This ratio is referred to as the signal-to-noise ratio (SNR) associated with the HyDAR system1780A. The controller of HyDAR system1780A can increase the laser power to a level that the SNR associated with HyDAR system1780A is no less than a SNR threshold. When the laser power of the light source increases, the detection range of the HyDAR system1780A also increases.

Thus, comparing the first and second diagrams ofFIG.17E, the FOV of HyDAR system1780A changes from FOV1781A to1781A′. FOV1781A′ is bigger than FOV1781A and the transmission laser signals from HyDAR1780A can travel farther distances. On the detection side, with the improved SNR, the performance degradation of the LiDAR sensor and/or the image sensor of HyDAR system1780A due to the interference light signals emitted by the interference HyDAR systems1780B and1780C can be reduced or prevented. In turn, it reduces or prevents false detection by the HyDAR system1780A.

The third diagram (i.e., diagram #3) ofFIG.17Eillustrates that in addition to adjusting the laser power, the controller of the current HyDAR system1780A may cause the steering mechanism to adjust the FOV to further reduce the impact of the interference light signals generated by the interference HyDAR systems1780B and1780C. As described above, when the current HyDAR system1780A detects the interference light signals from interference HyDAR systems1780B and1780C, the controller of HyDAR system1780A increases the laser power to increase the SNR of the return light signals to the interference light signals to improve the detection. Sometimes, simply increase the laser power may not be sufficient to increase the SNR to a desired level. In addition, increasing the laser power too much may have safety concerns too. Therefore, in some examples, the controller of the current HyDAR system1780A can cause the steering mechanism to adjust the FOV to avoid at least some of interference light sources. As described above, after increasing the laser power, the FOV1781A of HyDAR system1780A changes to FOV1781A′. Nonetheless, FOV1781A′ of HyDAR system1780A may still overlap significantly with FOV1781C of interference HyDAR system1780C and overlap partially with FOV1781B of interference HyDAR system1780B. As such, the LiDAR sensor and/or the image sensor of HyDAR system1780A still receives significant interference light signals from HyDAR system1780C and some interference light signals from HyDAR system1780B.

To further reduce the interference light signals received by the HyDAR system1780A, the controller of system1780A can control the steering mechanism (e.g., steering mechanism1206) to turn the FOV range, as described above. For instance, as shown in the third diagram ofFIG.17E, the steering mechanism can be adjusted such that the HyDAR system1780A changes its scanning range to cover FOV1781A″. Compare to the FOV1781A′, FOV1781A″ only partially overlaps with the FOV1781C from the interference HyDAR system1780C and docs not overlap with the FOV1781B from the interference HyDAR system1780B. As a result, the LiDAR sensor and/or the image sensor of the current HyDAR system1780A receive less interference light signals from interference HyDAR system1780C and receives no or minimum interference light signals from interference HyDAR system1780B. In turn, because the signal intensity of the interference light signals reduces, the SNR of the detected return light signals at the HyDAR system1780A further improves. A further improved SNR can better prevent false detection by the HyDAR system1780A.

In some scenarios, the interference HyDAR systems1780B and/or1780C may be located very close to the current HyDAR system1780A. As a result, the interference light signals may have a very high intensity. Increasing the laser power (diagram #2ofFIG.17E) may not be sufficient to obtain a sufficiently large SNR ratio. Further, because the interference HyDAR systems1780B and/or1780C are located very close, adjusting the steering mechanism may not be able to avoid the significant overlapping of the FOVs between the current HyDAR system1780A and the interference HyDAR systems1780B and/or1780C. In some other scenarios, the interference light signals may be generated by HyDAR system1780A itself (e.g., if the system1780A has a blockage of the aperture window, or if some internal components of the system1780A has a high signal reflection, or if system1780A has a degraded intrinsic calibration). In these scenarios, the controller of HyDAR system1780A may reduce the laser power or even turn off the laser power as illustrated in the fourth diagram (i.e., diagram #4) ofFIG.17E. Turning off the laser power can avoid directing laser light signals to a location of the interference light sources. In some cases, the controller may report to adjustments it made to a user or to another system (e.g., a vehicle planning and perception system). Based on the report, the user or the other system may determine that decisions (e.g., vehicle planning or perception decisions) should not be made while the laser power is turned off at HyDAR system1780A.

With reference back toFIG.17D, it is a diagram illustrating another example process for making adjustment based on detected interference light signals.FIG.17Dshows adjusting the controller to discard or ignore data representing the at least a part of the interference signals provided by the one or more interference light sources, according to various embodiments. With reference toFIG.17D, in one example, the HyDAR system mounted to vehicle1776may receive interference light signals1775from vehicle1772. The interference light signals1775may be a high beam of the headlight of vehicle1772, or may be transmission laser signals from a LiDAR or HyDAR system mounted to vehicle1772. Vehicle1772may send other light signals1774(light beams from another headlight), which may not be received by the HyDAR system mounted to vehicle1776, and are therefore not interference light signals.

As shown inFIG.17D, in some embodiments, the controller of the current HyDAR system (e.g., the one mounted to vehicle1776) can adjust to discard or ignore the data representing the at least a part of the interference light signals1775provided by the interference light sources (e.g., the high beam from vehicle1772).FIG.17Dillustrates scanlines1778generated by the controller of the current HyDAR system, and data corresponding to area1779in the scanlines1778may be identified to have too much noise due to the interference light signals1775. The controller can thus discard or ignore such data corresponding to the area1779. In turn, because such data are not used for object detection, the current HyDAR system's performance degradation can be reduced or prevented.

While not explicitly shown inFIG.17D, in some embodiments, a controller of the current HyDAR system may apply or enhance a noise filter to remove data representing the at least a part of the interference light signals provided by the interference light sources. For example, a noise filter can be applied to discard all data points along a particular direction corresponding to the location of the interference light sources; to discard all data points that have signal intensities satisfying certain threshold (either high or low threshold); and/or discard data points based on signal distribution and inter-spacing between the data points. The noise filter may be implemented by hardware and/or software. The noise filter can be applied before discarding or ignoring data completely. In some examples, a noise filter having a first level of filtering can be applied to all data points in the point cloud data and/or the image data obtained by the current HyDAR system; while an enhanced noise filter having a second level of filtering can be applied to a part of the data points that correspond to interference light signals from particular interference light sources. Referring toFIG.17D, for example, a default level of noise filtering may be applied to all data points corresponding to the scanlines1778, while an enhanced level of noise filtering may be applied to the data point corresponding to area1779.

FIGS.17A and17C-17Eillustrate various methods of detecting interference light signals as a degradation factor for a HyDAR system and making adjustments to reduce or prevent HyDAR misdetection.FIG.17Bshows an example implementation of the methods described above for detecting interference light signals and causing adjustments to reduce or prevent misdetection. Specifically, in an implementation of a method1730, the controller (e.g., controller1214or control circuitry350) starts (block1731) by obtaining a frame of data (e.g., a frame of point cloud data generated by the LiDAR sensor and/or a frame of image data generated by the image sensor). The controller can extract a first light profile (block1732) and a second light profile (block1734) from the frame of data. As described above, the first light profile may represent characteristics of the first return light signals (e.g., intensity, wavelength, distribution, etc. of the return signals detected by the LiDAR sensor); and the second light profile may represent characteristics of the second return light signals (e.g., intensity, wavelength, distribution, etc. of the return signals detected by the image sensor).

In block1736of the process1730, the controller has a profile comparator configured to compare the first light profile and/or the second light profile with one or more profiles of known interference signals (block1735). The profiles of known interference signals are stored in a storage device of the HyDAR system or stored somewhere else (e.g., in a cloud storage). The profile comparator can be implemented using hardware and/or software, for more efficient and faster comparison. In some cases, the comparison and other steps in method1730are performed in real time such that the results of method1730(e.g., determining the interference light signals and making adjustments accordingly) can be delivered quickly for real-time operation of a vehicle.

In block1737, the controller determines if the first/second light profiles match with the profiles of known interference signals. If yes, the controller can cause the execution of mitigation plans to reduce or prevent misdetection caused by the interference light signals. As described above, the controller may cause the adjustments of one or more of the steering mechanisms, a noise filter, laser power, etc. to reduce or prevent the misdetection caused by the interference light signals. After the adjustment and if there is no match from block1737, the controller can proceed to obtain (block1738) the next frame of data.

Window blockage and interference light signals are two factors that may degrade the performance of a HyDAR system. Another degradation factor is extrinsic calibration degradation.FIG.18is a block diagram illustrating a moveable platform1800mounted with a HyDAR system1802and various sensors, according to various embodiments. The moveable platform1800can be a vehicle, a robot, etc. HyDAR system1802can be substantially the same or similar to systems400or1200described above. Sensors1804,1806, and1808may include, for example, cameras, ultrasonic sensors, radars, etc. or a combination thereof. There may be other components mounted to the moveable platform1800and are not shown. When the HyDAR system1802is manufactured and mounted to moveable platform1800, its position and orientation is calibrated such that it can correctly operate to detect the objects in the desired FOV around the moveable platform1800. For example, the HyDAR system1802's roll, pitch, yaw are set and calibrated to the desired values for the system to operate in a desired manner. This type of calibration is referred to as the extrinsic calibration of the HyDAR system1802, because the calibration is with respect to the moveable platform1800(or its components, other sensors mounted thereto, etc.) located external to the HyDAR system1802.

After the moveable platform1800operates for some time, the HyDAR system1802may change its position and orientation relative to the vehicle due to, for example, vibration, shock, humidity, temperature, or other environmental, operational, or user related factors. As shown inFIG.18, HyDAR system1802may change its position and orientation (e.g., roll, pitch, and yaw) with respect to its original position (and therefore also with respect to the moveable platform1800). In some examples, over time, one or more components in the HyDAR system1802may change their positions and orientations with respect to the moveable platform1800, sensors1804,1806, and/or1808; and/or other components mounted to the moveable platform1800. Accordingly, there is a need to detect the extrinsic calibration degradation and cause adjustments to compensate for the degradation.

If a LiDAR system or a camera are discrete sensors (compared to integrated multimodal sensor in a HyDAR system) mounted to a vehicle, extrinsic calibration of the discrete LiDAR system or the camera can be difficult or cumbersome, because it requires accurate synchronization between the sensors and the specific calibration setup. By using the HyDAR system described herein, which includes a multimodal sensor integrating the LiDAR sensor and the image sensor together, the extrinsic calibration can be performed or monitored with no additional synchronization required. As described above, in a HyDAR system described herein, the point cloud data provided by the LiDAR sensor and the image data provided by the image sensor are time-and-space synchronized at the hardware level. Therefore, no additional computational efforts are required for synchronization above the hardware level. Furthermore, if the extrinsic degradation is within a predetermined threshold, the HyDAR system can adjust itself during operation. Thus, it minimizes the impact on the vehicle's operation (e.g., the vehicle can keep operating with the HyDAR system continuously providing detection results).

FIGS.19A-19Bare flowcharts illustrating methods for detecting extrinsic calibration degradation of a HyDAR system (e.g., system1200or1802) mounted to a moveable platform (e.g., platform1800), according to various embodiments.FIGS.19C-19Gare diagrams illustrating an example method of detecting extrinsic calibration degradation of a HyDAR system (e.g., system1200or1802) using parallel line features extending along a road surface, according to various embodiments. The HyDAR system includes a LiDAR sensor and an image sensor integrated together to form a multimodal sensor, as described above. The flowchart ofFIG.19Ais described first with the examples shown inFIGS.19C-19H.FIG.19Ashows a method1900, which corresponds to the block1350inFIG.13. With reference toFIG.19A, in some embodiments, the controller begins the method1900by obtaining the point cloud data generated by the LiDAR sensor and the image data generated by the image sensor. In block1902, the point cloud data representing the first return light signals and image data representing the second return light signals are combined to obtain a combined dataset. A controller can perform the combing of the point cloud data and the image data. Because the point cloud data and the image data are synchronized in time and space at the hardware level, there is no extra synchronization required.FIG.19Cillustrates an example image1930representing the combined point cloud data and the image data. Image1930may also represent just the point cloud data or just the image data.

In block1904, the controller segments a space-of-interest based on the combined dataset. Comparing image1930inFIG.19Cand image1931inFIG.19D, the controller extracts the space-of-interest1933and removed (e.g., filtered out) other features that are not of interest. In this example, the space-of-interest1933corresponds to the features of a road surface on which a vehicle mounted with the HyDAR system operates. Other features in image1930are removed or filtered out. Such features may include, for example, other vehicles operating on the road surface, the sky, trees, and other roadside objects. Extracting the features from the combined dataset to segment the space of interest can be performed using, for example, a machine-learning based algorithm and/or other pattern recognition algorithms. For example, the image data in the combined dataset may include color information and shape information of the road surface (e.g., a rough triangular shape as shown inFIG.19Cif the road is straight, and curved if the road is turning). The point cloud data in the combined dataset may include distance and height information of the objects in image1930. For instance, in image1930, any objects that has a height less than a threshold (e.g., 0.1 m) can be considered the road surface. Based on these types of information contained in the combined dataset, the controller can accurately identify the road surface and segment the road surface (i.e., the space-of-interest in this example) from the other features (e.g., trees, vehicles, sky, etc.). In some examples, the controller may also use other data to determine the operating conditions before performing the segmentation to obtain a space of interest. For instance, based on the GPS data and/or the combined dataset, the controller may determine that the vehicle is moving forward; that there is no apparent turning; that the vehicle speed is within a certain threshold; etc. When all these operating conditions are satisfied, the controller segments the space-of-interest1933(e.g., road surface) based on the combined dataset.

Next, with reference back toFIG.19A, in block1906, the controller detects parallel line features in the space-of-interest1933based on the combined dataset. This is illustrated inFIG.19E, where parallel line features1932are identified from the space-of-interest1933. The parallel line features1932can correspond to the lane lines on a road surface. The lane lines can be identified using the image data in the combined dataset. For example, the lane lines may have a higher signal intensity (e.g., because they include reflection materials) and are thus distinguishable from other features of the road surface. The parallel line features1932may also correspond to other objects like curbs, isolating islands, buildings, or any other objects disposed along the road surface. In one example, a Hough Line transformation can be performed to extract the parallel line features1932. For more accurate extraction, freeway lane lines may be better because they are straight, and their lane curvature is more stable or slow changing.

With reference back toFIG.19A, in block1908, the controller identifies an intersection position of the detected parallel line features1932. This is illustrated inFIG.19F. For example, the parallel line features1932can be extrapolated to find the intersection position1934at the horizon1937. The controller can identify the intersection position1934by its coordinates (x, y, z).FIG.19His a diagram illustrating another example where the lane features are curved (compared to straight lines inFIG.19F). In this case, the controller may identify a set of intersection positions1952A-1952C) based on the curved line features. The set of intersection positions1952A-1952C can be identified by their coordinates (e.g., A (x1, y1, z1); B (x2, y2, z2); and C (x3, y3, z3)). For example, based on the curvatures of lanes at different locations, the controller can identify a set of intersection points1952A-1952C.

With reference back toFIG.19A, the process including blocks1902,1904,1906, and1908can be repeated multiple times to obtain multiple intersection positions. This is illustrated inFIG.19G, where multiple intersection positions1934A-1934N at the horizon1937A-1937N at different time points are calculated. In one example, each combined dataset corresponds to one frame of point cloud data and image data. Each combined dataset can be processed by the controller to obtain an intersection position. Thus, multiple combined datasets1935can be used to obtain multiple intersection positions1934A-1934N.

With reference back toFIG.19A, based on the multiple intersection positions, the controller can estimate if the relation between the HyDAR system (e.g., system1200or1802) and the moveable platform (e.g., platform1800) to which the HyDAR system is mounted has shifted from an original configuration. For example, the controller can compare the multiple intersection positions with one or more corresponding stored extrinsic positions of the HyDAR system. The stored extrinsic positions may be the calibration results obtained at the time the HyDAR system was mounted to the moveable platform at a factory and thus may be factory-calibrated positions (also referred to as ground truth). The stored extrinsic positions may also be calibration results obtained at any other previous time points (e.g., during a later vehicle maintenance service).

In some examples, because a combined dataset includes both the point cloud data generated by the LiDAR sensor and the image data generated by the image data, the controller can calculate a first intersection position using the point cloud data and calculate a second intersection position using the image data. The controller can similarly acquire multiple first intersection positions using multiple frames of point cloud data and acquire multiple second intersection positions using multiple frames of image data. For detecting intrinsic calibration degradation, the controller can compare the multiple first intersection positions with the multiple second intersection positions. This comparison using data from difference sensors (e.g., the LiDAR sensor and the image sensor) of the HyDAR system may be necessary if the factory calibrated positions (ground truth) are not available. For instance, as described above in connection withFIG.19H, if the lane features are curved, the controller may calculate multiple intersection positions1952A-1952C. Because the curvature of the road may not be precisely estimated at the time the HyDAR system is manufactured or mounted to the vehicle, there may not be ground truth data. In this situation, the controller cannot compare intersection points with ground truth data. The controller may perform the comparison between the multiple first intersection positions calculated using the point cloud data with the multiple second intersection positions calculated using the image data. In some other examples, this comparison can also be performed as an additional check even if the factory calibrated positions (ground truth) are available. In other examples, the calculation of the intersection positions may be compared with calculations based on a high definition map. The calculation of the intersection positions may also be refined by using an IMU sensor to improve accuracy. For example, the HD map and/or the IMU sensor may provide additional information about the slope of the road surface, which may be used to improve the calculation accuracy.

The comparison results can be used to determine if the relation between the HyDAR system (e.g., system1200or1802) and the moveable platform (e.g., platform1800) to which the HyDAR system is mounted has shifted from an original configuration. If the comparison results are within a predetermine threshold, the controller can determine that the HyDAR system has not shifted and the extrinsic calibration has not degraded (or not degraded beyond a threshold). If the comparison results are greater than a predetermined threshold, the controller can determine that the extrinsic calibration has degraded beyond the threshold. As such, the HyDAR system may need to adjust or recalibrated.

FIG.19Billustrates a particular example method1920of detecting the extrinsic calibration degradation for a HyDAR system mounted to a vehicle. Method1920can correspond to block1350shown inFIG.13. In block1921, the controller of the HyDAR system may detect that the vehicle is traveling through a long straight road section with a speed more than a threshold speed (e.g., 10 m/s). In block1922, the controller of the HyDAR system may obtain a frame of the HyDAR data, which includes a combined dataset. The combined dataset has point cloud data generated by a LiDAR sensor and image data generated by an image sensor. In block1923, based on the frame of the HyDAR data that includes the combined dataset, the controller segments the data with combined 2D and 3D information. As described above, the point cloud data may include 3D information like the horizontal and vertical coordinates and distance information. The image data may include 2D information. The image data may have color information and are higher resolution. The controller can segment a space-of-interest using the combined data, as described above.FIG.9Bfurther provides some examples of segmentations that can be performed by the controller of the HyDAR system. In one example, the controller may perform the segmentation to obtain a space-of-interest based on a set of criteria including (1) the vertical coordinates needs to be 1.5 m lower than the sensor; (2) the elevation angle is less than the azimuth angle*0.5; (3) the elevation angle is less than the azimuth angle*(−0.5); and (4) the color is yellow or white. In another example, the controller may perform the segmentation to obtain a space-of-interest based on a set of criteria including (1) the 3D coordinates need to be within 0.1 meter from a plane Ax+By+Cz+d=0; (2) the elevation angle and azimuth angle needs to satisfy condition K*elevation+M*azimuth+n>0; and (3) the color is yellow or white. In the above equations, A, B, C, d, K, M, and n are configurable constant.

In block1924, the controller identifies the line features that are parallel within the segmented space-of-interest. The line features may correspond to lane lines on a freeway, for example. In bock1925, the controller computes the position of the intersection point (also referred to as the vanish point). In block1926, the controller accumulates the positions of the intersection points, also referred to as the vanishing points, (e.g., by repeating blocks1922-1925) to create a distribution. In block1927, the controller determines if the distribution of the positions of the vanishing points are expected. This may be performed by comparing the vanish points' positions distribution with a distribution obtained by factory calibration (ground truth). If the controller determines the distribution is as expected (e.g., it is within a threshold from the ground truth), it reports (block1929) that the extrinsic parameters are consistent. That is, the extrinsic calibration did not degrade (or degraded slightly but still within a tolerance range). Otherwise, the controller reports (block1928) that the extrinsic calibration has drifted from the expected values).

The above-described methods1900and1920for detecting extrinsic calibration degradation can be performed during vehicle operation using line features of a road surface and are referred to as dynamic extrinsic calibration or simply dynamic calibration. In some other embodiments, the detection extrinsic calibration can be performed using one or more set of features of predefined stationary targets. These sets of features are also referred to as key features or key points.FIG.20Ais a flowchart illustrating another method2000for providing information related to key features of predefined stationary targets used for detecting the extrinsic calibration degradation obtained from point cloud data and image data, according to various embodiments. Method2000can be a part of block1350inFIG.13.

With reference toFIG.20A, in block2002, the controller identifies one or more sets of features associated with one or more predefined stationary targets, based on the point cloud data representing the first return light signals and the image data representing the second return light signals. Examples of the predefined stationary targets are shown inFIG.20B. For instance, stationary target2012may have an array of circles having a predefined pattern (e.g., two black circles in the middle with white circles around them as shown inFIG.20B). Another example stationary target2014may have a checkboard pattern. Another stationary target2016may have a pattern with alternating black and white blocks as shown inFIG.20B. It is understood that predefined stationary targets are not limited to the examples shown inFIG.20B. The key features (or key points) identified in these predefined stationary targets may include certain particular points, scale-invariable features, and/or features that are not affected by the viewing angle, the FOV size, or the like. These features can be used for detecting extrinsic calibration degradation with an improved accuracy.

With reference still toFIG.20A, the key features that are identified from the predefined stationary targets may have specific characteristics design for making accurate extrinsic calibrations. These specific characteristics may be related to the intensity or the environment conditions of the key features. When detecting the extrinsic calibration degradation, the HyDAR system is turned on to capture images of the predefined stationary targets using both the LiDAR sensor and the image sensor. The key features can thus be extracted from the point cloud data and the image data.

FIG.20Afurther illustrates that in block2004, the controller can provide one or more of the following information in accordance with the identified one or more sets of features. For example, the controller may provide the positions of the one or more sets of features in a three-dimensional (3D) space; the positions of the one or more sets of features in elevation and azimuth; the point cloud data representing the first return light signals at an area associated with the one or more sets of features; the image data representing the second return light signals at the one or more features; the gradient associated with the point cloud data representing the first return light signals at the one or more sets of features; the gradient associated with the image data representing the second return light signals at the one or more sets of features; the histogram of gradient (HOG) associated with the point cloud data representing the first return light signals at the one or more sets of features; and the histogram of gradient (HOG) associated with the image data representing second return light signals at the one or more sets of features. At least some of the above information are derived (e.g., calculated, transformed, determined, etc.) from the point cloud data and the image data captured by the HyDAR system with respect to the predefined stationary targets.

FIG.20Cis a flowchart illustrating a method2020of detecting extrinsic calibration degradation of a HyDAR system using sets of features based on image data and LiDAR point cloud data, according to various embodiments. Method2020corresponds to the block1350inFIG.13. With reference toFIG.20C, the controller obtains the image data and optionally the point cloud data from the HyDAR system. In block2022, the controller identifies a first set of features associated with predefined stationary targets based on at least the image data representing second return light signals. If the controller also obtains the point cloud data as a part of the combined dataset, the first set of features may also be identified based on both the image data and the point cloud data. The controller may provide various information (e.g., positions, gradient, histogram, etc.) in accordance with the first set of features as described above in connection withFIG.20A.

In block2024, the controller obtains a second set of features from a sensor external to the HyDAR system. The sensor may include one or more of a camera, an infrared camera, and a LiDAR or HyDAR system. This external sensor is not a part of the HyDAR system, for which the detection of extrinsic calibration is being performed. This external sensor may be mounted to the same moveable platform (e.g., a vehicle) as the HyDAR system. The first set of features and the second set of features may be obtained by using the same predefined stationary targets. The HyDAR system and the external sensor may be positioned to face the same predefined stationary targets. Similarly, based on the second set of features, the controller may provide various information (e.g., positions, gradient, histogram, etc.) in accordance with the second set of features similar to those described above in connection withFIG.20A.

Next, in block2026, the controller establishes correspondence between the first set of features and the second set of features. For example, the controller may identify the corresponding features/gradients/positions/histograms/data in both sets of features. In block2028, the controller calculates an affine transformation between the first and second sets of features. An affine transformation is a type of geometric transformation that preserves points, straight lines, and planes. It includes transformations such as translations, rotations, scaling (resizing), and shearing (skewing). In the affine transformation, for example, any straight line before the transformation remains a straight line after transformation; parallel lines remain parallel after transformation; and the ratio of distances between points on a straight line remains the same after transformation. The affine transformation can be represented by matrix multiplication and addition, where a coordinate (x, y) is transformed into a new coordinate (x′, y′) using a matrix and a translation vector. Thus, the first set of feature can be transformed to the second set of features using an affine transformation, and vice versa. Using the first set of features and the second set of features, the controller can calculate the affine transformation between them.

With reference still toFIG.20C, in block2029, the controller can estimate, based on the calculated transformation, if the relation between the HyDAR system and the moveable platform to which the HyDAR system is mounted has shifted from an original configuration. For example, the affine transformation between the first and second sets of features can be compared to a factory calibrated affine transformation (a ground truth). If they are different more than a threshold, it means the HyDAR system's extrinsic calibration has degraded (changed with respect to the external sensor).

In another example, in blocks2028-2029, the controller can calculate the relation between two coordination systems associated with the two sets of features. After a coordination system transformation, the first set of features become a first set of transformed features. If the first set of transformed features matches with the second set of features (e.g., within a threshold), then there is no shift of the positions of the HyDAR system. In turn, this means the HyDAR system's extrinsic calibration has not degraded. In other examples, both sets of features can also be transformed to a common coordinate system for comparison.

As described above, in one example, the detection of the extrinsic calibration degradation based on predefined stationary targets can use the image data only, because it has a higher resolution than the point cloud data. But the controller can also use a combined dataset including both the point cloud data and the image data to identify the key features. Also as described above, the method2020requires using an external sensor to determine if the HyDAR system's extrinsic calibration has degraded. While the HyDAR system includes a LiDAR sensor and an image sensor, they are integrated together. Therefore, the internal image sensor or the LiDAR sensor cannot be used to perform extrinsic calibration degradation detection, because they are likely to shift in their positions and orientations together. Thus, an external sensor is often required. However, if the LiDAR sensor and the image sensor in the same HyDAR system are separately mounted and they do not shift in their positions and orientations together, they may also be used to do extrinsic calibration degradation detection.

FIG.20Dis a flowchart illustrating a particular example method2030of detecting the extrinsic calibration degradation for a HyDAR system mounted to a vehicle. Method2030corresponds to the block1350ofFIG.13. As shown inFIG.20D, in block2031, a HyDAR system (e.g., system1200or1802) and a second sensor external to the HyDAR system are placed to face predefined stationary targets with dateable key points (e.g., key features). In block2032, The HyDAR system sends laser emissions including laser light signals to the predefined stationary targets. The HyDAR system detects first return light signals by its LiDAR sensor and detects second return light signals by its image sensor. In block2033, a second sensor can also detect return light signals. The second sensor is external to the HyDAR system and may be a LiDAR sensor, a camera, or any other sensors. The HyDAR system has an internal or external controller. The second sensor may have an internal or external controller too.

In block2034, the controller of the HyDAR system (or another computer or control circuit) identifies a first set of key points based on the first return light signals and second return light signals detected by the HyDAR system. In block2035, a second set of key points is also identified by, for example, the internal or external controller of the second sensor external to to the HyDAR system. In block2036, the controller of the HyDAR system establishes correspondence between the first and second sets of key points, similar to those described above. In block2037, the controller of the HyDAR system computes a transformation (e.g., an affine transformation or any other transformation that translate one coordinate system to another). In block2038, the controller can determine if the transformation is as expected (e.g., by comparing with a ground truth). If yes, the controller reports (block2040) that the extrinsic parameters are consistent, which means there is no or minimum extrinsic calibration needed. If no, the controller reports (block2039) that the extrinsic calibration has drifted from the expected values. As described above, the controller can also compare the transformed first set of key points with the second set of key points, and vice versa. Or the controller can transform both the first set of key points and the second set of key points to a common coordinate system, and then compare the transformed first and second sets of key points. Based on the comparison, the controller can determine if the HyDAR system has an extrinsic calibration degradation.

In addition to extrinsic calibration degradation, a HyDAR system (or a LiDAR system) may also have intrinsic calibration degradation.FIG.21is a block diagram illustrating a HyDAR system1200that may have intrinsic calibration degradation, according to various embodiments. When a HyDAR system is manufactured, the position and orientation relations between its internal components are carefully configured and set. The HyDAR system may be mounted to a moveable platform like a vehicle. Thus, the HyDAR system may operate outdoor under all-weather conditions where it may encounter vibration, wear and tear, shock, and/or other external damages or impacts. As a result, when the HyDAR system operates over time, the relations between its internal components may change. For instance, as shown inFIG.21, some facets of the one or more steering mechanisms1206may have varied flatness; the steering mechanism1206may shift horizontally or vertically with respect to its original position; its pitch, yaw, and roll may also change with respect to other components; and/or the collection lens1210may also change its distance from the LiDAR sensor1202such that the first return light signals may not be focused well onto the LiDAR sensor1202. These types of relation changes between internal components of a HyDAR system is referred to as the intrinsic calibration degradation. It is understood that intrinsic calibration degradation is not limited to the examples shown inFIG.21.

FIG.22Ais a flowchart illustrating an example method2200for detecting intrinsic calibration degradation of a HyDAR system, according to various embodiments. Method2200corresponds to block1360ofFIG.13. As shown inFIG.22A, in block2202of method2200, a controller obtains at least the image data representing the second return light signals at different internal configurations of the HyDAR system. Optionally, the controller can obtain point cloud data or the combined dataset including the point cloud data and the image data. As described above, the configuration of a HyDAR system described herein has an integrated multimodal sensor including a LiDAR sensor and an image sensor. Therefore, the point cloud data and the image data are time-and-space synchronized at the hardware level. The method2200can be performed with just the image data or the combined dataset, and the below description uses the image data for illustration.

The image data may include data representing the second return light signals detected at two or more internal configurations of the HyDAR system. For instance, with reference toFIG.21, the image data may be generated based on second return light signals received by two different facets of the steering mechanism1206, or based on second return light signals formed from laser light signals transmitted by two different facets. Specifically, in one configuration, facet1206A is used to transmit laser light signals and to receive second return light signals; while in another configuration, facet1206B is used to transmit laser light signals and to receive the second return light signals. Facet1206A and1206B may be identical at the factory when the HyDAR system1200is manufactured; but over time, one or both facets may become skewed/wrapped/distorted/etc., as shown inFIG.21.

As another example, the multiple different configurations may include using different levels of laser power (e.g., high or low), different laser light signals repetition rates, different data collection cycles, different LiDAR sensor and/or image sensor sensitivities, etc. That is, the multiple internal configurations of the HyDAR system have different operational parameters or hardware configurations.

With reference back toFIG.22A, in block2204, the controller identifies a plurality of sets of features from a same set of predefined targets for each internal configuration of the different internal configurations. The predefine targets may be stationary targets as described above, or may be moving targets. As long as the same set of targets are used for each internal configuration of the multiple different internal configurations, the resulted sets of features can be used for detecting intrinsic calibration degradation.

In block2206of method2200, the controller correlates each of the plurality of sets of features to a physical setup of the predefined targets to calculate a relative position and orientation of a corresponding internal configuration of the different internal configurations. For example, with reference toFIG.21, assuming facet1206A and1206C are supposed to measure the same portion of the FOV, and facet1206A is slightly off by 0.5 degree in the horizontal direction (but the controller does not know) as illustrated inFIG.21. The same feature in the FOV is detected at azimuth=0 deg by the controller while using facet1206C (configuration 1), and again detected at azimuth=1 degree by the controller using facet1206A (configuration 2). From the difference between the detection of the same feature using different configuration, the controller can deduce the internal offset between facets1206A and1206C.

In block2208, the controller can validate whether the calculated relative positions and orientations of the different internal configurations fall within an acceptable tolerance from target values. For instance, a first set of features may be obtained when the HyDAR's system operates using the first internal configuration that has a first relative position and orientation; and a second of feature may be obtained when the HyDAR system operates using the second internal configuration that has a second relative position and orientation. The first and second relative positions and orientations can be used to calculate a transformation (e.g., an affine transformation). Or the first relative position and orientation can be transformed to a coordinate system under the second internal configuration, or vice versa.

Similar to described above for detecting the extrinsic calibration degradation, the intrinsic calibration degradation can be detected by comparing the transformation between the relative positions and orientations under two different internal configurations with a ground truth (or simply that they should not have any difference or have only minimum difference). The intrinsic calibration degradation can also be detected by comparing a relative position and orientation transformed from the coordinate system under the first configuration to the coordinate system under the second configuration, and vice versa. In other words, the intrinsic calibration degradation can be detected by comparing the relative positions and orientations obtained under two different internal configurations, with proper coordinate transformation. It is essentially using the relative position/orientation under one configuration as the basis for determining if the relative position/orientation under the other configuration changes beyond an acceptable tolerance from target values.

FIG.22Billustrates a flowchart of an example method2230for detecting intrinsic calibration degradation using two configurations of the steering mechanism (e.g., mechanism1206inFIG.21). In block2231of method2230, the HyDAR system is placed to face the predefined targets with detectable key points. The predefined targets can be those shown above inFIG.20B. In block2232, the HyDAR system operates to transmit laser light signals and receive first and second return light signals using the steering mechanism under the first configuration (e.g., receiving the return light signals using a first facet1206A). In block2233, the HyDAR system operates to transmit laser light signals and receive first and second return light signals using the steering mechanism under the second configuration (e.g., receiving the return light signals using a second facet1206B).

In block2234, a first set of key points are obtained based on the first and second return light signals received by the HyDAR system under the first configuration. In block2235, a second set of key points are obtained based on the first and second return light signals received by the HyDAR system under the second configuration.

In block2236, the controller correlates between the first set of key points and the second set of key points. In block2237, the controller computes a transformation between the two sets of key points. In block2238, the controller determines if the transformation is as expected. If yes, the controller reports (block2240) that the intrinsic parameters are consistent, which means no or minimum intrinsic calibration degradation is detected. If no, the controller reports (block2239) that the intrinsic calibration has drifted from the expected values. Blocks2236-2240can be substantially the same or similar to blocks2036-2040, respectively in method2030for detecting extrinsic calibration degradation shown inFIG.20D, and are thus not repeatedly described.

As described above, the HyDAR system described herein can perform early fusion of the point cloud data and the image data to detect one or more degradation factors including window blockage, interference light signals, extrinsic calibration degradation, and intrinsic calibration degradation. In addition, the early fusion can be used to enhance the point cloud data resolution.FIG.23are diagrams illustrating enhanced point cloud data resolution using image data, according to various embodiments. As shown inFIG.23, point cloud data2310are generated by the LiDAR sensor; and the image data2320are generated by the image sensor. Oftentimes, the point cloud data2310has a lower resolution than the image data2320. This is because the image sensor nowadays has a very large pixel array (e.g., in the tens or hundreds of millions), while the LiDAR sensor resolution is limited by the scanning speed, number of scanning beams, and laser pulse triggering rate.

Therefore, when a HyDAR system has an integrated multimodal sensor that includes both a LiDAR sensor and an image sensor, at least a part of the image data representing the second return light signals has corresponding point cloud data representing the first return light signals. On the other hand, a part of the image data representing the second return light signals may have no corresponding point cloud data representing the first return light signals. As shown inFIG.23, for instance, when the LiDAR sensor and the image sensor of a HyDAR system senses return light from the same FOV, data points2312A,2312B, and2312C in the point cloud data2310correspond to data points2322A,2322C, and2322E, respectively, in the image data2320. However, data points2322B,2322D, and2322F in the image data2320has no corresponding data points in the point cloud data2310, because the point cloud data2310has a lower point cloud resolution.

In some examples, the controller of a HyDAR system can infer distance information using the part of the image data representing the second return light signals that have no corresponding point cloud data representing the first return light signals, based on the at least a part of the image data representing the second return light signals that has corresponding point cloud data representing the first return light signals. For example, as shown inFIG.23, in point cloud data2330, the data points2312A,2312B and2312C are acquired data points based on the first return light signals detected by a LiDAR sensor of the HyDAR system. They are the same as those in point cloud data2310. The controller can find their corresponding data points2322A,2322C and2322E in the image data2320. Because the image data2320has a higher resolution, there might be one or more additional data points between these data points2322A,2322C, and2322E. As shown, between data points2322A and2322C in image data2320, there is one additional data point2322B. The controller can determine if the data point2322B has any changes from its neighboring data points2232A and2232C, and the extent of change. The changes can be changes in brightness, contrast, color, etc. When the change is small, it means that the physical distance in the FOV represented by the neighboring data points2232A,2232B, and2232C are small. Otherwise, the physical distance may be large.

Therefore, using the information obtained from the extent of changes between the data points2322A,2322B, and2322C, the controller can infer distance information for an inferred data point2332between real data points2312A and2312B in point cloud data2330. The inferred data point2332is not actually acquired by detecting a first return light signal. Instead, it is an inferred data point. For example, if the controller determines that the change is small from data point2322B to2322A and from data point2322B to2322C, it means the physical distance changes for the inferred data point2332A to2312A and from inferred data point2332A to2312B are likely small. The physical distance changes can be calculated or inferred based on the calculated changes between the data points2322A,2322B, and2322C. As a result, the distance at the inferred data point2332A can be inferred or estimated with a relatively high confidence.