Light detection and ranging (LiDaR) scan smoothing

Among other things, techniques are described for identifying, in a light detection and ranging (LiDAR) scan line, a first LiDAR data point and a plurality of LiDAR data points within a vicinity of the first LiDAR data point. The techniques may further include identifying, based on a comparison of the first LiDAR data point to at least one LiDAR data point of the plurality of LiDAR return points, a coefficient of the first LiDAR data point, wherein the coefficient is related to image smoothness. The techniques may further include identifying, based on a comparison of the coefficient to a threshold, whether to include the first LiDAR data point in an updated LiDAR scan line, and then identifying, based on the updated LiDAR scan line, a location of the autonomous vehicle.

FIELD OF THE INVENTION

This description relates to light detection and ranging (LiDAR) scan smoothing.

BACKGROUND

Typically, a vehicle such as an autonomous vehicle may use a localization procedure to identify where the vehicle is geographically. Specifically, the vehicle may obtain a depth image by, for example, performing a LiDAR scan. The LiDAR scan may then be compared against data related to known geographic locations to identify the location at which the vehicle is located.

DETAILED DESCRIPTION

In the drawings, specific arrangements or orderings of schematic elements, such as those representing devices, modules, instruction blocks and data elements, are shown for ease of description. However, it should be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the drawings is not meant to imply that a particular order or sequence of processing, or separation of processes, is required. Further, the inclusion of a schematic element in a drawing is not meant to imply that such element is required in all embodiments or that the features represented by such element may not be included in or combined with other elements in some embodiments.

Several features are described hereafter that can each be used independently of one another or with any combination of other features. However, any individual feature may not address any of the problems discussed above or might only address one of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Although headings are provided, information related to a particular heading, but not found in the section having that heading, may also be found elsewhere in this description. Embodiments are described herein according to the following outline:

1. General Overview

2. System Overview

3. Autonomous Vehicle Architecture

4. Autonomous Vehicle Inputs

5. Scan Line Overview

7. Use of the Smoothness Coefficient

General Overview

In order to smooth a LiDAR scan, a data point (which may also be referred to as a “return” or a “point”) in a LiDAR scan line is identified, and a smoothness coefficient is calculated for that data point based on neighboring data points in the scan line. Based on the smoothness coefficient, the data point is discarded from the LiDAR scan line (if, e.g., the smoothness coefficient equals or exceeds a threshold), or the data point remains in the LiDAR scan line (if, e.g., the smoothness coefficient equals or is below the threshold).

Embodiments herein provide a number of advantages, particularly when implemented as a part of a localization process performed by a vehicle. Specifically, in some embodiments, determining the existence of a relatively high smoothness coefficient associated with a data point can indicate the presence of a transient element in an environment such as a person, a bicycle, foliage, etc., that is associated with that data point. These transient elements are likely to change often (e.g., a person may walk away, a bicycle may ride away, foliage may change, etc.), and, as a result, it can be difficult for a vehicle to accurately perform the localization process based on the presence of these elements. Through detection and removal of data points associated with these elements, less transient structures such a buildings, walls, etc. can be more accurately identified. The use of these less transient structures can also increases the accuracy and repeatability of the localization process for the vehicle, thereby improving the overall efficiency of the vehicle. Additionally, or alternatively, comparisons between at least one LiDAR data point data point included in a LiDAR scan obtained by the vehicle and at least one LiDAR data point included in an earlier-generated LiDAR scan associated with a certain area can be reduced by discounting (e.g., removing) the at least one LiDAR data point included in a LiDAR scan obtained by the vehicle that is associated with (e.g., corresponds to) the transient elements.

System Overview

FIG.1shows an example of an autonomous vehicle100having autonomous capability.

As used herein, the term “autonomous capability” refers to a function, feature, or facility that enables a vehicle to be partially or fully operated without real-time human intervention, including without limitation fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles.

As used herein, an autonomous vehicle (AV) is a vehicle that possesses autonomous capability.

As used herein, “vehicle” includes means of transportation of goods or people. For example, cars, buses, trains, airplanes, drones, trucks, boats, ships, submersibles, dirigibles, etc. A driverless car is an example of a vehicle.

As used herein, “trajectory” refers to a path or route to navigate an AV from a first spatiotemporal location to second spatiotemporal location. In an embodiment, the first spatiotemporal location is referred to as the initial or starting location and the second spatiotemporal location is referred to as the destination, final location, goal, goal position, or goal location. In some examples, a trajectory is made up of one or more segments (e.g., sections of road) and each segment is made up of one or more blocks (e.g., portions of a lane or intersection). In an embodiment, the spatiotemporal locations correspond to real world locations. For example, the spatiotemporal locations are pick up or drop-off locations to pick up or drop-off persons or goods.

As used herein, “sensor(s)” includes one or more hardware components that detect information about the environment surrounding the sensor. Some of the hardware components can include sensing components (e.g., image sensors, biometric sensors), transmitting and/or receiving components (e.g., laser or radio frequency wave transmitters and receivers), electronic components such as analog-to-digital converters, a data storage device (such as a RAM and/or a nonvolatile storage), software or firmware components and data processing components such as an ASIC (application-specific integrated circuit), a microprocessor and/or a microcontroller.

As used herein, a “scene description” is a data structure (e.g., list) or data stream that includes one or more classified or labeled objects detected by one or more sensors on the AV vehicle or provided by a source external to the AV.

As used herein, a “road” is a physical area that can be traversed by a vehicle, and may correspond to a named thoroughfare (e.g., city street, interstate freeway, etc.) or may correspond to an unnamed thoroughfare (e.g., a driveway in a house or office building, a section of a parking lot, a section of a vacant lot, a dirt path in a rural area, etc.). Because some vehicles (e.g., 4-wheel-drive pickup trucks, sport utility vehicles, etc.) are capable of traversing a variety of physical areas not specifically adapted for vehicle travel, a “road” may be a physical area not formally defined as a thoroughfare by any municipality or other governmental or administrative body.

As used herein, a “lane” is a portion of a road that can be traversed by a vehicle. A lane is sometimes identified based on lane markings. For example, a lane may correspond to most or all of the space between lane markings, or may correspond to only some (e.g., less than 50%) of the space between lane markings. For example, a road having lane markings spaced far apart might accommodate two or more vehicles between the markings, such that one vehicle can pass the other without traversing the lane markings, and thus could be interpreted as having a lane narrower than the space between the lane markings, or having two lanes between the lane markings. A lane could also be interpreted in the absence of lane markings. For example, a lane may be defined based on physical features of an environment, e.g., rocks and trees along a thoroughfare in a rural area or, e.g., natural obstructions to be avoided in an undeveloped area. A lane could also be interpreted independent of lane markings or physical features. For example, a lane could be interpreted based on an arbitrary path free of obstructions in an area that otherwise lacks features that would be interpreted as lane boundaries. In an example scenario, an AV could interpret a lane through an obstruction-free portion of a field or empty lot. In another example scenario, an AV could interpret a lane through a wide (e.g., wide enough for two or more lanes) road that does not have lane markings. In this scenario, the AV could communicate information about the lane to other AVs so that the other AVs can use the same lane information to coordinate path planning among themselves.

The term “over-the-air (OTA) client” includes any AV, or any electronic device (e.g., computer, controller, IoT device, electronic control unit (ECU)) that is embedded in, coupled to, or in communication with an AV.

The term “over-the-air (OTA) update” means any update, change, deletion or addition to software, firmware, data or configuration settings, or any combination thereof, that is delivered to an OTA client using proprietary and/or standardized wireless communications technology, including but not limited to: cellular mobile communications (e.g., 2G, 3G, 4G, 5G), radio wireless area networks (e.g., WiFi) and/or satellite Internet.

The term “edge node” means one or more edge devices coupled to a network that provide a portal for communication with AVs and can communicate with other edge nodes and a cloud based computing platform, for scheduling and delivering OTA updates to OTA clients.

The term “edge device” means a device that implements an edge node and provides a physical wireless access point (AP) into enterprise or service provider (e.g., VERIZON, AT&T) core networks. Examples of edge devices include but are not limited to: computers, controllers, transmitters, routers, routing switches, integrated access devices (IADs), multiplexers, metropolitan area network (MAN) and wide area network (WAN) access devices.

As used herein, an AV system refers to the AV along with the array of hardware, software, stored data, and data generated in real-time that supports the operation of the AV. In an embodiment, the AV system is incorporated within the AV. In an embodiment, the AV system is spread across several locations. For example, some of the software of the AV system is implemented on a cloud computing environment.

In general, this document describes technologies applicable to any vehicles that have one or more autonomous capabilities including fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles, such as so-called Level 5, Level 4 and Level 3 vehicles, respectively (see SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, which is incorporated by reference in its entirety, for more details on the classification of levels of autonomy in vehicles). The technologies described in this document are also applicable to partially autonomous vehicles and driver assisted vehicles, such as so-called Level 2 and Level 1 vehicles (see SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems). In an embodiment, one or more of the Level 1, 2, 3, 4 and 5 vehicle systems may automate certain vehicle operations (e.g., steering, braking, and using maps) under certain operating conditions based on processing of sensor inputs. The technologies described in this document can benefit vehicles in any levels, ranging from fully autonomous vehicles to human-operated vehicles.

Autonomous vehicles have advantages over vehicles that require a human driver. One advantage is safety. For example, in 2016, the United States experienced 6 million automobile accidents, 2.4 million injuries, 40,000 fatalities, and 13 million vehicles in crashes, estimated at a societal cost of $910+ billion. U.S. traffic fatalities per 100 million miles traveled have been reduced from about six to about one from 1965 to 2015, in part due to additional safety measures deployed in vehicles. For example, an additional half second of warning that a crash is about to occur is believed to mitigate 60% of front-to-rear crashes. However, passive safety features (e.g., seat belts, airbags) have likely reached their limit in improving this number. Thus, active safety measures, such as automated control of a vehicle, are the likely next step in improving these statistics. Because human drivers are believed to be responsible for a critical pre-crash event in 95% of crashes, automated driving systems are likely to achieve better safety outcomes, e.g., by reliably recognizing and avoiding critical situations better than humans; making better decisions, obeying traffic laws, and predicting future events better than humans; and reliably controlling a vehicle better than a human.

Referring toFIG.1, an AV system120operates the vehicle100along a trajectory198through an environment190to a destination199(sometimes referred to as a final location) while avoiding objects (e.g., natural obstructions191, vehicles193, pedestrians192, cyclists, and other obstacles) and obeying rules of the road (e.g., rules of operation or driving preferences).

In an embodiment, the AV system120includes devices101that are instrumented to receive and act on operational commands from the computer processors146. We use the term “operational command” to mean an executable instruction (or set of instructions) that causes a vehicle to perform an action (e.g., a driving maneuver). Operational commands can, without limitation, including instructions for a vehicle to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate, decelerate, perform a left turn, and perform a right turn. In an embodiment, computing processors146are similar to the processor204described below in reference toFIG.2. Examples of devices101include a steering control102, brakes103, gears, accelerator pedal or other acceleration control mechanisms, windshield wipers, side-door locks, window controls, and turn-indicators.

In an embodiment, the AV system120includes sensors121for measuring or inferring properties of state or condition of the vehicle100, such as the AV's position, linear and angular velocity and acceleration, and heading (e.g., an orientation of the leading end of vehicle100). Example of sensors121are GPS, inertial measurement units (IMU) that measure both vehicle linear accelerations and angular rates, wheel speed sensors for measuring or estimating wheel slip ratios, wheel brake pressure or braking torque sensors, engine torque or wheel torque sensors, and steering angle and angular rate sensors.

In an embodiment, the sensors121also include sensors for sensing or measuring properties of the AV's environment. For example, monocular or stereo video cameras122in the visible light, infrared or thermal (or both) spectra, LiDAR123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, speed sensors, temperature sensors, humidity sensors, and precipitation sensors.

In an embodiment, the AV system120includes a data storage unit142and memory144for storing machine instructions associated with computer processors146or data collected by sensors121. In an embodiment, the data storage unit142is similar to the ROM208or storage device210described below in relation toFIG.2. In an embodiment, memory144is similar to the main memory206described below. In an embodiment, the data storage unit142and memory144store historical, real-time, and/or predictive information about the environment190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions. In an embodiment, data relating to the environment190is transmitted to the vehicle100via a communications channel from a remotely located database134.

In an embodiment, the AV system120includes communications devices140for communicating measured or inferred properties of other vehicles' states and conditions, such as positions, linear and angular velocities, linear and angular accelerations, and linear and angular headings to the vehicle100. These devices include Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication devices and devices for wireless communications over point-to-point or ad hoc networks or both. In an embodiment, the communications devices140communicate across the electromagnetic spectrum (including radio and optical communications) or other media (e.g., air and acoustic media). A combination of Vehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) communication (and, in some embodiments, one or more other types of communication) is sometimes referred to as Vehicle-to-Everything (V2X) communication. V2X communication typically conforms to one or more communications standards for communication with, between, and among autonomous vehicles.

In an embodiment, the communication devices140include communication interfaces. For example, wired, wireless, WiMAX, Wi-Fi, Bluetooth, satellite, cellular, optical, near field, infrared, or radio interfaces. The communication interfaces transmit data from a remotely located database134to AV system120. In an embodiment, the remotely located database134is embedded in a cloud computing environment. The communication devices140transmit data collected from sensors121or other data related to the operation of vehicle100to the remotely located database134. In an embodiment, communication devices140transmit information that relates to teleoperations to the vehicle100. In some embodiments, the vehicle100communicates with other remote (e.g., “cloud”) servers136.

In an embodiment, the remotely located database134also stores and transmits digital data (e.g., storing data such as road and street locations). Such data is stored on the memory144on the vehicle100, or transmitted to the vehicle100via a communications channel from the remotely located database134.

In an embodiment, the remotely located database134stores and transmits historical information about driving properties (e.g., speed and acceleration profiles) of vehicles that have previously traveled along trajectory198at similar times of day. In one implementation, such data can be stored on the memory144on the vehicle100, or transmitted to the vehicle100via a communications channel from the remotely located database134.

Computer processors146located on the vehicle100algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system120to execute its autonomous driving capabilities.

In an embodiment, the AV system120includes computer peripherals132coupled to computer processors146for providing information and alerts to, and receiving input from, a user (e.g., an occupant or a remote user) of the vehicle100. In an embodiment, peripherals132are similar to the display212, input device214, and cursor controller216discussed below in reference toFIG.2. The coupling is wireless or wired. Any two or more of the interface devices can be integrated into a single device.

In an embodiment, the AV system120receives and enforces a privacy level of a passenger, e.g., specified by the passenger or stored in a profile associated with the passenger. The privacy level of the passenger determines how particular information associated with the passenger (e.g., passenger comfort data, biometric data, etc.) is permitted to be used, stored in the passenger profile, and/or stored on the cloud server136and associated with the passenger profile. In an embodiment, the privacy level specifies particular information associated with a passenger that is deleted once the ride is completed. In an embodiment, the privacy level specifies particular information associated with a passenger and identifies one or more entities that are authorized to access the information. Examples of specified entities that are authorized to access information can include other AVs, third party AV systems, or any entity that could potentially access the information.

A privacy level of a passenger can be specified at one or more levels of granularity. In an embodiment, a privacy level identifies specific information to be stored or shared. In an embodiment, the privacy level applies to all the information associated with the passenger such that the passenger can specify that none of her personal information is stored or shared. Specification of the entities that are permitted to access particular information can also be specified at various levels of granularity. Various sets of entities that are permitted to access particular information can include, for example, other AVs, cloud servers136, specific third party AV systems, etc.

In an embodiment, the AV system120or the cloud server136determines if certain information associated with a passenger can be accessed by the AV100or another entity. For example, a third-party AV system that attempts to access passenger input related to a particular spatiotemporal location must obtain authorization, e.g., from the AV system120or the cloud server136, to access the information associated with the passenger. For example, the AV system120uses the passenger's specified privacy level to determine whether the passenger input related to the spatiotemporal location can be presented to the third-party AV system, the AV100, or to another AV. This enables the passenger's privacy level to specify which other entities are allowed to receive data about the passenger's actions or other data associated with the passenger.

FIG.2shows a computer system200. In an implementation, the computer system200is a special purpose computing device. The special-purpose computing device is hard-wired to perform the techniques or includes digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or can include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices can also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. In various embodiments, the special-purpose computing devices are desktop computer systems, portable computer systems, handheld devices, network devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.

In an embodiment, the computer system200includes a bus202or other communication mechanism for communicating information, and a processor204coupled with a bus202for processing information. The processor204is, for example, a general-purpose microprocessor. The computer system200also includes a main memory206, such as a random-access memory (RAM) or other dynamic storage device, coupled to the bus202for storing information and instructions to be executed by processor204. In one implementation, the main memory206is used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor204. Such instructions, when stored in non-transitory storage media accessible to the processor204, render the computer system200into a special-purpose machine that is customized to perform the operations specified in the instructions.

In an embodiment, the computer system200further includes a read only memory (ROM)208or other static storage device coupled to the bus202for storing static information and instructions for the processor204. A storage device210, such as a magnetic disk, optical disk, solid-state drive, or three-dimensional cross point memory is provided and coupled to the bus202for storing information and instructions.

In an embodiment, the computer system200is coupled via the bus202to a display212, such as a cathode ray tube (CRT), a liquid crystal display (LCD), plasma display, light emitting diode (LED) display, or an organic light emitting diode (OLED) display for displaying information to a computer user. An input device214, including alphanumeric and other keys, is coupled to bus202for communicating information and command selections to the processor204. Another type of user input device is a cursor controller216, such as a mouse, a trackball, a touch-enabled display, or cursor direction keys for communicating direction information and command selections to the processor204and for controlling cursor movement on the display212. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x-axis) and a second axis (e.g., y-axis), that allows the device to specify positions in a plane.

According to one embodiment, the techniques herein are performed by the computer system200in response to the processor204executing one or more sequences of one or more instructions contained in the main memory206. Such instructions are read into the main memory206from another storage medium, such as the storage device210. Execution of the sequences of instructions contained in the main memory206causes the processor204to perform the process steps described herein. In alternative embodiments, hard-wired circuitry is used in place of or in combination with software instructions.

In an embodiment, various forms of media are involved in carrying one or more sequences of one or more instructions to the processor204for execution. For example, the instructions are initially carried on a magnetic disk or solid-state drive of a remote computer. The remote computer loads the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the computer system200receives the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector receives the data carried in the infrared signal and appropriate circuitry places the data on the bus202. The bus202carries the data to the main memory206, from which processor204retrieves and executes the instructions. The instructions received by the main memory206can optionally be stored on the storage device210either before or after execution by processor204.

The computer system200also includes a communication interface218coupled to the bus202. The communication interface218provides a two-way data communication coupling to a network link220that is connected to a local network222. For example, the communication interface218is an integrated service digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface218is a local area network (LAN) card to provide a data communication connection to a compatible LAN. In some implementations, wireless links are also implemented. In any such implementation, the communication interface218sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.

The network link220typically provides data communication through one or more networks to other data devices. For example, the network link220provides a connection through the local network222to a host computer224or to a cloud data center or equipment operated by an Internet Service Provider (ISP)226. The ISP226in turn provides data communication services through the world-wide packet data communication network now commonly referred to as the “Internet”228. The local network222and Internet228both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link220and through the communication interface218, which carry the digital data to and from the computer system200, are example forms of transmission media. In an embodiment, the network220contains the cloud or a part of the cloud.

The computer system200sends messages and receives data, including program code, through the network(s), the network link220, and the communication interface218. In an embodiment, the computer system200receives code for processing. The received code is executed by the processor204as it is received, and/or stored in storage device210, or other non-volatile storage for later execution.

Autonomous Vehicle Architecture

FIG.3shows an example architecture300for an autonomous vehicle (e.g., the vehicle100shown inFIG.1). The architecture300includes a perception module302(sometimes referred to as a perception circuit), a planning module304(sometimes referred to as a planning circuit), a control module306(sometimes referred to as a control circuit), a localization module308(sometimes referred to as a localization circuit), and a database module310(sometimes referred to as a database circuit). Each module plays a role in the operation of the vehicle100. Together, the modules302,304,306,308, and310can be part of the AV system120shown inFIG.1. In some embodiments, any of the modules302,304,306,308, and310is a combination of computer software (e.g., executable code stored on a computer-readable medium) and computer hardware (e.g., one or more microprocessors, microcontrollers, application-specific integrated circuits [ASICs]), hardware memory devices, other types of integrated circuits, other types of computer hardware, or a combination of any or all of these things). Each of the modules302,304,306,308, and310is sometimes referred to as a processing circuit (e.g., computer hardware, computer software, or a combination of the two). A combination of any or all of the modules302,304,306,308, and310is also an example of a processing circuit.

In use, the planning module304receives data representing a destination312and determines data representing a trajectory314(sometimes referred to as a route) that can be traveled by the vehicle100to reach (e.g., arrive at) the destination312. In order for the planning module304to determine the data representing the trajectory314, the planning module304receives data from the perception module302, the localization module308, and the database module310.

The perception module302identifies nearby physical objects using one or more sensors121, e.g., as also shown inFIG.1. The objects are classified (e.g., grouped into types such as pedestrian, bicycle, automobile, traffic sign, etc.) and a scene description including the classified objects316is provided to the planning module304.

The planning module304also receives data representing the AV position318from the localization module308. The localization module308determines the AV position by using data from the sensors121and data from the database module310(e.g., a geographic data) to calculate a position. For example, the localization module308uses data from a GNSS (Global Navigation Satellite System) sensor and geographic data to calculate a longitude and latitude of the AV. In an embodiment, data used by the localization module308includes high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations of them), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types. In an embodiment, the high-precision maps are constructed by adding data through automatic or manual annotation to low-precision maps.

The control module306receives the data representing the trajectory314and the data representing the AV position318and operates the control functions320a-c(e.g., steering, throttling, braking, ignition) of the AV in a manner that will cause the vehicle100to travel the trajectory314to the destination312. For example, if the trajectory314includes a left turn, the control module306will operate the control functions320a-cin a manner such that the steering angle of the steering function will cause the vehicle100to turn left and the throttling and braking will cause the vehicle100to pause and wait for passing pedestrians or vehicles before the turn is made.

Autonomous Vehicle Inputs

FIG.4shows an example of inputs402a-d(e.g., sensors121shown inFIG.1) and outputs404a-d(e.g., sensor data) that is used by the perception module302(FIG.3). One input402ais a LiDAR (Light Detection and Ranging) system (e.g., LiDAR123shown inFIG.1). LiDAR is a technology that uses light (e.g., bursts of light such as infrared light) to obtain data about physical objects in its line of sight. A LiDAR system produces LiDAR data as output404a. For example, LiDAR data is collections of 3D or 2D points (also known as a point clouds) that are used to construct a representation of the environment190.

Another input402bis a RADAR system. RADAR is a technology that uses radio waves to obtain data about nearby physical objects. RADARs can obtain data about objects not within the line of sight of a LiDAR system. A RADAR system produces RADAR data as output404b. For example, RADAR data are one or more radio frequency electromagnetic signals that are used to construct a representation of the environment190.

Another input402cis a camera system. A camera system uses one or more cameras (e.g., digital cameras using a light sensor such as a charge-coupled device [CCD]) to obtain information about nearby physical objects. A camera system produces camera data as output404c. Camera data often takes the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). In some examples, the camera system has multiple independent cameras, e.g., for the purpose of stereopsis (stereo vision), which enables the camera system to perceive depth. Although the objects perceived by the camera system are described here as “nearby,” this is relative to the AV. In some embodiments, the camera system is configured to “see” objects far, e.g., up to a kilometer or more ahead of the AV. Accordingly, in some embodiments, the camera system has features such as sensors and lenses that are optimized for perceiving objects that are far away.

Another input402dis a traffic light detection (TLD) system. A TLD system uses one or more cameras to obtain information about traffic lights, street signs, and other physical objects that provide visual navigation information. A TLD system produces TLD data as output404d. TLD data often takes the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). A TLD system differs from a system incorporating a camera in that a TLD system uses a camera with a wide field of view (e.g., using a wide-angle lens or a fish-eye lens) in order to obtain information about as many physical objects providing visual navigation information as possible, so that the vehicle100has access to all relevant navigation information provided by these objects. For example, the viewing angle of the TLD system is about 120 degrees or more.

In some embodiments, outputs404a-dare combined using a sensor fusion technique. Thus, either the individual outputs404a-dare provided to other systems of the vehicle100(e.g., provided to a planning module304as shown inFIG.3), or the combined output can be provided to the other systems, either in the form of a single combined output or multiple combined outputs of the same type (e.g., using the same combination technique or combining the same outputs or both) or different types type (e.g., using different respective combination techniques or combining different respective outputs or both). In some embodiments, an early fusion technique is used. An early fusion technique is characterized by combining outputs before one or more data processing steps are applied to the combined output. In some embodiments, a late fusion technique is used. A late fusion technique is characterized by combining outputs after one or more data processing steps are applied to the individual outputs.

FIG.5shows an example of a LiDAR system502(e.g., the input402ashown inFIG.4). The LiDAR system502emits light504a-cfrom a light emitter506(e.g., a laser transmitter). Light emitted by a LiDAR system is typically not in the visible spectrum; for example, infrared light is often used. Some of the light504bemitted encounters a physical object508(e.g., a vehicle) and reflects back to the LiDAR system502. (Light emitted from a LiDAR system typically does not penetrate physical objects, e.g., physical objects in solid form.) The LiDAR system502also has one or more light detectors510, which detect the reflected light. In an embodiment, one or more data processing systems associated with the LiDAR system generates an image512representing the field of view514of the LiDAR system. The image512includes information that represents the boundaries516of a physical object508. In this way, the image512is used to determine the boundaries516of one or more physical objects near an AV.

FIG.6shows the LiDAR system502in operation. In the scenario shown in this figure, the vehicle100receives both camera system output404cin the form of an image602and LiDAR system output404ain the form of LiDAR data points604. In use, the data processing systems of the vehicle100compares the image602to the data points604. In particular, a physical object606identified in the image602is also identified among the data points604. In this way, the vehicle100perceives the boundaries of the physical object based on the contour and density of the data points604.

FIG.7shows the operation of the LiDAR system502in additional detail. As described above, the vehicle100detects the boundary of a physical object based on characteristics of the data points detected by the LiDAR system502. As shown inFIG.7, a flat object, such as the ground702, will reflect light704a-demitted from a LiDAR system502in a consistent manner. Put another way, because the LiDAR system502emits light using consistent spacing, the ground702will reflect light back to the LiDAR system502with the same consistent spacing. As the vehicle100travels over the ground702, the LiDAR system502will continue to detect light reflected by the next valid ground point706if nothing is obstructing the road. However, if an object708obstructs the road, light704e-femitted by the LiDAR system502will be reflected from points710a-bin a manner inconsistent with the expected consistent manner. From this information, the vehicle100can determine that the object708is present.

Scan Line Overview

As previously noted, for example with respect toFIGS.6and7, LiDAR is used by a vehicle such as an autonomous vehicle to gather data related to the surroundings of said vehicle. In an embodiment, a LiDAR scan includes multiple scan lines (e.g., 20 scan lines, 40 scan lines, etc.). Each line corresponds to a planar representation of the environment in which the LiDAR system (e.g., LiDAR system402aofFIG.4) is located.

FIG.8depicts an example LiDAR scan800with multiple scan lines805a,805b,805c,805d(collectively, scan lines805), in accordance with various embodiments. As previously noted, although only four scan lines805are depicted in the LiDAR scan800, in another embodiment the LiDAR scan800can include more or fewer scan lines. For example, the LiDAR scan800can include between 20 and 40 scan lines in one embodiment, while in another embodiment the LiDAR scan800includes more or fewer scan lines dependent on factors such as processing power of the LiDAR system, the speed with which the scan is being performed, an acceptable quality of the scan, or some other factor. Additionally, although the scan lines805are depicted as being generally lateral, in another embodiment the scan lines805are rotated such that they are vertical, or at some angle between lateral and vertical. Additionally, the scan lines805are depicted as being separated by a vertical distance (with respect to the orientation ofFIG.8), however in real-world implementations the distance between the scan lines805may be more or less than depicted. Generally,FIG.8should be interpreted as a high-level example for the purpose of illustrating and discussing concepts herein, rather than being considered as a limiting example of real-world implementations.

Respective ones of the scan lines805include a plurality of LiDAR data points810, which are similar to, for example, data points604. For example, in the embodiment depicted inFIG.8, each scan line805is shown as including sixteen LiDAR data points810. However, it will be understood that another embodiment will include more or fewer LiDAR data points than depicted.

Respective ones of the LiDAR data points810relate to data that is provided to the LiDAR system. Specifically, respective LiDAR data points810provide TOF data to the LiDAR system. The TOF data relates to the length between the time that an optical signal left an emitter of the LiDAR system and the time that the reflected optical signal was received by a receiver of the LiDAR system. Through the use of this TOF data for respective LiDAR data points810of scan lines805of a LiDAR scan800, the LiDAR system is able to construct, or facilitate construction of, a three-dimensional (3D) image of objects around the LiDAR system (for example, as explained above with respect toFIGS.4and5).

Smoothness Coefficient Examples

As previously noted, the results of a LiDAR scan (e.g., LiDAR scan800) are used by an autonomous vehicle for a localization process. That is, in an embodiment, the results of the LiDAR scan800are compared to pre-identified information of geographic locations. Based on this comparison, the autonomous vehicle is able to identify where the autonomous vehicle is currently located.

However, in some situations the localization procedure is complicated by inconsistent or transient objects or structures. For example, the shape of certain vegetation such as shrubs or trees changes with the changing of seasons, pruning, etc. Other transient objects such as people, bicycles, etc. will likewise create inconsistent results within the LiDAR scan such that a scan of a location at one time is different than a scan of the same location at a different time if such objects move or change shape.

By contrast, more consistent or permanent (e.g., non-transient) structures will provide more consistent results during the localization procedure. Such objects include artificial structures such as buildings, road barriers, walls, etc. Because these objects are artificial, these objects often have a relatively flat or smooth profile in comparison to the profile of objects such as trees, where the leaves or branches may generate significant variance within the LiDAR scan. Therefore, to increase the consistency of the results of the LiDAR scan, embodiments herein remove data points from the LiDAR scan that are identified as belonging to a transient structure such as vegetation, the presence of a person or bicycle, etc.

One way by which a data points may be removed from the LiDAR scan is by analyzing a data point in context with other data points around it. Specifically, in an embodiment, a data point is analyzed and a smoothness coefficient c is calculated. The smoothness coefficient c is then compared to a threshold value. If the smoothness coefficient c is greater than (or, optionally, greater than or equal to) the threshold value, then the LiDAR system (or a processor coupled thereto) identifies that the data point belongs to a region with a relatively high degree of variability, and therefore that the data point is associated with a transient object. In some embodiments, the LiDAR system can then determine that the data point should be removed from the scan line. If the smoothness coefficient c is less than (or, optionally, less than or equal to) the threshold value, then the LiDAR system (or a processor coupled thereto) identifies that the data point belongs to a region with a relatively low degree of variability and therefore should remain in the scan line (or be included in an output “smoothed” scan line).

Equation 1 presents an example equation by which a smoothness coefficient c is calculated for point X(k,i)L.

Specifically, XLrefers to a data point in a LiDAR scan such as LiDAR scan800. k is an identifier of a given scan line. In Equation 1, the data point in question, X(k,i)L, is compared to a number S of data points. i and j may refer to specific positions within the scan line. Specifically, the data related to data point X(k,i)Lis compared to data related to each of the other data points in S. Based on this comparison, the smoothness coefficient c is calculated. Specifically, vectors between the data point in question, X(k,i)L, and each other point in S are calculated or identified. The vectors are then summed together to produce the smoothness coefficient c.

It will be noted that in one embodiment each of the vectors between the data point in question and each of the other data points is calculated to produce the coefficient c. However, in another embodiment one or more of the vectors may have been pre-calculated and is stored such that it may be identified rather than re-calculated. For example, in one embodiment a coefficient c will have been calculated for one of the data points in S based on a vector between that data point and the data point currently in question, X(k,i)L. As such, recalculation of the vector may not be necessary and, instead, the vector may be identified as previously calculated.

FIG.9depicts a graphical example of the algorithm by which a smoothness coefficient related to a data point of a LiDAR scan is computed, in accordance with an embodiment.FIG.10depicts an alternative graphical example of the algorithm by which a smoothness coefficient related to a data point of a LiDAR scan is computed, in accordance with an embodiment.

Specifically, inFIGS.9and10, an X-axis X and a Y-axis Y are depicted. Generally, it may be assumed that the LiDAR system is located at the crosspoint of axes X and Y. A number of LiDAR data points are depicted inFIGS.9and10, which are similar to LiDAR data points810ofFIG.8.

Specifically,FIG.9depicts LiDAR data points905a,905b,905c,905d, and905e(collectively, LiDAR data points905).FIG.10depicts LiDAR data points1005a,1005b,1005c,1005d, and1005e(collectively, LiDAR data points1005). In the depiction ofFIGS.9and10, the LiDAR data points905and1005are based on the TOF data related to each data point, withFIGS.9and10representing a top-down view of the TOF data with respect to where the LiDAR system can be located.

Turning specifically toFIG.9, LiDAR data point905cis the data point under current analysis, and corresponds to X(k,i)Lof Equation 1. In this example, S is equal to 5.

In accordance with Equation 1, vectors910a,910b,910d, and910e(collectively, vectors910) are calculated between data point905cand each of the other data points905a,905b,905d, and905e. The vectors910are then summed together. It will be seen inFIG.9that the sum of vectors910are approximately zero. Specifically, the values associated with certain vectors (e.g., vectors910aand910e) can cancel one another out. Likewise, vectors910band910dcan cancel one another out. As such, the coefficient c is approximately 0, indicating that the data point905cis “smooth” and therefore should remain in the scan line or included in an updated scan line. It will be noted that this indication that data point905cis “smooth” corresponds to the visual depiction of the group of data points905inFIG.9.

By contrast,FIG.10depicts a group S of LiDAR data points1005wherein LiDAR data point1005cis considered to have a higher coefficient c than the coefficient c of data point905c. Similarly toFIG.9, a number of vectors1010a,1010b,1010d, and1010e(collectively, vectors1010) are calculated between data point1005cand the other data points ofFIG.10. The vectors1010are then summed to calculate coefficient c as discussed above with respect to Equation 1.

The horizontal components of each of the vectors1010a(e.g., the components along the X-axis) cancel one another out in a manner similar to that described above with respect toFIG.9. Specifically, the horizontal component of vector1010acancels the horizontal component of vector1010e. Similarly, the horizontal component of vector1010bcancels the horizontal component of vector1010d.

However, each of the vectors1010further have a vertical component (e.g., a component along the Y-axis) that is not canceled. As such, the overall sum of the vectors1010is greater than 0, resulting in a non-zero c.

This description of how a smoothness coefficient c is calculated may be considered to be a simplified example of one embodiment, and other embodiments may vary. For example, the specific Equation 1 is intended as one example, and other variations may be based on a different equation that is used for calculating a smoothness coefficient such as c. As one example variation, the group of data points S may include data points from multiple scan lines, rather than the single scan line described with respect to Equation 1 orFIG.9or10.

Use of the Smoothness Coefficient

FIG.11depicts an example technique by which a LiDAR scan line is updated, in accordance with various embodiments. Generally, the technique relates to the use of the smoothness coefficient c as described above. The technique may be performed by a LiDAR system such as LiDAR system402a(FIG.4), a processor such as processor204(FIG.2), a control module such as control module306(FIG.3), a localization module such as localization module308(FIG.3), some combination thereof, at least one additional element of an autonomous vehicle.

Initially, a scan line in a LiDAR scan is identified at1105. The scan line is similar to, for example, one of LiDAR scan lines805. The technique further includes identifying, at1110, LiDAR data points based on the scan line. The LiDAR data points are similar to, for example, LiDAR data points905, or1005and are based on LiDAR data points810in scan line(s)805.

The technique further includes identifying, at1115, a target LiDAR data point and neighboring LiDAR data points. The target LiDAR data point is, for example, LiDAR data point905cor1005cthat corresponds to X(k,i)L. The neighboring LiDAR data points are, for example, LiDAR data points905a/905b/905d/905e/1005a/1005b/1005d/1005ethat correspond to the other data points in S.

The technique further includes computing, at1120, a smoothness coefficient for a target LiDAR data point. The smoothness coefficient, for example, the coefficient c that is calculated in accordance with Equation 1 as described above. In another embodiment, the smoothness coefficient is additionally or alternatively a different coefficient that is calculated in accordance with a different equation or equation, or in accordance with at least one other variables.

The technique further includes comparing, at1125, the smoothness coefficient (e.g., c) with a threshold. In one embodiment, the above-noted comparison is performed based on identification of whether the smoothness coefficient is greater than (or greater than or equal to) the threshold. In another embodiment, the comparison is based on identification of whether the smoothness coefficient is less than (or less than or equal to) the threshold. In another embodiment, the comparison is a different type of comparison.

In one embodiment, the threshold is a pre-identified threshold. That is, the threshold is pre-identified based on previous testing or some other factor. In another embodiment, the threshold is at least partially dynamic. For example, the threshold is based on analysis of other data points in a given LiDAR scan, other data points in a scan line, previous calculated coefficients, etc.

If the coefficient is identified at1125to be less than (or, in an embodiment, less than or equal to) the threshold, then the specific LiDAR data point (e.g., X(k,i)L) is discarded. As used herein, discarding a data point refers to removing the specific LiDAR data point from a scan line or LiDAR scan that is used for the purpose of localization. By contrast, if the coefficient is identified to be greater than (or greater than or equal to) the threshold, then the LiDAR data point is included in a scan line that is processed by a localization module (e.g., localization module408). Such a scan line is referred to as an “updated” scan line. In an embodiment, the updated scan line is the existing scan line with the discarded data points removed. In another embodiment, the updated scan line is a new scan line that is being generated based on the included data points.

More specifically, in an embodiment, if the coefficient is less than (or less than or equal to) the threshold, then the LiDAR data point may be discarded from the scan line, while in another embodiment the LiDAR data point may be marked for removal from the scan line in, for example, a batch operation. In another embodiment, if the coefficient is greater than (or greater than or equal to) the threshold, then the LiDAR data point may not be removed from the scan line and, instead, may remain in the scan line. In another embodiment, the LiDAR data point may be included in a new iteration of the scan line which is based on LiDAR data points that were marked as having an acceptable smoothness coefficient (e.g., a coefficient that is greater than, or greater than or equal to, the threshold).

FIG.12depicts an alternative example technique by which a LiDAR scan line is updated, in accordance with an embodiment. GenerallyFIG.12is considered to be complementary toFIG.11, and includes similar elements. Similarly toFIG.11, the technique may be performed by a LiDAR system such as LiDAR system402a(FIG.4), a processor such as processor204(FIG.2), a control module such as control module306(FIG.3), a localization module such as localization module308(FIG.3), some combination thereof, or at least one additional elements of an autonomous vehicle.

The technique includes identifying, at1205, in a LiDAR scan line, a first LiDAR data point. The LiDAR scan line is similar to, for example, one of LiDAR scan lines805. The LiDAR data point is similar to, for example, LiDAR data points810,905, or1005. More specifically, the first LiDAR data point is similar to one of LiDAR data points905cor1005c.

The technique further includes identifying, at1210, a plurality of LiDAR data points within a vicinity of the first LiDAR data point in the LiDAR scan line1210. The plurality of LiDAR data points are, for example, the other data points in S and include data points905a/905b/905d/905e/1005a/1005b/1005d/1005e. As previously noted, in an embodiment all of the plurality of LiDAR data points are in a same scan line as the first LiDAR data point, while in another embodiment at least one of the plurality of LiDAR data points is in a different scan line than the first LiDAR data point.

The technique further includes identifying, at1215, based on a comparison of the first LiDAR data point to the plurality of LiDAR data points, a coefficient of the first LiDAR data point, wherein the coefficient is related to image smoothness. The comparison is, for example, the comparison described above with respect to Equation 1, and the coefficient is c. However, as previously noted, in another embodiment the comparison is some other type of comparison based on a different equation or at least one additional or alternative factor.

The technique further includes identifying, at1220based on a comparison of the coefficient to a threshold, whether to include the LiDAR return point in an updated LiDAR scan line. The threshold is, for example, the threshold described above with respect to element1125. Specifically, the threshold is a pre-determined threshold or a dynamic threshold as described above. Additionally, as noted, the comparison of the threshold is to identify whether the coefficient is greater than (or greater than or equal to) the threshold, or less than (or less than or equal to) the threshold.

If the coefficient is identified as being greater than (or greater than or equal to) the threshold, the first data point is discarded from an updated scan line. By contrast, if the coefficient is identified as being less than (or less than or equal to) the threshold, then the first data point is included in an updated scan line. As previously noted, in one embodiment inclusion in an updated scan line includes not removing the data point from an existing scan line. In another embodiment, inclusion in an updated scan line is based on including the data point in a new scan line that is being created.

The technique then includes identifying, at1225, based on the updated LiDAR scan line, a location of the AV. Specifically, as described above with respect to localization module308, the localization module308determines the AV position by using data from the sensors121(e.g., the updated scan line) and data from the database module310(e.g., a geographic data) to calculate a position. As described above, by removing data points that are related to transient elements such as trees, people, etc., then the localization module308will identify the position of the AV based on non-transient elements such as buildings, roadways, etc. As a result, the consistency of the localization module308will be increased, thereby increasing the overall efficiency of navigation of the AV.

It will be understood that the techniques described with respect toFIGS.11and12are intended as high-level example techniques, and another embodiment will include variations from those examples. For example, various embodiments have more or fewer elements than depicted in the example techniques, or elements in a different arrangement or order than depicted. Other variations will be present in another embodiment.

In the foregoing description, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The description and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. In addition, when we use the term “further comprising,” in the foregoing description or following claims, what follows this phrase can be an additional step or entity, or a sub-step/sub-entity of a previously-recited step or entity.