Patent Description:
The present disclosure relates generally to LIDAR systems.

LIDAR systems use lasers to create three-dimensional representations of surrounding environments. A LIDAR system includes at least one emitter paired with a receiver to form a channel, though an array of channels may be used to expand the field of view of the LIDAR system. During operation, each channel emits a laser beam into the environment. The laser beam reflects off of an object within the surrounding environment, and the reflected laser beam is detected by the receiver. A single channel provides a single point of ranging information. Collectively, channels are combined to create a point cloud that corresponds to a three-dimensional representation of the surrounding environment. The LIDAR system also includes circuitry to measure the time-of-flight (that is, the elapsed time from emitting the laser beam to detecting the reflected laser beam). The time-of-flight measurement is used to determine the distance of the LIDAR system to the object. Document <CIT> and <CIT> each disclose a laser emitter with an optical element in the transmit path, the optical element being configured to direct a portion of the emitted beam towards the receive path as secondary laser beam.

Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or may be learned from the description, or may be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to a LIDAR system including the technical features set out in claim <NUM>. The LIDAR system includes an emitter. The emitter includes a light source and one or more lenses positioned along a transmit path. The light source is configured to emit a primary laser beam through the one or more lenses in the transmit path to provide a transmit beam. The LIDAR system includes - inter alia - a receiver spaced apart from the emitter. The receiver includes one or more lenses positioned along a receive path such that the one or more lenses receive a reflected laser beam. The LIDAR system includes an optical element positioned along the transmit path. The optical element is configured to direct a portion of the primary laser beam in a direction towards the receive path as a secondary laser beam that is emitted to or towards a near-field associated with the Lidar system corresponding to an area ranging from about <NUM> meters in front of the receiver to about <NUM> meters in front of the receiver.

Another example aspect of the present disclosure is directed to an autonomous vehicle. The autonomous vehicle includes a LIDAR system according to claim <NUM> or the claims dependnet from claim <NUM>.

Yet another example aspect of the present disclosure is directed to a method of operating a LIDAR system that includes an emitter and a receiver spaced apart from the emitter. The method includes - inter alia - emitting, via the emitter, a primary laser beam through one or more lenses disposed along a transmit path to provide a transmit beam. The method further includes directing, via an optical element disposed along the transmit path, a portion of the primary laser beam as a secondary laser beam towards a receive path associated with the receiver of the LIDAR system. Still further, the method includes directing, via an optical element disposed along the transmit path, a portion of the primary laser beam as a secondary laser beam towards a receive path associated with the receiver of the LIDAR wherein the secondary laser beam is emitted to or towards a near-field associated with the Lidar system corresponding to an area ranging from about <NUM> meters in front of the receiver to about <NUM> meters in front of the receiver.

The autonomous vehicle technology described herein can help improve the safety of passengers of an autonomous vehicle, improve the safety of the surroundings of the autonomous vehicle, improve the experience of the rider and/or operator of the autonomous vehicle, as well as provide other improvements as described herein. Moreover, the autonomous vehicle technology of the present disclosure can help improve the ability of an autonomous vehicle to effectively provide vehicle services to others and support the various members of the community in which the autonomous vehicle is operating, including persons with reduced mobility and/or persons that are underserved by other transportation options. Additionally, the autonomous vehicle of the present disclosure may reduce traffic congestion in communities as well as provide alternate forms of transportation that may provide environmental benefits.

These and other features, aspects and advantages of various embodiments will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the related principles.

Example aspects of the present disclosure are directed to a light detection and ranging (LIDAR) system. The LIDAR system can be used, for instance, in conjunction with an autonomous vehicle. The LIDAR system can include an emitter and a receiver that is spaced apart from the emitter. The emitter can emit a transmit beam that can reflect off of one or more objects. The receiver can receive one or more reflected beams and process the one or more reflected beams to generate a depth map of a surrounding environment in which the autonomous vehicle is operating. However, since the receiver is spaced apart from the emitter, an amount of light received by the receiver decreases when the one or more objects are within a close range (e.g., within about <NUM> meters) of the autonomous vehicle. This decrease in the amount of light when the one or more objects are within the close range of the autonomous vehicle introduces a parallax error. The parallax error is due, at least in part, to the reflected laser beams moving relative to the receiver such that the one or more objects are not detected by the LIDAR system. In this manner, the LIDAR system can have a blind spot that is due, at least in part, to the offset (e.g., spacing) between the emitter and the receiver.

The technology of the present disclosure can improve the LIDAR system to solve this parallax error and reduce such a resulting blind spot. For instance, the emitter of the LIDAR system can include a light source (e.g., laser diode) and one or more lenses or other optical elements positioned along a transmit path. The light source can emit a primary laser beam through the one or more lenses or other optical elements to provide a transmit beam along the transmit path. The transmit beam can have a narrow divergence angle so that it can travel long distances and can be reflected off of surfaces.

Furthermore, the receiver of the LIDAR system can include a detector and one or more lenses or other optical elements positioned along a receive path. In this manner, the one or more lenses or other optical elements can focus a reflected laser beam onto the detector. The detector can generate data points and/or a point cloud based on the detected laser beams to generate a depth map of a surrounding environment.

According to example aspects of the present disclosure, the LIDAR system can include an optical element positioned along the transmit path. The optical element can direct a portion of the primary laser beam in a direction towards the receive path as a secondary laser beam. The secondary laser beam can have a wide divergence angle relative to the transmit beam. Furthermore, the secondary laser beam can spread within a near-field associated with the LIDAR system. As such, the secondary laser beam can reflect off of objects within the near-field and can be directed onto the detector via the one or more lenses or other optical elements in the receive path. Furthermore, since the secondary laser beam spreads within the near-field, light reflected off the objects with the near-field and directed onto the detector can be increased. This can lead to increased capability of the LIDAR system to detect objects within the near-field that may otherwise not be detected due to the parallax error.

In some implementations, the LIDAR system can be implemented onboard an autonomous vehicle (e.g., ground-based vehicle, aerial vehicle, etc.). The autonomous vehicle can include various systems and devices configured to control the operation of the autonomous vehicle. For example, the autonomous vehicle can include an onboard vehicle computing system (e.g., located on or within the autonomous vehicle) that is configured to operate the autonomous vehicle. The onboard vehicle computing system can obtain sensor data from sensor(s) onboard the vehicle (e.g., cameras, LIDAR, RADAR, etc.), attempt to comprehend the vehicle's surrounding environment by performing various processing techniques on the sensor data, and generate an appropriate motion plan through the vehicle's surrounding environment. This can include, for example, detecting of object(s) (e.g., pedestrians, vehicles, bicycles/bicyclists, etc.) within the vehicle's surrounding environment, predicting the future motion trajectory of those objects, and planning the vehicle's motion to avoid interference with the object(s). Moreover, the autonomous vehicle can include a communications system that can allow the autonomous vehicle to communicate with a computing system that is remote from the autonomous vehicle such as, for example, that of a service entity.

An autonomous vehicle can perform vehicle services for one or more service entities. A service entity can be associated with the provision of one or more vehicle services. For example, a service entity can be an individual, a group of individuals, a company (e.g., a business entity, organization, etc.), a group of entities (e.g., affiliated companies), and/or another type of entity that offers and/or coordinates the provision of vehicle service(s) to one or more users. As an example, a service entity can offer vehicle service(s) to users via a software application (e.g., on a user computing device), via a website, and/or via other types of interfaces that allow a user to request a vehicle service. The vehicle services can include user transportation services (e.g., by which the vehicle transports user(s) from one location to another), delivery services (e.g., by which a vehicle delivers item(s) to a requested destination location), courier services (e.g., by which a vehicle retrieves item(s) from a requested origin location and delivers the item to a requested destination location), and/or other types of services.

An operations computing system of the service entity can help to coordinate the performance of vehicle services by autonomous vehicles. For instance, the operations computing system can include a service platform. The service platform can include a plurality of back-end services and front-end interfaces, which are accessible via one or more APIs. For example, an autonomous vehicle and/or another computing system that is remote from the autonomous vehicle can communicate/access the service platform (and its backend services) by calling the one or more APIs. Such components can facilitate secure, bidirectional communications between autonomous vehicles and/or the service entity's operations system (e.g., including a data center, etc.).

The service platform can allow an autonomous vehicle to obtain data from and/or communicate data to the operations computing system. By way of example, a user can provide (e.g., via a user device) a request for a vehicle service to the operations computing system associated with the service entity. The request can indicate the type of vehicle service that the user desires (e.g., a user transportation service, a delivery service, etc.), one or more locations (e.g., an origin, destination, etc.), timing constraints (e.g., pick-up time, drop-off time, deadlines, etc.), a number of user(s) and/or items to be transported in the vehicle, other service parameters (e.g., a need for handicap access, handle with care instructions, etc.), and/or other information. The operations computing system of the service entity can process the request and identify one or more autonomous vehicles that may be able to perform the requested vehicle services for the user. For instance, the operations computing system can identify which autonomous vehicle(s) are online with the service entity (e.g., available for a vehicle service assignment, addressing a vehicle service assignment, etc.). An autonomous vehicle can go online with a service entity by, for example, connecting with the service entity's operations computing system (e.g., the service platform) so that the vehicle computing system can communicate with the operations computing system via a network. Once online, the operations computing system can communicate a vehicle service assignment indicative of the requested vehicle services and/or other data to the autonomous vehicle.

The autonomous vehicle can be configured to operate in one or more modes including, for example, a fully autonomous operating mode, a semi-autonomous operating mode, and a manual operating mode. The fully autonomous (e.g., self-driving) operating mode can be one in which the autonomous vehicle can provide driving and navigational operation with minimal and/or no interaction from a human driver present in the autonomous vehicle. The semi-autonomous operating mode can be one in which the vehicle can operate with some interaction from a human driver present in the vehicle. The manual operating mode can be one in which a human driver present in the autonomous vehicle manually controls (e.g., acceleration, braking, steering) the autonomous vehicle via one or more input devices (e.g., steering device) of the autonomous vehicle.

The LIDAR system can be implemented on the autonomous vehicle to obtain data associated with the surrounding environment in which the autonomous vehicle is operating (e.g., while online with service entity, performing a vehicle service, etc.). In particular, the emitter of the LIDAR system and the receiver of the LIDAR system can be spaced apart from one another along a lateral direction of the autonomous vehicle or a vertical direction of the autonomous vehicle. It should be understood that the lateral direction can extend between opposing sides (e.g., first side and second side) of the autonomous vehicle. It should also be understood that the vertical direction can extend between a bottom portion of the autonomous vehicle and a top portion of the autonomous vehicle. Operation of the LIDAR system will now be discussed in more detail.

The emitter can include a light source (e.g., laser diode) and one or more lenses positioned along a transmit path. The light source can be configured to emit a primary laser beam through the one or more lenses to provide a transmit beam. In some implementations, a divergence angle associated with the transmit beam can be about <NUM> degrees or less. It should be understood that the divergence angle is indicative of an increase in beam diameter or radius of the transmit beam with distance from the one or more lenses. Stated another way, the divergence angle of the transmit beam can be the angle subtended by the transmit beam exiting the last lens in the transmit path.

It should be understood that the LIDAR system can include any suitable number of emitters. For instance, in some implementations, the LIDAR system can be configured as a single channel system having only one emitter. In alternative implementations, the LIDAR system can be configured as a multi-channel system that includes an array of emitters. Furthermore, each emitter included in the array of light sources can correspond to a single channel in the multi-channel system.

In some implementations, the one or more lenses of the emitter can include a first lens and a second lens. It should be understood, however, that the emitter can include more or fewer lenses. The first lens and the second lens can, in some implementations, be positioned along the transmit path such that the first lens is positioned between the light source and the second lens along the transmit path. In this manner, the primary laser beam emitted from the light source can pass through the first lens before passing through the second lens.

In some implementations, the first lens and the second lens can each be a collimation lens. For instance, the first lens and the second lens can be a fast-axis collimation lens and a slow-axis collimation lens, respectively. The fast-axis collimation lens can be configured to reduce a divergence angle associated with the primary laser beam. In particular, the fast-axis lens can be configured to reduce the divergence angle such that the primary laser beam is directed towards the slow-axis collimation lens. It should also be understood that the beam output by the slow-axis collimation lens can be the transmit beam.

The receiver can include a detector and one or more lenses positioned along a receive path. In this manner, the one or more lenses of the receiver can receive a reflected laser beam. Furthermore, the one or more lenses of the receiver can be configured to direct the reflected laser beam onto the detector. The detector can be configured to process the reflected laser beam to generate a point cloud indicative of the surrounding environment in which the autonomous vehicle is operating. In particular, the point cloud can be indicative of a distance to one or more objects along a path to be traversed by the autonomous vehicle.

According to example aspects of the present disclosure, the LIDAR system can include an optical element disposed along the transmit path. The optical element can be configured to divert a portion of the primary laser beam towards the receive path as a secondary laser beam. An amount of energy associated with the secondary laser beam can be less than an amount of energy associated with the primary laser beam. For instance, in some implementations, the amount of energy associated with the secondary laser beam can be less than about <NUM> percent of a total energy associated with the primary laser beam. Additionally, or alternatively, a divergence angle associated with the secondary laser beam can be different than the divergence angle associated with the transmit beam. For instance, in some implementations, the divergence angle of the secondary laser beam can range from about <NUM> degrees to about <NUM> degrees. In this manner, the secondary laser beam can spread out with distance and therefore be less focused than the transmit beam. It should be understood that the divergence angle of the secondary laser beam can be the angle subtended by the secondary laser beam exiting the optical element.

In some implementations, the optical element can form at least a portion of the one or more lenses in the transmit path. For instance, in some implementations, the one or more lenses in the transmit path can include a bifocal lens. In such implementations, the bifocal lens can be configured to direct a majority of the primary laser beam along the transmit path as the transmit beam. The bifocal lens can be further configured to direct a minority of the primary laser beam towards the receive path as the secondary laser beam.

In some implementations, the one or more lenses in the transmit path can include a fast-axis collimation lens. In such implementations, the optical element can include a portion of the fast-axis collimation lens. For instance, a portion of the fast-axis collimation lens can be configured to direct a portion of the primary laser beam towards the receive path as the secondary laser beam. Furthermore, the fast-axis collimation lens can be configured to spread the secondary laser beam to reduce or eliminate a parallax error associated with detecting objects in the near-field associated with the LIDAR system.

In some implementations, the optical element can form at least a portion of the one or more lenses in the transmit path. For instance, the one or more lenses in the transmit path can include a slow-axis collimation lens. In such implementations, the portion of the lens can form at least a portion of the slow-axis collimation lens. In this manner, the portion of the lens can divert a portion of the primary laser beam towards the receive path as the secondary laser beam.

In some implementations, the optical element can include a diffuser. The diffuser can spread the primary laser beam such that a portion of the primary laser beam is directed towards the receive path as the secondary laser beam. In some implementations, the diffuser can form at least a portion of the one or more lenses in the transmit path. For instance, the diffuser can correspond to a portion of a surface of the one or more lenses that is modified relative to the remainder of the surface. As an example, the portion of the surface can be roughened relative to the remainder of the surface. In some implementations, the diffuser can include a coating that is applied to at least a portion of the surface of the one or more lenses in the transmit path. The portion of the surface to which the coating is applied can direct the primary light towards the receive path as the secondary laser beam.

In some implementations, the portion of the surface of the one or more lenses that is modified relative to the remainder of the surface can correspond to any suitable surface of the one or more lenses in the transmit path. For instance, in some implementations, the surface can be associated with an edge of the one or more lenses in the transmit path. It should also be appreciated that, in some implementations, the diffuser can be separate (e.g., standalone) from the one or more lenses in the transmit path.

In some implementations, the optical element can include a divergence lens (e.g., a wedge prism) positioned in the transmit path between the first lens and the second lens. In such implementations, the divergence lens can be configured to divert a portion of the primary laser beam exiting the first lens towards the receive path as a secondary laser beam. It should be appreciated, however, that the divergence lens can be positioned at any suitable location along the transmit path.

Another example aspect of the present disclosure is directed to a method of controlling operation of the LIDAR system. The method can include emitting, via the emitter, the primary laser beam through the one or more lenses disposed along the transmit path to provide the transmit beam. The method can further include directing, via the optical element disposed along the transmit path, a portion of the primary laser beam towards the receive path as the secondary laser beam. In addition, the method can include receiving, via one or more lenses of the receiver, a reflected laser beam. Furthermore, the method can include generating, via the detector of the receiver, a point cloud based, at least in part, on data associated with the reflected laser beam.

An autonomous vehicle can utilize the described LIDAR system to account for object(s) within a near-field associated with the LIDAR system during autonomous operation. For instance, an autonomous vehicle (e.g., its onboard computing system) can obtain sensor data via the LIDAR system. The sensor data can be indicative of an object within a near-field associated with the LIDAR system. The autonomous vehicle can determine perception data for the object within the near-field associated with the LIDAR system based at least in part on the sensor data. The perception data can describe, for example, an estimate of the object's current and/or past: location and/or position; speed; velocity; acceleration; heading; orientation; size/footprint (e.g., as represented by a bounding shape); class (e.g., pedestrian class vs. vehicle class vs. bicycle class); and/or other state information. The autonomous vehicle can determine future location(s) of the object based at least in part on the perception data. For example, the autonomous vehicle can generate a trajectory (e.g., including one or more waypoints) that is indicative of a predicted future motion of the object, given the object's heading, velocity, type, etc. over current/previous timestep(s). The autonomous vehicle can determine an action for the autonomous vehicle based at least in part on the detected object and/or the future location(s) of the object within the near-field associated with the LIDAR system. For example, the autonomous vehicle can generate a motion plan that includes a vehicle trajectory by which the vehicle can travel to avoid interfering/colliding with the object. In another example, the autonomous vehicle can determine that the object is a user that intends to enter the autonomous vehicle (e.g., for a human transportation service) and/or that intends place an item in the autonomous vehicle (e.g., for a courier/delivery service). The autonomous vehicle can unlock a door, trunk, etc. to allow the user to enter and/or place an item within the vehicle. The autonomous vehicle can communicate one or more control signals (e.g., to a motion control system, door control system, etc.) to initiate the determined actions.

A LIDAR system in accordance with the present disclosure can provide numerous technical effects and benefits. For instance, the optical element disposed in the transmit path can, as discussed above, divert a portion of the primary laser beam emitted by the light source of the emitter towards the receive path as a secondary laser beam. Furthermore, a divergence angle associated with the secondary laser beam can be greater (e.g., by at least about <NUM> times) than a divergence angle associated with the transmit beam emitted along the transmit path. In this manner, the secondary laser beam can spread out with distance and therefore be less focused than the transmit beam. As such, the secondary laser beam can reflect off of objects within a near-field associated with the LIDAR system and can be focused onto the detector via one or more lenses disposed along the receive path. Furthermore, since the secondary laser beam is being reflected off of objects within the near-field and being focused onto the detector, detection of the objects within the near-field can be improved. In particular, a parallax error associated with detecting objects in the near-field can be reduced or eliminated.

Referring now to the FIGS. , <FIG> depicts a system <NUM> that includes a communications network <NUM>; an operations computing system <NUM>; one or more remote computing devices <NUM>; a vehicle <NUM>; a vehicle computing system <NUM>; one or more sensors <NUM>; sensor data <NUM>; a positioning system <NUM>; an autonomy computing system <NUM>; map data <NUM>; a perception system <NUM>; a prediction system <NUM>; a motion planning system <NUM>; perception data <NUM>; prediction data <NUM>; motion plan data <NUM>; a communication system <NUM>; a vehicle control system <NUM>; and a human-machine interface <NUM>.

The operations computing system <NUM> can be associated with a service provider that can provide one or more vehicle services to a plurality of users via a fleet of vehicles that includes, for example, the vehicle <NUM>. The vehicle services can include transportation services (e.g., rideshare services), courier services, delivery services, and/or other types of services.

The operations computing system <NUM> can include multiple components for performing various operations and functions. For example, the operations computing system <NUM> can be configured to monitor and communicate with the vehicle <NUM> and/or its users to coordinate a vehicle service provided by the vehicle <NUM>. To do so, the operations computing system <NUM> can communicate with the one or more remote computing devices <NUM> and/or the vehicle <NUM> via one or more communications networks including the communications network <NUM>. The communications network <NUM> can send and/or receive signals (e.g., electronic signals) or data (e.g., data from a computing device) and include any combination of various wired (e.g., twisted pair cable) and/or wireless communication mechanisms (e.g., cellular, wireless, satellite, microwave, and radio frequency) and/or any desired network topology (or topologies). For example, the communications network <NUM> can include a local area network (e.g. intranet), wide area network (e.g. the Internet), wireless LAN network (e.g., via Wi-Fi), cellular network, a SATCOM network, VHF network, a HF network, a WiMAX based network, and/or any other suitable communications network (or combination thereof) for transmitting data to and/or from the vehicle <NUM>.

Each of the one or more remote computing devices <NUM> can include one or more processors and one or more memory devices. The one or more memory devices can be used to store instructions that when executed by the one or more processors of the one or more remote computing devices <NUM> cause the one or more processors to perform operations and/or functions including operations and/or functions associated with the vehicle <NUM> including sending and/or receiving data or signals to and from the vehicle <NUM>, monitoring the state of the vehicle <NUM>, and/or controlling the vehicle <NUM>. The one or more remote computing devices <NUM> can communicate (e.g., exchange data and/or signals) with one or more devices including the operations computing system <NUM> and the vehicle <NUM> via the communications network <NUM>. For example, the one or more remote computing devices <NUM> can request the location of the vehicle <NUM> or a state of one or more objects detected by the one or more sensors <NUM> of the vehicle <NUM>, via the communications network <NUM>.

The one or more remote computing devices <NUM> can include one or more computing devices (e.g., a desktop computing device, a laptop computing device, a smart phone, and/or a tablet computing device) that can receive input or instructions from a user or exchange signals or data with an item or other computing device or computing system (e.g., the operations computing system <NUM>). Further, the one or more remote computing devices <NUM> can be used to determine and/or modify one or more states of the vehicle <NUM> including a location (e.g., a latitude and longitude), a velocity, an acceleration, a trajectory, a heading, and/or a path of the vehicle <NUM> based, at least in part, on signals or data exchanged with the vehicle <NUM>. In some implementations, the operations computing system <NUM> can include the one or more remote computing devices <NUM>.

The vehicle <NUM> can be a ground-based vehicle (e.g., an automobile, a motorcycle, a train, a tram, a bus, a truck, a tracked vehicle, a light electric vehicle, a moped, a scooter, and/or an electric bicycle), an aircraft (e.g., airplane or helicopter), a boat, a submersible vehicle (e.g., a submarine), an amphibious vehicle, a hovercraft, a robotic device (e.g. a bipedal, wheeled, or quadrupedal robotic device), and/or any other type of vehicle. The vehicle <NUM> can be an autonomous vehicle that can perform various actions including driving, navigating, and/or operating, with minimal and/or no interaction from a human driver. The vehicle <NUM> can be configured to operate in one or more modes including, for example, a fully autonomous operational mode, a semi-autonomous operational mode, a manual operating mode, a park mode, and/or a sleep mode. A fully autonomous (e.g., self-driving) operational mode can be one in which the vehicle <NUM> can provide driving and navigational operation with minimal and/or no interaction from a human driver present in the vehicle. A semi-autonomous operational mode can be one in which the vehicle <NUM> can operate with some interaction from a human driver present in the vehicle. A manual operating mode can be one in which a human driver present in the autonomous vehicle manually controls (e.g., acceleration, braking, steering) the vehicle <NUM> via one or more vehicle control devices (e.g., steering device) of the vehicle <NUM>. Park and/or sleep modes can be used between operational modes while the vehicle <NUM> performs various actions including waiting to provide a subsequent vehicle service, and/or recharging between operational modes.

An indication, record, and/or other data indicative of the state of the vehicle <NUM>, the state of one or more passengers of the vehicle <NUM>, and/or the state of an environment external to the vehicle <NUM> including one or more objects (e.g., the physical dimensions, velocity, acceleration, heading, location, and/or appearance of the one or more objects) can be stored locally in one or more memory devices of the vehicle <NUM>. Furthermore, as discussed above, the vehicle <NUM> can provide data indicative of the state of the one or more objects (e.g., physical dimensions, velocity, acceleration, heading, location, and/or appearance of the one or more objects) within a predefined distance of the vehicle <NUM> to the operations computing system <NUM> and/or the remote computing devices <NUM>, which can store an indication, record, and/or other data indicative of the state of the one or more objects within a predefined distance of the vehicle <NUM> in one or more memory devices associated with the operations computing system <NUM> and/or the one or more remote computing devices <NUM> (e.g., remote from the vehicle).

The vehicle <NUM> can include and/or be associated with the vehicle computing system <NUM>. The vehicle computing system <NUM> can include one or more computing devices located onboard the vehicle <NUM>. For example, the one or more computing devices of the vehicle computing system <NUM> can be located on and/or within the vehicle <NUM>. The one or more computing devices of the vehicle computing system <NUM> can include various components for performing various operations and functions. For instance, the one or more computing devices of the vehicle computing system <NUM> can include one or more processors and one or more tangible non-transitory, computer readable media (e.g., memory devices). The one or more tangible non-transitory, computer readable media can store instructions that when executed by the one or more processors cause the vehicle <NUM> (e.g., its computing system, one or more processors, and other devices in the vehicle <NUM>) to perform operations and/or functions, including those described herein for accessing state data including information associated with one or more respective locations and/or characteristics of one or more objects over a plurality of time intervals and/or determining, based at least in part on the state data and a machine-learned prediction generator model, one or more predicted trajectories of the one or more objects at one or more subsequent time intervals following the plurality of time intervals. Furthermore, the vehicle computing system <NUM> can perform one or more operations associated with the control, exchange of data, and/or operation of various devices and systems including robotic devices and/or other computing devices.

As depicted in <FIG>, the vehicle computing system <NUM> can include the one or more sensors <NUM>; the positioning system <NUM>; the autonomy computing system <NUM>; the communication system <NUM>; the vehicle control system <NUM>; and the human-machine interface <NUM>. One or more of these systems can be configured to communicate with one another via a communication channel. The communication channel can include one or more data buses (e.g., controller area network (CAN)), on-board diagnostics connector (e.g., OBD-II), and/or a combination of wired and/or wireless communication links. The onboard systems can exchange (e.g., send and/or receive) data, messages, and/or signals amongst one another via the communication channel.

The one or more sensors <NUM> can be configured to generate and/or store data including the sensor data <NUM> associated with one or more objects proximate to the vehicle <NUM> (e.g., within range or a field of view of one or more of the one or more sensors <NUM>). The one or more sensors <NUM> can include one or more Light Detection and Ranging (LiDAR) systems, one or more Radio Detection and Ranging (RADAR) systems, one or more cameras (e.g., visible spectrum cameras and/or infrared cameras), one or more sonar systems, one or more motion sensors, and/or other types of image capture devices and/or sensors. The sensor data <NUM> can include image data, radar data, LiDAR data, sonar data, and/or other data acquired by the one or more sensors <NUM>. The one or more objects can include, for example, pedestrians, vehicles, bicycles, buildings, roads, foliage, utility structures, bodies of water, and/or other objects. The one or more objects can be located on or around (e.g., in the area surrounding the vehicle <NUM>) various parts of the vehicle <NUM> including a front side, rear side, left side, right side, top, or bottom of the vehicle <NUM>. The sensor data <NUM> can be indicative of a location of the one or more objects within the surrounding environment of the vehicle <NUM> at one or more times. For example, sensor data <NUM> can be indicative of one or more LiDAR point clouds associated with the one or more objects within the surrounding environment. The one or more sensors <NUM> can provide the sensor data <NUM> to the autonomy computing system <NUM>.

In addition to the sensor data <NUM>, the autonomy computing system <NUM> can retrieve or otherwise obtain data, including the map data <NUM>. The map data <NUM> can provide detailed information about the surrounding environment of the vehicle <NUM>. For example, the map data <NUM> can provide information regarding: the identity and/or location of different roadways, road segments, buildings, or other items or objects (e.g., lampposts, crosswalks and/or curbs); the location and directions of traffic lanes (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway or other travel way and/or one or more boundary markings associated therewith); traffic control data (e.g., the location and instructions of signage, traffic lights, or other traffic control devices); and/or any other map data that provides information that assists the vehicle computing system <NUM> in processing, analyzing, and perceiving its surrounding environment and its relationship thereto.

The positioning system <NUM> can determine a current position of the vehicle <NUM>. The positioning system <NUM> can be any device or circuitry for analyzing the position of the vehicle <NUM>. For example, the positioning system <NUM> can determine a position by using one or more of inertial sensors, a satellite positioning system, based on IP/MAC address, by using triangulation and/or proximity to network access points or other network components (e.g., cellular towers and/or Wi-Fi access points) and/or other suitable techniques. The position of the vehicle <NUM> can be used by various systems of the vehicle computing system <NUM> and/or provided to one or more remote computing devices (e.g., the operations computing system <NUM> and/or the remote computing devices <NUM>). For example, the map data <NUM> can provide the vehicle <NUM> relative positions of the surrounding environment of the vehicle <NUM>. The vehicle <NUM> can identify its position within the surrounding environment (e.g., across six axes) based at least in part on the data described herein. For example, the vehicle <NUM> can process the sensor data <NUM> (e.g., LiDAR data, camera data) to match it to a map of the surrounding environment to get a determination of the vehicle's position within that environment (e.g., transpose the vehicle's position within its surrounding environment).

The autonomy computing system <NUM> can include a perception system <NUM>, a prediction system <NUM>, a motion planning system <NUM>, and/or other systems that cooperate to perceive the surrounding environment of the vehicle <NUM> and determine a motion plan for controlling the motion of the vehicle <NUM> accordingly. For example, the autonomy computing system <NUM> can receive the sensor data <NUM> from the one or more sensors <NUM>, attempt to determine the state of the surrounding environment by performing various processing techniques on the sensor data <NUM> (and/or other data), and generate an appropriate motion plan through the surrounding environment, including for example, a motion plan that navigates the vehicle <NUM> around the current and/or predicted locations of one or more objects detected by the one or more sensors <NUM>. The autonomy computing system <NUM> can control the one or more vehicle control systems <NUM> to operate the vehicle <NUM> according to the motion plan.

The autonomy computing system <NUM> can identify one or more objects that are proximate to the vehicle <NUM> based at least in part on the sensor data <NUM> and/or the map data <NUM>. For example, the perception system <NUM> can obtain perception data <NUM> descriptive of a current and/or past state of an object that is proximate to the vehicle <NUM>. The perception data <NUM> for each object can describe, for example, an estimate of the object's current and/or past: location and/or position; speed; velocity; acceleration; heading; orientation; size/footprint (e.g., as represented by a bounding shape); class (e.g., pedestrian class vs. vehicle class vs. bicycle class), and/or other state information. The perception system <NUM> can provide the perception data <NUM> to the prediction system <NUM> (e.g., for predicting the movement of an object).

The prediction system <NUM> can generate prediction data <NUM> associated with each of the respective one or more objects proximate to the vehicle <NUM>. The prediction data <NUM> can be indicative of one or more predicted future locations of each respective object. The prediction data <NUM> can be indicative of a predicted path (e.g., predicted trajectory) of at least one object within the surrounding environment of the vehicle <NUM>. For example, the predicted path (e.g., trajectory) can indicate a path along which the respective object is predicted to travel over time (and/or the velocity at which the object is predicted to travel along the predicted path). The prediction system <NUM> can provide the prediction data <NUM> associated with the one or more objects to the motion planning system <NUM>.

In some implementations, the prediction system <NUM> can utilize one or more machine-learned models. For example, the prediction system <NUM> can determine prediction data <NUM> including a predicted trajectory (e.g., a predicted path, one or more predicted future locations, etc.) along which a respective object is predicted to travel over time based on one or more machine-learned models. By way of example, the prediction system <NUM> can generate such predictions by including, employing, and/or otherwise leveraging a machine-learned prediction model. For example, the prediction system <NUM> can receive perception data <NUM> (e.g., from the perception system <NUM>) associated with one or more objects within the surrounding environment of the vehicle <NUM>. The prediction system <NUM> can input the perception data <NUM> (e.g., BEV image, LIDAR data, etc.) into the machine-learned prediction model to determine trajectories of the one or more objects based on the perception data <NUM> associated with each object. For example, the machine-learned prediction model can be previously trained to output a future trajectory (e.g., a future path, one or more future geographic locations, etc.) of an object within a surrounding environment of the vehicle <NUM>. In this manner, the prediction system <NUM> can determine the future trajectory of the object within the surrounding environment of the vehicle <NUM> based, at least in part, on the machine-learned prediction generator model.

As discussed above, the machine-learned prediction model can be previously trained via one or more machine-learning techniques. In some implementations, the machine-learned prediction model can be previously trained by one or more devices (e.g., training computing system, operations computing system <NUM>, one or more remote computing devices <NUM>, etc.) remote from the vehicle <NUM>.

The motion planning system <NUM> can determine a motion plan and generate motion plan data <NUM> for the vehicle <NUM> based at least in part on the prediction data <NUM> (and/or other data). The motion plan data <NUM> can include vehicle actions with respect to the objects proximate to the vehicle <NUM> as well as the predicted movements. For instance, the motion planning system <NUM> can implement an optimization algorithm that considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, and/or other aspects of the environment), if any, to determine optimized variables that make up the motion plan data <NUM>. By way of example, the motion planning system <NUM> can determine that the vehicle <NUM> can perform a certain action (e.g., pass an object) without increasing the potential risk to the vehicle <NUM> and/or violating any traffic laws (e.g., speed limits, lane boundaries, signage). The motion plan data <NUM> can include a planned trajectory, velocity, acceleration, and/or other actions of the vehicle <NUM>.

The motion planning system <NUM> can provide the motion plan data <NUM> with data indicative of the vehicle actions, a planned trajectory, and/or other operating parameters to the vehicle control systems <NUM> to implement the motion plan data <NUM> for the vehicle <NUM>. For instance, the vehicle <NUM> can include a mobility controller configured to translate the motion plan data <NUM> into instructions. In some implementations, the mobility controller can translate determined motion plan data <NUM> into instructions for controlling the vehicle <NUM> including adjusting the steering of the vehicle <NUM> "X" degrees and/or applying a certain magnitude of braking force. The mobility controller can send one or more control signals to the responsible vehicle control component (e.g., braking control system, steering control system and/or acceleration control system) to execute the instructions and implement the motion plan data <NUM>.

The vehicle computing system <NUM> can include a communications system <NUM> configured to allow the vehicle computing system <NUM> (and its one or more computing devices) to communicate with other computing devices. The vehicle computing system <NUM> can use the communications system <NUM> to communicate with the operations computing system <NUM> and/or one or more other remote computing devices (e.g., the one or more remote computing devices <NUM>) over one or more networks (e.g., via one or more wireless signal connections). In some implementations, the communications system <NUM> can allow communication among one or more of the system on-board the vehicle <NUM>. The communications system <NUM> can also be configured to enable the autonomous vehicle to communicate with and/or provide and/or receive data and/or signals from a remote computing device <NUM> associated with a user and/or an item (e.g., an item to be picked-up for a courier service). The communications system <NUM> can utilize various communication technologies including, for example, radio frequency signaling and/or Bluetooth low energy protocol. The communications system <NUM> can include any suitable components for interfacing with one or more networks, including, for example, one or more: transmitters, receivers, ports, controllers, antennas, and/or other suitable components that can help facilitate communication. In some implementations, the communications system <NUM> can include a plurality of components (e.g., antennas, transmitters, and/or receivers) that allow it to implement and utilize multiple-input, multiple-output (MIMO) technology and communication techniques.

The vehicle computing system <NUM> can include the one or more human-machine interfaces <NUM>. For example, the vehicle computing system <NUM> can include one or more display devices located on the vehicle computing system <NUM>. A display device (e.g., screen of a tablet, laptop and/or smartphone) can be viewable by a user of the vehicle <NUM> that is located in the front of the vehicle <NUM> (e.g., driver's seat, front passenger seat). Additionally, or alternatively, a display device can be viewable by a user of the vehicle <NUM> that is located in the rear of the vehicle <NUM> (e.g., a back passenger seat). For example, the autonomy computing system <NUM> can provide one or more outputs including a graphical display of the location of the vehicle <NUM> on a map of a geographical area within one kilometer of the vehicle <NUM> including the locations of objects around the vehicle <NUM>. A passenger of the vehicle <NUM> can interact with the one or more human-machine interfaces <NUM> by touching a touchscreen display device associated with the one or more human-machine interfaces.

In some implementations, the vehicle computing system <NUM> can perform one or more operations including activating, based at least in part on one or more signals or data (e.g., the sensor data <NUM>, the map data <NUM>, the perception data <NUM>, the prediction data <NUM>, and/or the motion plan data <NUM>) one or more vehicle systems associated with operation of the vehicle <NUM>. For example, the vehicle computing system <NUM> can send one or more control signals to activate one or more vehicle systems that can be used to control and/or direct the travel path of the vehicle <NUM> through an environment.

By way of further example, the vehicle computing system <NUM> can activate one or more vehicle systems including: the communications system <NUM> that can send and/or receive signals and/or data with other vehicle systems, other vehicles, or remote computing devices (e.g., remote server devices); one or more lighting systems (e.g., one or more headlights, hazard lights, and/or vehicle compartment lights); one or more vehicle safety systems (e.g., one or more seatbelt and/or airbag systems); one or more notification systems that can generate one or more notifications for passengers of the vehicle <NUM> (e.g., auditory and/or visual messages about the state or predicted state of objects external to the vehicle <NUM>); braking systems; propulsion systems that can be used to change the acceleration and/or velocity of the vehicle which can include one or more vehicle motor or engine systems (e.g., an engine and/or motor used by the vehicle <NUM> for locomotion); and/or steering systems that can change the path, course, and/or direction of travel of the vehicle <NUM>.

Referring now to <FIG>, a block diagram of a LIDAR system <NUM> is provided according to example embodiments of the present disclosure. It should be understood that the LIDAR system <NUM> can be included as part of the sensors <NUM> discussed above with reference to <FIG>. As shown, the LIDAR system <NUM> can include multiple channels <NUM>; specifically, channels <NUM> - N are illustrated. The LIDAR system <NUM> can include one or more LIDAR units. Thus, the channels <NUM>-N can be included in a single LIDAR unit or may be spread across multiple LIDAR units. Each channel <NUM> can output point data that provides a single point of ranging information. The point data output by each of the channels <NUM> (i.e., point data<NUM>-N) can combined to create a point cloud that corresponds to a three-dimensional representation of the surrounding environment.

As shown, each channel <NUM> can include an emitter <NUM> paired with a receiver <NUM>. The emitter <NUM> emits a laser signal into the environment that is reflected off the surrounding environment and returned back to a detector <NUM> (e.g., an optical detector) of the receiver <NUM>. Each emitter <NUM> can have an adjustable power level that controls an intensity of the emitted laser signal. The adjustable power level allows the emitter <NUM> to be capable of emitting the laser signal at one of multiple different power levels (e.g., intensities).

The detector <NUM> can provide the return signal to a read-out circuit <NUM>. The read-out circuit <NUM> can, in turn, output the point data based on the return signal. The point data can indicate a distance the LIDAR system <NUM> is from a detected object (e.g., road, pedestrian, vehicle, etc.) that is determined by the read-out circuit <NUM> by measuring time-of-flight (ToF), which is the time elapsed time between the emitter <NUM> emitting the laser signal and the receiver <NUM> detecting the return signal.

The point data further includes an intensity value corresponding to each return signal. The intensity value indicates a measure of intensity of the return signal determined by the read-out circuit <NUM>. As noted above, the intensity of the return signal provides information about the surface reflecting the signal and can be used by the autonomy computing system <NUM> (<FIG>) for localization, perception, prediction, and motion planning. The intensity of the return signals depends on a number of factors, such as the distance of the LIDAR system <NUM> to the detected object, the angle of incidence at which the emitter <NUM> emits the laser signal, temperature of the surrounding environment, the alignment of the emitter <NUM> and the receiver <NUM>, and the reflectivity of the detected surface.

As shown, a reflectivity processing system <NUM> receives the point data from the LIDAR system <NUM> and processes the point data to classify specular reflectivity characteristics of objects. The reflectivity processing system <NUM> classifies the specular reflectivity characteristics of objects based on a comparison of reflectivity values derived from intensity values of return signals. In some embodiments, the LIDAR system <NUM> can be calibrated to produce the reflectivity values. For example, the read-out circuit <NUM> or another component of the LIDAR system <NUM> can be configured to normalize the intensity values to produce the reflectivity values. In these embodiments, the reflectivity values may be included in the point data received by the reflectivity processing system <NUM> from the LIDAR system <NUM>. In other embodiments, the reflectivity processing system <NUM> may generate the reflectivity values based on intensity return values included in the point data received from the LIDAR system <NUM>.

Regardless of which component is responsible for generating the reflectivity values, the process for doing so may, in some embodiments, include using a linear model to compute one or more calibration multipliers and one or more bias values to be applied to return intensity values. Depending on the embodiment, a calibration multiplier and bias value may be computed for and applied to each channel of the LIDAR system <NUM> at each power level. The linear model assumes a uniform diffuse reflectivity for all surfaces and describes an expected intensity value as a function of a raw intensity variable, a calibration multiplier variable, and/or a bias variable. The computing of the calibration multiplier and bias value for each channel/power level combination includes determining a median intensity value based on the raw intensity values output by the channel at the power level and using the median intensity value as the expected intensity value in the linear model while optimizing values for the calibration multiplier variable and bias variable. As an example, the calibration multiplier and bias value may be computed by solving the linear model using an Iterated Re-weighted Least Squares approach.

The calibration multiplier and bias value computed for each channel <NUM> at each power level can be assigned to the corresponding channel/power level combination. In this way, each power level of each channel of the LIDAR system <NUM> can have an independently assigned calibration multiplier and bias value from which reflectivity values may be derived. Once assigned, the calibration multiplier and bias value of each channel/power level combination can be used at run-time to determine reflectivity values from subsequent intensity values produced by the corresponding channel at the corresponding power level during operation of an autonomous or semi-autonomous vehicle. More specifically, reflectivity values can be determined from the linear model by using the value of the calibration multiplier and the bias value for the calibration multiplier variable and bias variable, respectively. In this manner, the intensity values can be normalized to be more aligned with the reflectivity of a surface by taking into account factors such as the distance of the LIDAR system <NUM> to the detected surface, the angle of incidence at which the emitter <NUM> emits the laser signal, temperature of the surrounding environment, and/or the alignment of the emitter <NUM> and the receiver <NUM>.

Referring now to <FIG>, components of the emitter <NUM> of the LIDAR system <NUM> and the receiver <NUM> of the LIDAR system <NUM> are provided according to example embodiments of the present disclosure. In some implementations, the emitter <NUM> can include a light source <NUM>, a first lens <NUM> and a second lens <NUM> disposed along a transmit path <NUM>. As shown, the first lens <NUM> can be positioned between the light source <NUM> and the second lens <NUM> along the transmit path <NUM>. Although the emitter <NUM> is depicted as having two lenses (e.g., first lens <NUM> and second lens <NUM>) disposed along the transmit path <NUM>, it should be understood that the emitter <NUM> can include more or fewer lenses disposed along the transmit path <NUM>. For instance, in some implementations, the emitter <NUM> can include only one lens disposed along the transmit path <NUM>. In alternative implementations, the emitter <NUM> can include more than two lenses disposed along the transmit path <NUM>.

In some implementations, the light source <NUM> can include a laser diode. The light source <NUM> can be configured to emit a primary laser beam <NUM> through the first lens <NUM> and then the second lens <NUM> to provide a transmit beam <NUM>. In some implementations, a divergence angle <NUM> associated with the transmit beam <NUM> can be about <NUM>. degrees or less. It should be understood that the divergence angle <NUM> is indicative of an increase in beam diameter or radius of the transmit beam <NUM> with distance from the second lens <NUM>. Stated another way, the divergence angle <NUM> of the transmit beam can be an angle subtended by the transmit beam <NUM> exiting the last lens (e.g., the second lens <NUM>) in the transmit path <NUM>.

In some implementations, the first lens <NUM> and the second lens <NUM> can each be a collimation lens. For instance, the first lens <NUM> can be a fast-axis collimation lens. Conversely, the second lens <NUM> can be a slow-axis collimation lens. In such implementations, the first lens <NUM> (e.g., fast axis collimation lens) can be configured to reduce a divergence angle associated with the primary laser beam <NUM>. In particular, the first lens <NUM> can be configured to reduce the divergence angle such that the primary laser beam <NUM> is directed towards the second lens <NUM> (e.g., slow-axis collimation lens).

As shown, receiver <NUM> can be spaced apart from the emitter <NUM>. In some implementations, the emitter <NUM> and the receiver <NUM> can be coupled to a vehicle body of the autonomous vehicle such that the emitter <NUM> and the receiver <NUM> are spaced apart from one another along a vertical direction of the autonomous vehicle <NUM> (<FIG>) or a lateral direction of the autonomous vehicle <NUM>. In particular, the emitter <NUM> and the receiver <NUM> can be positioned on the autonomous vehicle <NUM> such that the emitter <NUM> and the receiver <NUM> are parallel to one another.

In some implementations, the receiver <NUM> can include the detector <NUM>, a first lens <NUM>, and a second lens <NUM> disposed along a receive path <NUM>. As shown, the first lens <NUM> of the receiver <NUM> can be positioned between the detector <NUM> and the second lens <NUM> of the receiver <NUM> along the receive path <NUM>. Furthermore, although the receiver <NUM> is depicted as having two lenses (e.g., first lens <NUM> and second lens <NUM>) disposed along the receive path <NUM>, it should be understood that the receiver <NUM> can include more or fewer lenses disposed along the receive path <NUM>. For instance, in some implementations, the receive <NUM> can include only one lens disposed along the receive path <NUM>. In alternative implementations, the receiver <NUM> can include more than two lenses disposed along the receive path <NUM>.

The lenses (e.g., first lens <NUM>, second lens <NUM>) of the receiver <NUM> can receive a reflected laser beam <NUM> corresponding to a reflection of the transmit beam <NUM>. For instance, the reflected laser beam <NUM> can be the transmit beam <NUM> reflecting off one or more objects within a surrounding environment in which the LIDAR system <NUM> is operating. Furthermore, the lenses of the receiver <NUM> can be configured to direct the reflected laser beam <NUM> onto the detector <NUM>. The detector <NUM> can be configured to process the reflected laser beam to generate a point cloud indicative of the surrounding environment in which the LIDAR system <NUM> is operating. The detector <NUM> can be, for instance, an avalanche photodiode (APD).

Referring now to <FIG>, in some implementations, the LIDAR system <NUM> can include an optical element <NUM> disposed along the transmit path <NUM>. The optical element <NUM> can be configured to divert a portion of the primary laser beam <NUM> towards the receive path <NUM> (e.g., shown in <FIG>) as a secondary laser beam <NUM>. An amount of energy associated with the secondary laser beam <NUM> can be less than an amount of energy associated with the primary laser beam <NUM>. For instance, in some implementations, the amount of energy associated with the secondary laser beam <NUM> can be less than about <NUM> percent of a total amount of energy associated with the primary laser beam <NUM>. Additionally, a divergence angle <NUM> associated with the secondary laser beam <NUM> can be different than the divergence angle <NUM> associated with the transmit beam <NUM>. For instance, in some implementations, the divergence angle <NUM> of the secondary laser beam <NUM> can range from about <NUM> degrees to about <NUM> degrees. In this manner, the secondary laser beam <NUM> can spread out with distance and therefore be less focused than the transmit beam <NUM>. It should be understood that the divergence angle <NUM> of the secondary laser beam <NUM> can be an angle subtended by the secondary laser beam <NUM> exiting the optical element <NUM>.

In some implementations, the optical element <NUM> can form at least a portion of one of the lenses (e.g., first lens <NUM>, second lens <NUM>) disposed in the transmit path <NUM>. For instance, in some implementations, the first lens <NUM> or the second lens <NUM> can be a bifocal lens. In such implementations, the optical element <NUM> can include a portion of the bifocal lens. In particular, the bifocal lens can be configured to direct a majority of the primary laser beam <NUM> along the transmit path <NUM> as the transmit beam <NUM>. The bifocal lens can be further configured to direct a minority of the primary laser beam <NUM> towards the receive path <NUM> as the secondary laser beam <NUM>.

In some implementations, the first lens <NUM> in the transmit path <NUM> can be a fast-axis collimation lens. In such implementations, the optical element <NUM> can form at least a portion of the fast-axis collimation lens. For instance, a portion of the fast-axis collimation lens can be configured to direct a portion of the primary laser beam <NUM> towards the receive path <NUM> as the secondary laser beam <NUM>. Furthermore, the fast-axis collimation lens can be configured to spread the secondary laser beam <NUM> to reduce or eliminate a parallax error associated with detecting objects in a near-field (e.g., about <NUM> meters in front of the receiver <NUM>) associated with the LIDAR system <NUM>.

In some implementations, the second lens <NUM> in the transmit path <NUM> can be a slow-axis collimation lens. In such implementations, the optical element <NUM> can form at least a portion of the slow-axis collimation lens. In such implementations, the slow-axis collimation lens can be configured to direct a portion of the primary laser beam <NUM> towards the receive path <NUM> as the secondary laser beam <NUM>. Furthermore, the slow-axis collimation lens can be configured to spread the secondary laser beam <NUM> to reduce or eliminate the parallax error associated with detecting objects in the near-field associated with the LIDAR system <NUM>.

In some implementations, the optical element <NUM> can, as shown in <FIG>, be separate from the first lens <NUM> and the second lens <NUM> of emitter <NUM>. As shown, the optical element <NUM> can be disposed between the first lens <NUM> and the second lens <NUM> along the transmit path <NUM>. For instance, the optical element <NUM> can include a divergence lens (e.g., wedge prism) that is positioned between the first lens <NUM> and the second lens <NUM>. In such implementations, the optical element <NUM> (e.g., third lens) can be configured to direct a portion of the primary laser beam <NUM> towards the receive path <NUM> as the secondary laser beam <NUM>. Furthermore, the optical element <NUM> can be configured to spread the secondary laser beam <NUM> to reduce or eliminate the parallax error associated with detecting objects in the near-field associated with the LIDAR system <NUM>.

In some implementations, the optical element <NUM> can include a diffuser. The diffuser can spread the primary laser beam <NUM> such that a portion of the primary laser beam <NUM> is directed towards the receive path <NUM> as the secondary laser beam <NUM>. In some implementations, the diffuser can form at least a portion of the lenses (e.g., first lens <NUM>, second lens <NUM>) disposed in the transmit path <NUM>. For instance, the diffuser can form at least a portion of the second lens <NUM>.

In such implementations, the diffuser can correspond to a portion of a surface of the second lens <NUM> that is modified relative to the remainder of the surface. For example, the portion of the surface of the second lens <NUM> can be roughened relative to the remainder of the surface. In this manner, light (e.g., primary laser beam <NUM>) passing through the portion of the surface can spread and be directed towards the receive path <NUM> as the secondary laser beam <NUM>. As another example, the diffuser can include a coating that is applied to at least a portion of the surface of the second lens <NUM>. In this manner, light (e.g., primary laser beam <NUM>) passing through the portion of the surface of the second lens <NUM> to which the coating has been applied can spread and be directed towards the receive path <NUM> as the secondary laser beam <NUM>.

It should be understood that the portion of the surface of the second lens <NUM> that is modified relative to the remainder of the surface can correspond to any suitable surface of the second lens <NUM>. For instance, in some implementations, the surface can be associated with an edge of the second lens <NUM>. It should also be understood that, in some implementations, the diffuser can be separate (e.g., standalone) from the lenses (e.g., first lens <NUM>, second lens <NUM>) disposed in the transmit path <NUM>.

Referring now to <FIG>, the LIDAR system <NUM> can, in some implementations, be implemented onboard the autonomous vehicle <NUM> according to example embodiments of the present disclosure. As shown, the LIDAR system <NUM> can be operable while the autonomous vehicle <NUM> is traveling along a road <NUM>. In particular, the emitter <NUM> emits the transmit beam <NUM> to or towards a far-field <NUM> associated with the LIDAR system <NUM>. Additionally, the emitter <NUM> emits the secondary laser beam <NUM> to or towards a near-field associated with the LIDAR system <NUM>. It should be understood that the near-field <NUM> corresponds to an area ranging from about <NUM> meters in front of the receiver <NUM> to about <NUM> meters in front of the receiver <NUM>. It should also be understood that the far-field corresponds to any area that is beyond the near-field <NUM>. For instance, in some implementations, the far-field <NUM> can correspond to any area that is greater than about <NUM> meters in front of the receiver <NUM>.

The secondary laser beam <NUM> can spread within near-field <NUM>. In this manner, the secondary laser beam <NUM> can reflect off of objects (e.g., pedestrians, vehicles, bicycles/bicyclists, etc.) within the near-field <NUM> and can be directed onto the receiver <NUM>, specifically the detector <NUM> (shown in <FIG>) thereof. Furthermore, since the secondary laser beam <NUM> spreads within the near-field <NUM>, light reflected off the objects with the near-field <NUM> and directed onto the detector <NUM> can be increased. This can lead to increased capability of the LIDAR system <NUM> to detect objects within the near-field <NUM> that may otherwise not be detected due to the parallax error.

Referring now to <FIG>, a flowchart diagram of an example method <NUM> of controlling operation of a LIDAR system is provided according to example embodiments of the present disclosure. The method <NUM> can be implemented using the LIDAR system <NUM> discussed above with reference to <FIG>. Furthermore, <FIG> depicts elements performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein can be adapted, rearranged, expanded, omitted, combined, and/or modified in various ways without deviating from the scope of the present disclosure.

At (<NUM>), the method <NUM> can include emitting, via an emitter of the LIDAR system, a primary laser beam through one or more lenses disposed along a transmit path to provide a transmit beam. In some implementations, the emitter of the LIDAR can include a light source configured to emit the primary laser beam. For instance, the light source can include a laser diode.

At (<NUM>), the method <NUM> can include directing, via an optical element disposed along the transmit path, a portion of the primary laser beam towards a receive path as the secondary laser beam. In some implementations, the optical element can form at least a portion of the one or more lenses disposed in the transmit path. For instance, in some implementations, the one or more lenses disposed in the transmit path can include a bifocal lens. In such implementations, the optical element can include a portion of the bifocal lens that is configured to divert the primary laser beam towards a receive path as a secondary laser beam. In some implementations, the optical element can include a diffuser that forms at least a portion of a surface of the one or more lenses disposed on the transmit path. In alternative implementations, the optical element can be separate from the one or more lenses disposed in the transmit path. For instance, in some implementations, the optical element can include a diffuser that is separate (e.g., standalone) from the one or more lenses. Alternatively, the optical element can include a divergence lens (e.g., wedge prism) that is separate from the one or more lenses disposed in the transmit path. For instance, the divergence lens can be positioned between a first lens (e.g., fast-axis collimation lens) and a second lens (e.g., slow-axis collimation lens) disposed in the transmit path.

At (<NUM>), the method <NUM> can include receiving, via one or more lenses of the receiver, a reflected laser beam. The reflected laser beam can include a reflected secondary laser beam. Alternatively, the reflected laser beam can include a reflected transmit beam. At (<NUM>), the method <NUM> can include generating, via a detector of the receiver, a point cloud based, at least in part, on data associated with the reflected laser beam.

Referring now to <FIG>, a flowchart diagram of an example method <NUM> of controlling operation of an autonomous vehicle having a LIAR system is provided according to example embodiments of the present disclosure. One or more portion(s) of the method <NUM> can be implemented by a computing system that includes one or more computing devices such as, for example, the computing systems described with reference to the other figures (e.g., the vehicle computing system <NUM>, the operations computing system <NUM>, the one or more remote computing devices <NUM>, etc.). Each respective portion of the method <NUM> can be performed by any (or any combination) of one or more computing devices. Moreover, one or more portion(s) of the method <NUM> can be implemented as an algorithm on the hardware components of the device(s) described herein to, for example, control operation of the autonomous vehicle according to data obtained from the LIDAR system. <FIG> depicts elements performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein can be adapted, rearranged, expanded, omitted, combined, and/or modified in various ways without deviating from the scope of the present disclosure. <FIG> is described with reference to elements/terms described with respect to other systems and figures for exemplary illustrated purposes and is not meant to be limiting. One or more portions of method <NUM> can be performed additionally, or alternatively, by other systems.

At (<NUM>), the method <NUM> can include obtaining, via the LIDAR system, sensor data indicative of an object within a near-field associated with the LIDAR system.

At (<NUM>), the method <NUM> can include determining perception data for the object within the near-field based, at least in part, on the sensor data obtained at (<NUM>). The perception data can describe, for example, an estimate of the object's current and/or past: location and/or position; speed; velocity; acceleration; heading; orientation; size/footprint (e.g., as represented by a bounding shape); class (e.g., pedestrian class vs. vehicle class vs. bicycle class); and/or other state information.

At (<NUM>), the method <NUM> can include determining one or more future locations of the object based, at least in part, on the perception data for the object within the near-field associated with the LIDAR system. For example, the autonomous vehicle can generate a trajectory (e.g., including one or more waypoints) that is indicative of a predicted future motion of the object, given the object's heading, velocity, type, etc. over current/previous timestep(s).

At (<NUM>), the method <NUM> can include determining an action for the autonomous vehicle based at least in part on the one or more future locations of the object within the near-field associated with the LIDAR system. For example, the autonomous vehicle can generate a motion plan that includes a vehicle trajectory by which the vehicle can travel to avoid interfering/colliding with the object. In another example, the autonomous vehicle can determine that the object is a user that intends to enter the autonomous vehicle (e.g., for a human transportation service) and/or that intends place an item in the autonomous vehicle (e.g., for a courier/delivery service). The autonomous vehicle can unlock a door, trunk, etc. to allow the user to enter and/or place an item within the vehicle. The autonomous vehicle can communicate one or more control signals (e.g., to a motion control system, door control system, etc.) to initiate the determined actions.

<FIG> depicts example system components of an example computing system <NUM> according to example embodiments of the present disclosure. The example computing system <NUM> can include the vehicle computing system <NUM> and one or more remote computing system(s) <NUM> that are communicatively coupled to the vehicle computing system <NUM> over one or more network(s) <NUM>. The computing system <NUM> can include one or more computing device(s) <NUM>. The computing device(s) <NUM> of the vehicle computing system <NUM> can include processor(s) <NUM> and a memory <NUM>. The one or more processors <NUM> can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory <NUM> can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

The memory <NUM> can store information that can be accessed by the one or more processors <NUM>. For instance, the memory <NUM> (e.g., one or more non-transitory computer-readable storage mediums, memory devices) can include computer-readable instructions <NUM> that can be executed by the one or more processors <NUM>. The instructions <NUM> can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions <NUM> can be executed in logically and/or virtually separate threads on processor(s) <NUM>.

For example, the memory <NUM> can store instructions <NUM> that when executed by the one or more processors <NUM> cause the one or more processors <NUM> to perform operations such as any of the operations and functions for which the computing systems) are configured, as described herein.

The memory <NUM> can store data <NUM> that can be obtained, received, accessed, written, manipulated, created, and/or stored. The data <NUM> can include, for instance, sensor data obtained via the LIDAR system <NUM> (shown in <FIG>), and/or other data/information described herein. In some implementations, the computing device(s) <NUM> can obtain from and/or store data in one or more memory device(s) that are remote from the computing system <NUM> such as one or more memory devices of the remote computing system <NUM>.

The computing device(s) <NUM> can also include a communication interface <NUM> used to communicate with one or more other system(s) (e.g., remote computing system <NUM>). The communication interface <NUM> can include any circuits, components, software, etc. for communicating via one or more networks (e.g., <NUM>). In some implementations, the communication interface <NUM> can include for example, one or more of a communications controller, receiver, transceiver, transmitter, port, conductors, software and/or hardware for communicating data/information.

The network(s) <NUM> can be any type of network or combination of networks that allows for communication between devices. In some embodiments, the network(s) <NUM> can include one or more of a local area network, wide area network, the Internet, secure network, cellular network, mesh network, peer-to-peer communication link and/or some combination thereof and can include any number of wired or wireless links. Communication over the network(s) <NUM> can be accomplished, for instance, via a network interface using any type of protocol, protection scheme, encoding, format, packaging, etc..

Claim 1:
A light detection and ranging, LIDAR, system (<NUM>) comprising:
an emitter (<NUM>) comprising a light source (<NUM>) and one or more lenses (<NUM>, <NUM>) positioned along a transmit path (<NUM>), the light source configured to emit a primary laser beam (<NUM>) through the one or more lenses in the transmit path to provide a transmit beam (<NUM>) to or towards a far-field (<NUM>) associated with the LIDAR system (<NUM>);
a receiver (<NUM>) spaced apart from the emitter, the receiver comprising one or more lenses (<NUM>, <NUM>) positioned along a receive path (<NUM>) such that the one or more lenses of the receiver receive a reflected laser beam (<NUM>); and
an optical element (<NUM>) positioned along the transmit path, the optical element configured to direct a portion of the primary laser beam (<NUM>) in a direction towards the receive path (<NUM>) as a secondary laser beam (<NUM>) that is emitted to or towards a near-field associated with the LIDAR system (<NUM>) corresponding to an area ranging from about <NUM> meters in front of the receiver (<NUM>) to about <NUM> meters in front of the receiver (<NUM>),
wherein the far-field associated with the LIDAR system corresponds to an area that is greater than about <NUM> meters in front of the receiver (<NUM>).