Blooming perception

Abstract

A system for light ranging and detection (LiDAR) is described. The system comprises image processing circuitry operative to generate point cloud data based on an image of return light that is a consequence of a light transmission in a field-of-view (FOV) captured by an optical receiver, where the point cloud data comprises one or more blooming features. The system further comprises feature extraction circuitry coupled to the image processing circuitry. The feature extraction circuitry is operative to weight probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features. The feature extraction circuitry is further operative to provide the weight probabilities to recognition circuitry operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities.

Description

BLOOMING PERCEPTIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 541,738, filed September 29, 2023, entitled “BLOOMING PERCEPTION,” the content of which is hereby incorporated by reference in its entirety for all purposes.FIELD OF THE TECHNOLOGY

[0002] This disclosure relates generally to light transmitting / detection and, more particularly, to a method of blooming perception in a LiDAR system.BACKGROUND

[0003] Light detection and ranging (LiDAR) systems use light pulses to create an image or point cloud of the external environment. A LiDAR system may be a scanning or non-scanning system. Some typical scanning LiDAR systems include a light source, a light transmitter, a light steering system, and a light detector. The light source generates a light beam that is directed by the light steering system in particular directions when being transmitted from the LiDAR system. When a transmitted light beam is scattered or reflected by an object, a portion of the scattered or reflected light returns to the LiDAR system to form a return light pulse. The light detector detects the return light pulse. Using the difference between the time that the return light pulse is detected and the time that a corresponding light pulse in the light beam is transmitted, the LiDAR system can determine the distance to the object based on the speed of light. This technique of determining the distance is referred to as the time-of-flight (ToF) technique. The light steering system can direct light beams along different paths to allow the LiDAR system to scan the surrounding environment and produce images or point clouds. A typical non-scanning LiDAR system illuminate an entire field-of-view (FOV) rather than scanning through the FOV. An example of the non-scanning LiDAR system is a flash LiDAR, which can also use the ToF technique to measure the distance to an object. LiDAR systems can also use techniques other than time-of-flight and scanning to measure the surrounding environment.SUMMARY

[0004] LiDAR is an active sensor which emits laser light pulses and measures the range between the sensor and the object. The output data of a LiDAR is called point cloud. The LiDAR is like a 3D camera, except each pixel presents the distance instead of color.

[0005] Because the LiDAR is shooting the laser onto the target, the point cloud may have a problem when the reflectivity of the target is too high. For example, a retro-reflective target will reflect most of the light back to LiDAR, and the target will look larger than it should be in the point cloud. We call this phenomenon blooming. In the point cloud image, a high-retro target may look much taller than it should be because of the blooming.

[0006] Blooming may be caused by single pixel enlargement due to laser beam expansion and / or optical crosstalk among different pixels. Because the return light is too strong, pixels in other channels also receive the light from the target channels.

[0007] Various blooming cancellation methods are typically used in LiDAR systems to improve the image quality of the point cloud. Particularly, blooming cancellation is used to recover the actual profde of the target. This is commonly required in the perception portion of the LiDAR system workflow to help determine object features.

[0008] However, the embodiments herein propose an alternative method. Instead of cancelling the blooming, we take advantage of blooming to help to perceive object features. By taking advantage of the blooming, the perception accuracy can be improved because of the unique pattern(s) of particular target objects. Particularly, the point cloud image without blooming cancellation is passed to an object feature extraction element directly, where the blooming features comprise both shape and intensity distribution, and the blooming feature extraction element outputs the result array directly.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The present application can be best understood by reference to the embodiments described below taken in conjunction with the accompanying drawing figures, in which like parts may be referred to by like numerals.

[0010] FIG. 1 illustrates one or more example LiDAR systems disposed or included in a motor vehicle.

[0011] FIG. 2 is a block diagram illustrating interactions between an example LiDAR system and multiple other systems including a vehicle perception and planning system.

[0012] FIG. 3 is a block diagram illustrating an example LiDAR system.

[0013] FIG. 4 is a block diagram illustrating an example fiber-based laser source.

[0014] FIGs. 5A-5C illustrate an example LiDAR system using pulse signals to measure distances to objects disposed in a field-of-view (FOV).

[0015] FIG. 6 is a block diagram illustrating an example apparatus used to implement systems, apparatus, and methods in various embodiments.

[0016] FIGs. 7A-7C illustrate an example of blooming in a LiDAR system point cloud image caused by a reflective test target.

[0017] FIG. 8 is a flow diagram illustrating a process for LiDAR perception.

[0018] FIGs. 9A-9C illustrate an example of blooming in a LiDAR system point cloud image caused by a reflective test target.

[0019] FIGs. 10A-10D illustrate an example of blooming in a LiDAR system point cloud image caused by a reflective portion of an automotive target.

[0020] FIG. 11 is a flow diagram illustrating a process for LiDAR perception in accordance with an embodiment.

[0021] FIG. 12 is a block diagram illustrating an example LiDAR Perception System in accordance with an embodiment.

[0022] FIG. 13 is a flow diagram illustrating a process for LiDAR perception in accordance with an embodiment.DETAILED DESCRIPTION

[0023] To provide a more thorough understanding of various embodiments of the present invention, the following description sets forth numerous specific details, such as specific configurations, parameters, examples, and the like. It should be recognized, however, that such description is not intended as a limitation on the scope of the present invention but is intended to provide a better description of the exemplary embodiments.

[0024] Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise:

[0025] The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. Thus, as described below, various embodiments of the disclosure may be readily combined, without departing from the scope or spirit of the invention.

[0026] As used herein, the term “or” is an inclusive “or” operator and is equivalent to the term “and / or,” unless the context clearly dictates otherwise.

[0027] The term “based on” is not exclusive and allows for being based on additional factors not described unless the context clearly dictates otherwise.

[0028] As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously. Within the context of a networked environment where two or more components or devices are able to exchange data, the terms “coupled to” and “coupled with” are also used to mean “communicatively coupled with”, possibly via one or more intermediary devices. The components or devices can be optical, mechanical, and / or electrical devices.

[0029] Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first sensor could be termed a second sensor and, similarly, a second sensor could be termed a first sensor, without departing from the scope of the various described examples. The first sensor and the second sensor can both be sensors and, in some cases, can be separate and different sensors.

[0030] In addition, throughout the specification, the meaning of “a”, “an”, and “the” includes plural references, and the meaning of “in” includes “in” and “on”.

[0031] Although some of the various embodiments presented herein constitute a single combination of inventive elements, it should be appreciated that the inventive subject matter is considered to include all possible combinations of the disclosed elements. As such, if one embodiment comprises elements A, B, and C, and another embodiment comprises elements Band D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly discussed herein. Further, the transitional term “comprising” means to have as parts or members, or to be those parts or members. As used herein, the transitional term “comprising” is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.

[0032] As used in the description herein and throughout the claims that follow, when a system, engine, server, device, module, or other computing element is described as being configured to perform or execute functions on data in a memory, the meaning of “configured to” or “programmed to” is defined as one or more processors or cores of the computing element being programmed by a set of software instructions stored in the memory of the computing element to execute the set of functions on target data or data objects stored in the memory.

[0033] It should be noted that any language directed to a computer should be read to include any suitable combination of computing devices or network platforms, including servers, interfaces, systems, databases, agents, peers, engines, controllers, modules, or other types of computing devices operating individually or collectively. One should appreciate the computing devices comprise a processor configured to execute software instructions stored on a tangible, non- transitory computer readable storage medium (e.g., hard drive, FPGA, PLA, solid state drive, RAM, flash, ROM, or any other volatile or non-volatile storage devices). The software instructions configure or program the computing device to provide the roles, responsibilities, or other functionality as discussed below with respect to the disclosed apparatus. Further, the disclosed technologies can be embodied as a computer program product that includes a non- transitory computer readable medium storing the software instructions that causes a processor to execute the disclosed steps associated with implementations of computer-based algorithms, processes, methods, or other instructions. In some embodiments, the various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPS, AES, public-private key exchanges, web service APIs, known financial transaction protocols, or other electronic information exchanging methods. Data exchanges among devices can be conducted over a packet-switched network, the Internet, LAN, WAN, VPN, or other type of packet switched network; a circuit switched network; cell switched network; or other type of network.

[0034] In the context of LiDAR point cloud data, “blooming” may describe single pixel enlargement due to laser beam expansion and / or cross channel crosstalk. For example, LiDAR raw point cloud data can include return light data with blooming caused by fog, dust, rain splashes in rainy weather, etc. The blooming features can be used as special information to identify different types of objects and / or to train for recognizing / deriving more general object types (e.g., for objects with blooming-generating portions at different locations). For example, the level of blooming may be different when there is rain or other environmental conditions (like the type or condition of a road) that affect the point cloud data returned from the object. These blooming levels can be used to indicate such conditions while the blooming-free portion can be used for object identification.

[0035] After the LiDAR raw point cloud data (without blooming cancellation) is obtained, an intermediate layer (e.g., a neural network) is added to the typical workflow to extract blooming features; and other layers may be operative to extract features from the blooming-free portion of target object data. Other than, or in addition to, extracting the blooming features, other point correction mechanisms can be used, such as intensity compensation, elongation correction due to shallow approaching angle to the surface, sun or other background noise cancellation by elevating background level, noise filtering, etc.

[0036] While typical current methods may use a neural network to extract / decouple blooming correction / cancellation data obtained in raw point cloud data, the embodiments described herein use blooming features in the deterministic method for object identification.

[0037] Embodiments of present invention are described below.

[0038] FIG. 1 illustrates one or more example LiDAR systems 110 and 120A-120I disposed or included in a motor vehicle 100. Vehicle 100 can be a car, a sport utility vehicle (SUV), a truck, a train, a wagon, a bicycle, a motorcycle, a tricycle, a bus, a mobility scooter, a tram, a ship, a boat, an underwater vehicle, an airplane, a helicopter, an unmanned aviation vehicle (UAV), a spacecraft, etc. Motor vehicle 100 can be a vehicle having any automated level. For example, motor vehicle 100 can be a partially automated vehicle, a highly automated vehicle, a fully automated vehicle, or a driverless vehicle. A partially automated vehicle can perform some driving functions without a human driver’s intervention. For example, a partially automated vehicle can perform blind-spot monitoring, lane keeping and / or lane changing operations,automated emergency braking, smart cruising and / or traffic following, or the like. Certain operations of a partially automated vehicle may be limited to specific applications or driving scenarios (e.g., limited to only freeway driving). A highly automated vehicle can generally perform all operations of a partially automated vehicle but with less limitations. A highly automated vehicle can also detect its own limits in operating the vehicle and ask the driver to take over the control of the vehicle when necessary. A fully automated vehicle can perform all vehicle operations without a driver’s intervention but can also detect its own limits and ask the driver to take over when necessary. A driverless vehicle can operate on its own without any driver intervention.

[0039] In typical configurations, motor vehicle 100 comprises one or more LiDAR systems 110 and 120A-120I. Each of LiDAR systems 110 and 120A-120I can be a scanning-based LiDAR system and / or a non-scanning LiDAR system (e.g., a flash LiDAR). A scanning-based LiDAR system scans one or more light beams in one or more directions (e.g., horizontal and vertical directions) to detect objects in a field-of-view (FOV). A non-scanning based LiDAR system transmits laser light to illuminate an FOV without scanning. For example, a flash LiDAR is a type of non-scanning based LiDAR system. A flash LiDAR can transmit laser light to simultaneously illuminate an FOV using a single light pulse or light shot.

[0040] A LiDAR system is a frequently-used sensor of a vehicle that is at least partially automated. In one embodiment, as shown in FIG. 1, motor vehicle 100 may include a single LiDAR system 110 (e.g., without LiDAR systems 120A-120I) disposed at the highest position of the vehicle (e.g., at the vehicle roof). Disposing LiDAR system 110 at the vehicle roof facilitates a 360-degree scanning around vehicle 100. In some other embodiments, motor vehicle 100 can include multiple LiDAR systems, including two or more of systems 110 and / or 120A-120I. As shown in FIG. 1, in one embodiment, multiple LiDAR systems 110 and / or 120A-120I are attached to vehicle 100 at different locations of the vehicle. For example, LiDAR system 120A is attached to vehicle 100 at the front right comer; LiDAR system 120B is attached to vehicle 100 at the front center position; LiDAR system 120C is attached to vehicle 100 at the front left corner; LiDAR system 120D is attached to vehicle 100 at the right-side rear view mirror; LiDAR system 120E is attached to vehicle 100 at the left-side rear view mirror; LiDAR system 120F is attached to vehicle 100 at the back center position; LiDAR system 120G is attached to vehicle 100 at the back right corner; LiDAR system 120H is attached to vehicle 100 at the back leftcorner; and / or LiDAR system 1201 is attached to vehicle 100 at the center towards the backend (e g., back end of the vehicle roof). It is understood that one or more LiDAR systems can be distributed and attached to a vehicle in any desired manner and FIG. 1 only illustrates one embodiment. As another example, LiDAR systems 120D and 120E may be attached to the B- pillars of vehicle 100 instead of the rear-view mirrors. As another example, LiDAR system 120B may be attached to the windshield of vehicle 100 instead of the front bumper.

[0041] In some embodiments, LiDAR systems 110 and 120A-1201 are independent LiDAR systems having their own respective laser sources, control electronics, transmitters, receivers, and / or steering mechanisms. In other embodiments, some of LiDAR systems 110 and 120A- 1201 can share one or more components, thereby forming a distributed sensor system. In one example, optical fibers are used to deliver laser light from a centralized laser source to all LiDAR systems. For instance, system 110 (or another system that is centrally positioned or positioned anywhere inside the vehicle 100) includes a light source, a transmitter, and a light detector, but has no steering mechanisms. System 110 may distribute transmission light to each of systems 120A-120I. The transmission light may be distributed via optical fibers. Optical connectors can be used to couple the optical fibers to each of system 110 and 120A-120I. In some examples, one or more of systems 120A-120I include steering mechanisms but no light sources, transmitters, or light detectors. A steering mechanism may include one or more moveable mirrors such as one or more polygon mirrors, one or more single plane mirrors, one or more multi-plane mirrors, or the like. Embodiments of the light source, transmitter, steering mechanism, and light detector are described in more detail below. Via the steering mechanisms, one or more of systems 120A-120I scan light into one or more respective FOVs and receive corresponding return light. The return light is formed by scattering or reflecting the transmission light by one or more objects in the FOVs. Systems 120A-120I may also include collection lens and / or other optics to focus and / or direct the return light into optical fibers, which deliver the received return light to system 110. System 110 includes one or more light detectors for detecting the received return light. In some examples, system 110 is disposed inside a vehicle such that it is in a temperature-controlled environment, while one or more systems 120A-120I may be at least partially exposed to the external environment.

[0042] FIG. 2 is a block diagram 200 illustrating interactions between vehicle onboard LiDAR system(s) 210 and multiple other systems including a vehicle perception and planning system220. LiDAR system(s) 210 can be mounted on or integrated to a vehicle. LiDAR system(s) 210 include sensor(s) that scan laser light to the surrounding environment to measure the distance, angle, and / or velocity of objects. Based on the scattered light that returned to LiDAR system(s) 210, it can generate sensor data (e.g., image data or 3D point cloud data) representing the perceived external environment.

[0043] LiDAR system(s) 210 can include one or more of short-range LiDAR sensors, mediumrange LiDAR sensors, and long-range LiDAR sensors. A short-range LiDAR sensor measures objects located up to about 20-50 meters from the LiDAR sensor. Short-range LiDAR sensors can be used for, e.g., monitoring nearby moving objects (e g., pedestrians crossing street in a school zone), parking assistance applications, or the like. A medium-range LiDAR sensor measures objects located up to about 70-200 meters from the LiDAR sensor. Medium-range LiDAR sensors can be used for, e.g., monitoring road intersections, assistance for merging onto or leaving a freeway, or the like. A long-range LiDAR sensor measures objects located up to about 200 meters and beyond. Long-range LiDAR sensors are typically used when a vehicle is travelling at a high speed (e.g., on a freeway), such that the vehicle’s control systems may only have a few seconds (e.g., 6-8 seconds) to respond to any situations detected by the LiDAR sensor. As shown in FIG. 2, in one embodiment, the LiDAR sensor data can be provided to vehicle perception and planning system 220 via a communication path 213 for further processing and controlling the vehicle operations. Communication path 213 can be any wired or wireless communication links that can transfer data.

[0044] With reference still to FIG. 2, in some embodiments, other vehicle onboard sensor(s) 230 are configured to provide additional sensor data separately or together with LiDAR system(s) 210. Other vehicle onboard sensors 230 may include, for example, one or more camera(s) 232, one or more radar(s) 234, one or more ultrasonic sensor(s) 236, and / or other sensor(s) 238. Camera(s) 232 can take images and / or videos of the external environment of a vehicle. Camera(s) 232 can take, for example, high-definition (HD) videos having millions of pixels in each frame. A camera includes image sensors that facilitate producing monochrome or color images and videos. Color information may be important in interpreting data for some situations (e.g., interpreting images of traffic lights). Color information may not be available from other sensors such as LiDAR or radar sensors. Camera(s) 232 can include one or more of narrowfocus cameras, wider-focus cameras, side-facing cameras, infrared cameras, fisheye cameras, orthe like. The image and / or video data generated by camera(s) 232 can also be provided to vehicle perception and planning system 220 via communication path 233 for further processing and controlling the vehicle operations. Communication path 233 can be any wired or wireless communication links that can transfer data. Camera(s) 232 can be mounted on, or integrated to, a vehicle at any location (e.g., rear-view mirrors, pillars, front grille, and / or back bumpers, etc.).

[0045] Other vehicle onboard sensos(s) 230 can also include radar sensor(s) 234. Radar sensor(s) 234 use radio waves to determine the range, angle, and velocity of objects. Radar sensor(s) 234 produce electromagnetic waves in the radio or microwave spectrum. The electromagnetic waves reflect off an object and some of the reflected waves return to the radar sensor, thereby providing information about the object’s position and velocity. Radar sensor(s) 234 can include one or more of short-range radar(s), medium-range radar(s), and long-range radar(s). A short-range radar measures objects located at about 0.1-30 meters from the radar. A short-range radar is useful in detecting objects located near the vehicle, such as other vehicles, buildings, walls, pedestrians, bicyclists, etc. A short-range radar can be used to detect a blind spot, assist in lane changing, provide rear-end collision warning, assist in parking, provide emergency braking, or the like. A medium-range radar measures objects located at about 30-80 meters from the radar. A long-range radar measures objects located at about 80-200 meters. Medium- and / or long-range radars can be useful in, for example, traffic following, adaptive cruise control, and / or highway automatic braking. Sensor data generated by radar sensor(s) 234 can also be provided to vehicle perception and planning system 220 via communication path 233 for further processing and controlling the vehicle operations. Radar sensor(s) 234 can be mounted on, or integrated to, a vehicle at any location (e.g., rear-view mirrors, pillars, front grille, and / or back bumpers, etc.).

[0046] Other vehicle onboard sensor(s) 230 can also include ultrasonic sensor(s) 236. Ultrasonic sensor(s) 236 use acoustic waves or pulses to measure objects located external to a vehicle. The acoustic waves generated by ultrasonic sensor(s) 236 are transmitted to the surrounding environment. At least some of the transmitted waves are reflected off an object and return to the ultrasonic sensor(s) 236. Based on the return signals, a distance of the object can be calculated. Ultrasonic sensor(s) 236 can be useful in, for example, checking blind spots, identifying parking spaces, providing lane changing assistance into traffic, or the like. Sensor data generated by ultrasonic sensor(s) 236 can also be provided to vehicle perception and planning system 220 viacommunication path 233 for further processing and controlling the vehicle operations.Ultrasonic sensor(s) 236 can be mounted on, or integrated to, a vehicle at any location (e.g., rearview mirrors, pillars, front grille, and / or back bumpers, etc.).

[0047] In some embodiments, one or more other sensor(s) 238 may be attached in a vehicle and may also generate sensor data. Other sensor(s) 238 may include, for example, global positioning systems (GPS), inertial measurement units (IMU), or the like. Sensor data generated by other sensor(s) 238 can also be provided to vehicle perception and planning system 220 via communication path 233 for further processing and controlling the vehicle operations. It is understood that communication path 233 may include one or more communication links to transfer data between the various sensor(s) 230 and vehicle perception and planning system 220.

[0048] In some embodiments, as shown in FIG. 2, sensor data from other vehicle onboard sensor(s) 230 can be provided to vehicle onboard LiDAR system(s) 210 via communication path 231. LiDAR system(s) 210 may process the sensor data from other vehicle onboard sensor(s) 230. For example, sensor data from camera(s) 232, radar sensor(s) 234, ultrasonic sensor(s) 236, and / or other sensor(s) 238 may be correlated or fused with sensor data LiDAR system(s) 210, thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system 220. It is understood that other configurations may also be implemented for transmitting and processing sensor data from the various sensors (e.g., data can be transmitted to a cloud or edge computing service provider for processing and then the processing results can be transmitted back to the vehicle perception and planning system 220 and / or LiDAR system 210).

[0049] With reference still to FIG. 2, in some embodiments, sensors onboard other vehicle(s) 250 are used to provide additional sensor data separately or together with LiDAR system(s) 210. For example, two or more nearby vehicles may have their own respective LiDAR sensor(s), camera(s), radar sensor(s), ultrasonic sensor(s), etc. Nearby vehicles can communicate and share sensor data with one another. Communications between vehicles are also referred to as V2V (vehicle to vehicle) communications. For example, as shown in FIG. 2, sensor data generated by other vehicle(s) 250 can be communicated to vehicle perception and planning system 220 and / or vehicle onboard LiDAR system(s) 210, via communication path 253 and / or communication path 251, respectively. Communication paths 253 and 251 can be any wired or wireless communication links that can transfer data.

[0050] Sharing sensor data facilitates a better perception of the environment external to the vehicles. For instance, a first vehicle may not sense a pedestrian that is behind a second vehicle but is approaching the first vehicle. The second vehicle may share the sensor data related to this pedestrian with the first vehicle such that the first vehicle can have additional reaction time to avoid collision with the pedestrian. In some embodiments, similar to data generated by sensor(s) 230, data generated by sensors onboard other vehicle(s) 250 may be correlated or fused with sensor data generated by LiDAR system(s) 210 (or with other LiDAR systems located in other vehicles), thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system 220.

[0051] In some embodiments, intelligent infrastructure system(s) 240 are used to provide sensor data separately or together with LiDAR system(s) 210. Certain infrastructures may be configured to communicate with a vehicle to convey information and vice versa.Communications between a vehicle and infrastructures are generally referred to as V2I (vehicle to infrastructure) communications. For example, intelligent infrastructure system(s) 240 may include an intelligent traffic light that can convey its status to an approaching vehicle in a message such as “changing to yellow in 5 seconds.” Intelligent infrastructure system(s) 240 may also include its own LiDAR system mounted near an intersection such that it can convey traffic monitoring information to a vehicle. For example, a left-turning vehicle at an intersection may not have sufficient sensing capabilities because some of its own sensors may be blocked by traffic in the opposite direction. In such a situation, sensors of intelligent infrastructure system(s) 240 can provide useful data to the left-turning vehicle. Such data may include, for example, traffic conditions, information of objects in the direction the vehicle is turning to, traffic light status and predictions, or the like. These sensor data generated by intelligent infrastructure system(s) 240 can be provided to vehicle perception and planning system 220 and / or vehicle onboard LiDAR system(s) 210, via communication paths 243 and / or 241, respectively.Communication paths 243 and / or 241 can include any wired or wireless communication links that can transfer data. For example, sensor data from intelligent infrastructure system(s) 240 may be transmitted to LiDAR system(s) 210 and correlated or fused with sensor data generated by LiDAR system(s) 210, thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system 220. V2V and V2I communicationsdescribed above are examples of vehicle-to-X (V2X) communications, where the “X” represents any other devices, systems, sensors, infrastructure, or the like that can share data with a vehicle.

[0052] With reference still to FIG. 2, via various communication paths, vehicle perception and planning system 220 receives sensor data from one or more of LiDAR system(s) 210, other vehicle onboard sensor(s) 230, other vehicle(s) 250, and / or intelligent infrastructure system(s) 240. In some embodiments, different types of sensor data are correlated and / or integrated by a sensor fusion sub-system 222. For example, sensor fusion sub-system 222 can generate a 360- degree model using multiple images or videos captured by multiple cameras disposed at different positions of the vehicle. Sensor fusion sub-system 222 obtains sensor data from different types of sensors and uses the combined data to perceive the environment more accurately. For example, a vehicle onboard camera 232 may not capture a clear image because it is facing the sun or a light source (e.g., another vehicle’s headlight during nighttime) directly. A LiDAR system 210 may not be affected as much and therefore sensor fusion sub-system 222 can combine sensor data provided by both camera 232 and LiDAR system 210, and use the sensor data provided by LiDAR system 210 to compensate the unclear image captured by camera 232. As another example, in a rainy or foggy weather, a radar sensor 234 may work better than a camera 232 or a LiDAR system 210. Accordingly, sensor fusion sub-system 222 may use sensor data provided by the radar sensor 234 to compensate the sensor data provided by camera 232 or LiDAR system 210.

[0053] In other examples, sensor data generated by other vehicle onboard sensor(s) 230 may have a lower resolution (e.g., radar sensor data) and thus may need to be correlated and confirmed by LiDAR system(s) 210, which usually has a higher resolution. For example, a sewage cover (also referred to as a manhole cover) may be detected by radar sensor 234 as an object towards which a vehicle is approaching. Due to the low-resolution nature of radar sensor 234, vehicle perception and planning system 220 may not be able to determine whether the object is an obstacle that the vehicle needs to avoid. High-resolution sensor data generated by LiDAR system(s) 210 thus can be used to correlated and confirm that the object is a sewage cover and causes no harm to the vehicle.

[0054] Vehicle perception and planning system 220 further comprises an object classifier 223.Using raw sensor data and / or correlated / fused data provided by sensor fusion sub-system 222,object classifier 223 can use any computer vision techniques to detect and classify the objects and estimate the positions of the objects. In some embodiments, object classifier 223 can use machine-learning based techniques to detect and classify objects. Examples of the machinelearning based techniques include utilizing algorithms such as region-based convolutional neural networks (R-CNN), Fast R-CNN, Faster R-CNN, histogram of oriented gradients (HOG), region-based fully convolutional network (R-FCN), single shot detector (SSD), spatial pyramid pooling (SPP-net), and / or You Only Look Once (Yolo).

[0055] Vehicle perception and planning system 220 further comprises a road detection subsystem 224. Road detection sub-system 224 localizes the road and identifies objects and / or markings on the road. For example, based on raw or fused sensor data provided by radar sensor(s) 234, camera(s) 232, and / or LiD AR system(s) 210, road detection sub-system 224 can build a 3D model of the road based on machine-learning techniques (e.g., pattern recognition algorithms for identifying lanes). Using the 3D model of the road, road detection sub-system 224 can identify objects (e.g., obstacles or debris on the road) and / or markings on the road (e.g., lane lines, turning marks, crosswalk marks, or the like).

[0056] Vehicle perception and planning system 220 further comprises a localization and vehicle posture sub-system 225. Based on raw or fused sensor data, localization and vehicle posture sub-system 225 can determine position of the vehicle and the vehicle’s posture. For example, using sensor data from LiDAR system(s) 210, camera(s) 232, and / or GPS data, localization and vehicle posture sub-system 225 can determine an accurate position of the vehicle on the road and the vehicle’s six degrees of freedom (e.g., whether the vehicle is moving forward or backward, up or down, and left or right). In some embodiments, high-definition (HD) maps are used for vehicle localization. HD maps can provide highly detailed, three-dimensional, computerized maps that pinpoint a vehicle’s location. For instance, using the HD maps, localization and vehicle posture sub-system 225 can determine precisely the vehicle’s current position (e.g., which lane of the road the vehicle is currently in, how close it is to a curb or a sidewalk) and predict vehicle’s future positions.

[0057] Vehicle perception and planning system 220 further comprises obstacle predictor 226. Objects identified by object classifier 223 can be stationary (e.g., a light pole, a road sign) or dynamic (e.g., a moving pedestrian, bicycle, another car). For moving objects, predicting theirmoving path or future positions can be important to avoid collision. Obstacle predictor 226 can predict an obstacle trajectory and / or warn the driver or the vehicle planning sub-system 228 about a potential collision. For example, if there is a high likelihood that the obstacle’s trajectory intersects with the vehicle’s current moving path, obstacle predictor 226 can generate such a warning. Obstacle predictor 226 can use a variety of techniques for making such a prediction. Such techniques include, for example, constant velocity or acceleration models, constant turn rate and velocity / acceleration models, Kalman Filter and Extended Kalman Filter based models, recurrent neural network (RNN) based models, long short-term memory (LSTM) neural network based models, encoder-decoder RNN models, or the like.

[0058] With reference still to FIG. 2, in some embodiments, vehicle perception and planning system 220 further comprises vehicle planning sub-system 228. Vehicle planning sub-system 228 can include one or more planners such as a route planner, a driving behaviors planner, and a motion planner. The route planner can plan the route of a vehicle based on the vehicle’s current location data, target location data, traffic information, etc. The driving behavior planner adjusts the timing and planned movement based on how other objects might move, using the obstacle prediction results provided by obstacle predictor 226. The motion planner determines the specific operations the vehicle needs to follow. The planning results are then communicated to vehicle control system 280 via vehicle interface 270. The communication can be performed through communication paths 227 and 271, which include any wired or wireless communication links that can transfer data.

[0059] Vehicle control system 280 controls the vehicle’s steering mechanism, throttle, brake, etc., to operate the vehicle according to the planned route and movement. In some examples, vehicle perception and planning system 220 may further comprise a user interface 260, which provides a user (e.g., a driver) access to vehicle control system 280 to, for example, override or take over control of the vehicle when necessary. User interface 260 may also be separate from vehicle perception and planning system 220. User interface 260 can communicate with vehicle perception and planning system 220, for example, to obtain and display raw or fused sensor data, identified objects, vehicle’s location / posture, etc. These displayed data can help a user to better operate the vehicle. User interface 260 can communicate with vehicle perception and planning system 220 and / or vehicle control system 280 via communication paths 221 and 261 respectively, which include any wired or wireless communication links that can transfer data. Itis understood that the various systems, sensors, communication links, and interfaces in FIG. 2 can be configured in any desired manner and not limited to the configuration shown in FIG. 2.

[0060] FIG. 3 is a block diagram illustrating an example LiDAR system 300. LiDAR system 300 can be used to implement LiDAR systems 110, 120A-120I, and / or 210 shown in FIGs. 1 and 2. In one embodiment, LiDAR system 300 comprises a light source 310, a transmitter 320, an optical receiver and light detector 330, a steering system 340, and a control circuitry 350. These components are coupled together using communications paths 312, 314, 322, 332, 342, 352, and 362. These communications paths include communication links (wired or wireless, bidirectional or unidirectional) among the various LiDAR system components, but need not be physical components themselves. While the communications paths can be implemented by one or more electrical wires, buses, or optical fibers, the communication paths can also be wireless channels or free-space optical paths so that no physical communication medium is present. For example, in one embodiment of LiDAR system 300, communication path 314 between light source 310 and transmitter 320 may be implemented using one or more optical fibers. Communication paths 332 and 352 may represent optical paths implemented using free space optical components and / or optical fibers. And communication paths 312, 322, 342, and 362 may be implemented using one or more electrical wires that carry electrical signals. The communications paths can also include one or more of the above types of communication mediums (e.g., they can include an optical fiber and a free-space optical component, or include one or more optical fibers and one or more electrical wires).

[0061] In some embodiments, LiDAR system 300 can be a coherent LiDAR system. One example is a frequency-modulated continuous-wave (FMCW) LiDAR. Coherent LiDARs detect objects by mixing return light from the objects with light from the coherent laser transmitter. Thus, as shown in FIG. 3, if LiDAR system 300 is a coherent LiDAR, it may include a route 372 providing a portion of transmission light from transmitter 320 to optical receiver and light detector 330. Route 372 may include one or more optics (e.g., optical fibers, lens, mirrors, etc.) for providing the light from transmitter 320 to optical receiver and light detector 330. The transmission light provided by transmitter 320 may be modulated light and can be split into two portions. One portion is transmitted to the FOV, while the second portion is sent to the optical receiver and light detector of the LiDAR system. The second portion is also referred to as the light that is kept local (LO) to the LiDAR system. The transmission light is scattered or reflectedby various objects in the FOV and at least a portion of it forms return light. The return light is subsequently detected and interferometrically recombined with the second portion of the transmission light that was kept local. Coherent LiDAR provides a means of optically sensing an object’s range as well as its relative velocity along the line-of-sight (LOS).

[0062] LiDAR system 300 can also include other components not depicted in FIG. 3, such as power buses, power supplies, LED indicators, switches, etc. Additionally, other communication connections among components may be present, such as a direct connection between light source 310 and optical receiver and light detector 330 to provide a reference signal so that the time from when a light pulse is transmitted until a return light pulse is detected can be accurately measured.

[0063] Light source 310 outputs laser light for illuminating objects in a field of view (FOV). The laser light can be infrared light having a wavelength in the range of 700nm to 1mm. Light source 310 can be, for example, a semiconductor-based laser (e.g., a diode laser) and / or a fiberbased laser. A semiconductor-based laser can be, for example, an edge emitting laser (EEL), a vertical cavity surface emitting laser (VCSEL), an external-cavity diode laser, a verticalexternal-cavity surface-emitting laser, a distributed feedback (DFB) laser, a distributed Bragg reflector (DBR) laser, an interband cascade laser, a quantum cascade laser, a quantum well laser, a double heterostructure laser, or the like. A fiber-based laser is a laser in which the active gain medium is an optical fiber doped with rare-earth elements such as erbium, ytterbium, neodymium, dysprosium, praseodymium, thulium and / or holmium. In some embodiments, a fiber laser is based on double-clad fibers, in which the gain medium forms the core of the fiber surrounded by two layers of cladding. The double-clad fiber allows the core to be pumped with a high-power beam, thereby enabling the laser source to be a high power fiber laser source.

[0064] In some embodiments, light source 310 comprises a master oscillator (also referred to as a seed laser) and power amplifier (MOP A). The power amplifier amplifies the output power of the seed laser. The power amplifier can be a fiber amplifier, a bulk amplifier, or a semiconductor optical amplifier. The seed laser can be a diode laser (e.g., a Fabry-Perot cavity laser, a distributed feedback laser), a solid-state bulk laser, or a tunable external-cavity diode laser. In some embodiments, light source 310 can be an optically pumped microchip laser. Microchip lasers are alignment-free monolithic solid-state lasers where the laser crystal is directly contacted with the end mirrors of the laser resonator. A microchip laser is typically pumped with a laserdiode (directly or using a fiber) to obtain the desired output power. A microchip laser can be based on neodymium-doped yttrium aluminum garnet (Y3AI5O12) laser crystals (i.e., Nd:YAG), or neodymium-doped vanadate (i.e., NDiYVCh) laser crystals. In some examples, light source 310 may have multiple amplification stages to achieve a high power gain such that the laser output can have high power, thereby enabling the LiDAR system to have a long scanning range. In some examples, the power amplifier of light source 310 can be controlled such that the power gain can be varied to achieve any desired laser output power.

[0065] FIG. 4 is a block diagram illustrating an example fiber-based laser source 400 having a seed laser and one or more pumps (e.g., laser diodes) for pumping desired output power. Fiberbased laser source 400 is an example of light source 310 depicted in FIG. 3. In some embodiments, fiber-based laser source 400 comprises a seed laser 402 to generate initial light pulses of one or more wavelengths (e.g., infrared wavelengths such as 1550 nm), which are provided to a wavelength-division multiplexor (WDM) 404 via an optical fiber 403. Fiber-based laser source 400 further comprises a pump 406 for providing laser power (e.g., of a different wavelength, such as 980 nm) to WDM 404 via an optical fiber 405. WDM 404 multiplexes the light pulses provided by seed laser 402 and the laser power provided by pump 406 onto a single optical fiber 407. The output of WDM 404 can then be provided to one or more pre-amplifier(s) 408 via optical fiber 407. Pre-amplifier(s) 408 can be optical amplifier(s) that amplify optical signals (e.g., with about 10-30 dB gain). In some embodiments, pre-amplifier(s) 408 are low noise amplifiers. Pre-amplifier(s) 408 output to an optical combiner 410 via an optical fiber 409. Combiner 410 combines the output laser light of pre-amplifier(s) 408 with the laser power provided by pump 412 via an optical fiber 411. Combiner 410 can combine optical signals having the same wavelength or different wavelengths. One example of a combiner is a WDM. Combiner 410 provides combined optical signals to a booster amplifier 414, which produces output light pulses via optical fiber 415. The booster amplifier 414 provides further amplification of the optical signals (e.g., another 20-40dB). The output light pulses can then be transmitted to transmitter 320 and / or steering mechanism 340 (shown in FIG. 3). It is understood that FIG. 4 illustrates one example configuration of fiber-based laser source 400. Laser source 400 can have many other configurations using different combinations of one or more components shown in FIG. 4 and / or other components not shown in FIG. 4 (e.g., other components such as power supplies, lens(es), filters, splitters, combiners, etc.).

[0066] In some variations, fiber-based laser source 400 can be controlled (e g., by control circuitry 350) to produce pulses of different amplitudes based on the fiber gain profile of the fiber used in fiber-based laser source 400. Communication path 312 couples fiber-based laser source 400 to control circuitry 350 (shown in FIG. 3) so that components of fiber-based laser source 400 can be controlled by or otherwise communicate with control circuitry 350. Alternatively, fiber-based laser source 400 may include its own dedicated controller. Instead of control circuitry 350 communicating directly with components of fiber-based laser source 400, a dedicated controller of fiber-based laser source 400 communicates with control circuitry 350 and controls and / or communicates with the components of fiber-based laser source 400. Fiber-based laser source 400 can also include other components not shown, such as one or more power connectors, power supplies, and / or power lines.

[0067] Referencing FIG. 3, typical operating wavelengths of light source 310 comprise, for example, about 850 nm, about 905 nm, about 940 nm, about 1064 nm, and about 1550 nm. For laser safety, the upper limit of maximum usable laser power is set by the U.S. FDA (U.S. Food and Drug Administration) regulations. The optical power limit at 1550 nm wavelength is much higher than those of the other aforementioned wavelengths. Further, at 1550 nm, the optical power loss in a fiber is low. There characteristics of the 1550 nm wavelength make it more beneficial for long-range LiDAR applications. The amount of optical power output from light source 310 can be characterized by its peak power, average power, pulse energy, and / or the pulse energy density. The peak power is the ratio of pulse energy to the width of the pulse (e.g., full width at half maximum or FWHM). Thus, a smaller pulse width can provide a larger peak power for a fixed amount of pulse energy. A pulse width can be in the range of nanosecond or picosecond. The average power is the product of the energy of the pulse and the pulse repetition rate (PRR). As described in more detail below, the PRR represents the frequency of the pulsed laser light. In general, the smaller the time interval between the pulses, the higher the PRR. The PRR typically corresponds to the maximum range that a LiDAR system can measure. Light source 310 can be configured to produce pulses at high PRR to meet the desired number of data points in a point cloud generated by the LiDAR system. Light source 310 can also be configured to produce pulses at medium or low PRR to meet the desired maximum detection distance. Wall plug efficiency (WPE) is another factor to evaluate the total power consumption, which may be a useful indicator in evaluating the laser efficiency. For example, as shown in FIG. 1, multipleLiDAR systems may be attached to a vehicle, which may be an electrical -powered vehicle or a vehicle otherwise having limited fuel or battery power supply. Therefore, high WPE and intelligent ways to use laser power are often among the important considerations when selecting and configuring light source 310 and / or designing laser delivery systems for vehicle-mounted LiDAR applications.

[0068] It is understood that the above descriptions provide non-limiting examples of a light source 310. Light source 310 can be configured to include many other types of light sources (e.g., laser diodes, short-cavity fiber lasers, solid-state lasers, and / or tunable external cavity diode lasers) that are configured to generate one or more light signals at various wavelengths. In some examples, light source 310 comprises amplifiers (e.g., pre-amplifiers and / or booster amplifiers), which can be a doped optical fiber amplifier, a solid-state bulk amplifier, and / or a semiconductor optical amplifier. The amplifiers are configured to receive and amplify light signals with desired gains.

[0069] With reference back to FIG. 3, LiDAR system 300 further comprises a transmitter 320. Light source 310 provides laser light (e.g., in the form of a laser beam) to transmitter 320. The laser light provided by light source 310 can be amplified laser light with a predetermined or controlled wavelength, pulse repetition rate, and / or power level. Transmitter 320 receives the laser light from light source 310 and transmits the laser light to steering mechanism 340 with low divergence. In some embodiments, transmitter 320 can include, for example, optical components (e.g., lens, fibers, mirrors, etc.) for transmitting one or more laser beams to a field-of-view (FOV) directly or via steering mechanism 340. While FIG. 3 illustrates transmitter 320 and steering mechanism 340 as separate components, they may be combined or integrated as one system in some embodiments. Steering mechanism 340 is described in more detail below.

[0070] Laser beams provided by light source 310 may diverge as they travel to transmitter 320. Therefore, transmitter 320 often comprises a collimating lens configured to collect the diverging laser beams and produce more parallel optical beams with reduced or minimum divergence. The collimated optical beams can then be further directed through various optics such as mirrors and lens. A collimating lens may be, for example, a single plano-convex lens or a lens group. The collimating lens can be configured to achieve any desired properties such as the beam diameter, divergence, numerical aperture, focal length, or the like. A beam propagation ratio or beamquality factor (also referred to as the M2factor) is used for measurement of laser beam quality. In many LiDAR applications, it is important to have good laser beam quality in the generated transmitting laser beam. The M2factor represents a degree of variation of a beam from an ideal Gaussian beam. Thus, the M2factor reflects how well a collimated laser beam can be focused on a small spot, or how well a divergent laser beam can be collimated. Therefore, light source 310 and / or transmitter 320 can be configured to meet, for example, a scan resolution requirement while maintaining the desired M2factor.

[0071] One or more of the light beams provided by transmitter 320 are scanned by steering mechanism 340 to a FOV. Steering mechanism 340 scans light beams in multiple dimensions (e.g., in both the horizontal and vertical dimension) to facilitate LiDAR system 300 to map the environment by generating a 3D point cloud. A horizontal dimension can be a dimension that is parallel to the horizon or a surface associated with the LiDAR system or a vehicle (e.g., a road surface). A vertical dimension is perpendicular to the horizontal dimension (i.e., the vertical dimension forms a 90-degree angle with the horizontal dimension). Steering mechanism 340 will be described in more detail below. The laser light scanned to an FOV may be scattered or reflected by an object in the FOV. At least a portion of the scattered or reflected light forms return light that returns to LiDAR system 300. FIG. 3 further illustrates an optical receiver and light detector 330 configured to receive the return light. Optical receiver and light detector 330 comprises an optical receiver that is configured to collect the return light from the FOV. The optical receiver can include optics (e.g., lens, fibers, mirrors, etc.) for receiving, redirecting, focusing, amplifying, and / or filtering return light from the FOV. For example, the optical receiver often includes a collection lens (e.g., a single plano-convex lens or a lens group) to collect and / or focus the collected return light onto a light detector.

[0072] A light detector detects the return light focused by the optical receiver and generates current and / or voltage signals proportional to the incident intensity of the return light. Based on such current and / or voltage signals, the depth information of the object in the FOV can be derived. One example method for deriving such depth information is based on the direct TOF (time of flight), which is described in more detail below. A light detector may be characterized by its detection sensitivity, quantum efficiency, detector bandwidth, linearity, signal to noise ratio (SNR), overload resistance, interference immunity, etc. Based on the applications, the light detector can be configured or customized to have any desired characteristics. For example,optical receiver and light detector 330 can be configured such that the light detector has a large dynamic range while having a good linearity. The light detector linearity indicates the detector’s capability of maintaining linear relationship between input optical signal power and the detector’s output. A detector having good linearity can maintain a linear relationship over a large dynamic input optical signal range.

[0073] To achieve desired detector characteristics, configurations or customizations can be made to the light detector’s structure and / or the detector’s material system. Various detector structures can be used for a light detector. For example, a light detector structure can be a PIN based structure, which has a undoped intrinsic semiconductor region (i.e., an “i” region) between a p- type semiconductor and an n-type semiconductor region. Other light detector structures comprise, for example, an APD (avalanche photodiode) based structure, a PMT (photomultiplier tube) based structure, a SiPM (Silicon photomultiplier) based structure, a SPAD (single-photon avalanche diode) based structure, and / or quantum wires. For material systems used in a light detector, Si, InGaAs, and / or Si / Ge based materials can be used. It is understood that many other detector structures and / or material systems can be used in optical receiver and light detector 330.

[0074] A light detector (e.g., an APD based detector) may have an internal gain such that the input signal is amplified when generating an output signal. However, noise may also be amplified due to the light detector’s internal gain. Common types of noise include signal shot noise, dark current shot noise, thermal noise, and amplifier noise. In some embodiments, optical receiver and light detector 330 may include a pre-amplifier that is a low noise amplifier (LNA). In some embodiments, the pre-amplifier may also include a transimpedance amplifier (TIA), which converts a current signal to a voltage signal. For a linear detector system, input equivalent noise or noise equivalent power (NEP) measures how sensitive the light detector is to weak signals. Therefore, they can be used as indicators of the overall system performance. For example, the NEP of a light detector specifies the power of the weakest signal that can be detected and therefore it in turn specifies the maximum range of a LiDAR system. It is understood that various light detector optimization techniques can be used to meet the requirement of LiDAR system 300. Such optimization techniques may include selecting different detector structures, materials, and / or implementing signal processing techniques (e.g., filtering, noise reduction, amplification, or the like). For example, in addition to, or instead of, using direct detection of return signals (e.g., by using ToF), coherent detection can also be usedfor a light detector. Coherent detection allows for detecting amplitude and phase information of the received light by interfering the received light with a local oscillator. Coherent detection can improve detection sensitivity and noise immunity.

[0075] FIG. 3 further illustrates that LiDAR system 300 comprises steering mechanism 340. As described above, steering mechanism 340 directs light beams from transmitter 320 to scan an FOV in multiple dimensions. A steering mechanism is referred to as a raster mechanism, a scanning mechanism, or simply a light scanner. Scanning light beams in multiple directions (e.g., in both the horizontal and vertical directions) facilitates a LiDAR system to map the environment by generating an image or a 3D point cloud. A steering mechanism can be based on mechanical scanning and / or solid-state scanning. Mechanical scanning uses rotating mirrors to steer the laser beam or physically rotate the LiDAR transmitter and receiver (collectively referred to as transceiver) to scan the laser beam. Solid-state scanning directs the laser beam to various positions through the FOV without mechanically moving any macroscopic components such as the transceiver. Solid-state scanning mechanisms include, for example, optical phased arrays based steering and flash LiDAR based steering. In some embodiments, because solid- state scanning mechanisms do not physically move macroscopic components, the steering performed by a solid-state scanning mechanism may be referred to as effective steering. A LiDAR system using solid-state scanning may also be referred to as a non-mechanical scanning or simply non-scanning LiDAR system (a flash LiDAR system is an example non-scanning LiDAR system).

[0076] Steering mechanism 340 can be used with a transceiver (e.g., transmitter 320 and optical receiver and light detector 330) to scan the FOV for generating an image or a 3D point cloud. As an example, to implement steering mechanism 340, a two-dimensional mechanical scanner can be used with a single-point or several single-point transceivers. A single-point transceiver transmits a single light beam or a small number of light beams (e.g., 2-8 beams) to the steering mechanism. A two-dimensional mechanical steering mechanism comprises, for example, polygon mirror(s), oscillating mirror(s), rotating prism(s), rotating tilt mirror surface(s), singleplane or multi-plane mirror(s), or a combination thereof. In some embodiments, steering mechanism 340 may include non-mechanical steering mechanism(s) such as solid-state steering mechanism(s). For example, steering mechanism 340 can be based on tuning wavelength of the laser light combined with refraction effect, and / or based on reconfigurable grating / phase array.In some embodiments, steering mechanism 340 can use a single scanning device to achieve two- dimensional scanning or multiple scanning devices combined to realize two-dimensional scanning.

[0077] As another example, to implement steering mechanism 340, a one-dimensional mechanical scanner can be used with an array or a large number of single-point transceivers. Specifically, the transceiver array can be mounted on a rotating platform to achieve 360-degree horizontal field of view. Alternatively, a static transceiver array can be combined with the onedimensional mechanical scanner. A one-dimensional mechanical scanner comprises polygon mirror(s), oscillating mirror(s), rotating prism(s), rotating tilt mirror surface(s), or a combination thereof, for obtaining a forward-looking horizontal field of view. Steering mechanisms using mechanical scanners can provide robustness and reliability in high volume production for automotive applications.

[0078] As another example, to implement steering mechanism 340, a two-dimensional transceiver can be used to generate a scan image or a 3D point cloud directly. In some embodiments, a stitching or micro shift method can be used to improve the resolution of the scan image or the field of view being scanned. For example, using a two-dimensional transceiver, signals generated at one direction (e.g., the horizontal direction) and signals generated at the other direction (e.g., the vertical direction) may be integrated, interleaved, and / or matched to generate a higher or full resolution image or 3D point cloud representing the scanned FOV.

[0079] Some implementations of steering mechanism 340 comprise one or more optical redirection elements (e.g., mirrors or lenses) that steer return light signals (e.g., by rotating, vibrating, or directing) along a receive path to direct the return light signals to optical receiver and light detector 330. The optical redirection elements that direct light signals along the transmitting and receiving paths may be the same components (e.g., shared), separate components (e.g., dedicated), and / or a combination of shared and separate components. This means that in some cases the transmitting and receiving paths are different although they may partially overlap (or in some cases, substantially overlap or completely overlap).

[0080] With reference still to FIG. 3, LiDAR system 300 further comprises control circuitry 350. Control circuitry 350 can be configured and / or programmed to control various parts of the LiDAR system 300 and / or to perform signal processing. In a typical system, control circuitry350 can be configured and / or programmed to perform one or more control operations including, for example, controlling light source 310 to obtain the desired laser pulse timing, the pulse repetition rate, and power; controlling steering mechanism 340 (e.g., controlling the speed, direction, and / or other parameters) to scan the FOV and maintain pixel registration and / or alignment; controlling optical receiver and light detector 330 (e.g., controlling the sensitivity, noise reduction, filtering, and / or other parameters) such that it is an optimal state; and monitoring overall system health / status for functional safety (e.g., monitoring the laser output power and / or the steering mechanism operating status for safety).

[0081] Control circuitry 350 can also be configured and / or programmed to perform signal processing to the raw data generated by optical receiver and light detector 330 to derive distance and reflectance information, and perform data packaging and communication to vehicle perception and planning system 220 (shown in FIG. 2). For example, control circuitry 350 determines the time it takes from transmitting a light pulse until a corresponding return light pulse is received; determines when a return light pulse is not received for a transmitted light pulse; determines the direction (e.g., horizontal and / or vertical information) for a transmitted / retum light pulse; determines the estimated range in a particular direction; derives the reflectivity of an object in the FOV, and / or determines any other type of data relevant to LiDAR system 300.

[0082] LiDAR system 300 can be disposed in a vehicle, which may operate in many different environments including hot or cold weather, rough road conditions that may cause intense vibration, high or low humidities, dusty areas, etc. Therefore, in some embodiments, optical and / or electronic components of LiDAR system 300 (e.g., optics in transmitter 320, optical receiver and light detector 330, and steering mechanism 340) are disposed and / or configured in such a manner to maintain long term mechanical and optical stability. For example, components in LiDAR system 300 may be secured and sealed such that they can operate under all conditions a vehicle may encounter. As an example, an anti-moisture coating and / or hermetic sealing may be applied to optical components of transmitter 320, optical receiver and light detector 330, and steering mechanism 340 (and other components that are susceptible to moisture). As another example, housing(s), enclosure(s), fairing(s), and / or window can be used in LiDAR system 300 for providing desired characteristics such as hardness, ingress protection (IP) rating, selfcleaning capability, resistance to chemical and resistance to impact, or the like. In addition,efficient and economical methodologies for assembling LiDAR system 300 may be used to meet the LiDAR operating requirements while keeping the cost low.

[0083] It is understood by a person of ordinary skill in the art that FIG. 3 and the above descriptions are for illustrative purposes only, and a LiDAR system can include other functional units, blocks, or segments, and can include variations or combinations of these above functional units, blocks, or segments. For example, LiDAR system 300 can also include other components not depicted in FIG. 3, such as power buses, power supplies, LED indicators, switches, etc. Additionally, other connections among components may be present, such as a direct connection between light source 310 and optical receiver and light detector 330 so that light detector 330 can accurately measure the time from when light source 310 transmits a light pulse until light detector 330 detects a return light pulse.

[0084] These components shown in FIG. 3 are coupled together using communications paths 312, 314, 322, 332, 342, 352, and 362. These communications paths represent communication (bidirectional or unidirectional) among the various LiDAR system components but need not be physical components themselves. While the communications paths can be implemented by one or more electrical wires, buses, or optical fibers, the communication paths can also be wireless channels or open-air optical paths so that no physical communication medium is present. For example, in one example LiDAR system, communication path 314 includes one or more optical fibers; communication path 352 represents an optical path; and communication paths 312, 322, 342, and 362 are all electrical wires that carry electrical signals. The communication paths can also include more than one of the above types of communication mediums (e.g., they can include an optical fiber and an optical path, or one or more optical fibers and one or more electrical wires).

[0085] As described above, some LiDAR systems use the time-of-flight (ToF) of light signals (e.g., light pulses) to determine the distance to objects in a light path. For example, with reference to FIG. 5A, an example LiDAR system 500 includes a laser light source (e.g., a fiber laser), a steering mechanism (e.g., a system of one or more moving mirrors), and a light detector (e.g., a photodetector with one or more optics). LiDAR system 500 can be implemented using, for example, LiDAR system 300 described above. LiDAR system 500 transmits a light pulse 502 along light path 504 as determined by the steering mechanism of LiDAR system 500. In thedepicted example, light pulse 502, which is generated by the laser light source, is a short pulse of laser light. Further, the signal steering mechanism of the LiDAR system 500 is a pulsed-signal steering mechanism. However, it should be appreciated that LiDAR systems can operate by generating, transmitting, and detecting light signals that are not pulsed and derive ranges to an object in the surrounding environment using techniques other than time-of-flight. For example, some LiDAR systems use frequency modulated continuous waves (i.e., “FMCW”). It should be further appreciated that any of the techniques described herein with respect to time-of-flight based systems that use pulsed signals also may be applicable to LiDAR systems that do not use one or both of these techniques.

[0086] Referring back to FIG. 5 A (e.g., illustrating a time-of-flight LiDAR system that uses light pulses), when light pulse 502 reaches object 506, light pulse 502 scatters or reflects to form a return light pulse 508. Return light pulse 508 may return to system 500 along light path 510. The time from when transmitted light pulse 502 leaves LiDAR system 500 to when return light pulse 508 arrives back at LiDAR system 500 can be measured (e.g., by a processor or other electronics, such as control circuitry 350, within the LiDAR system). This time-of-flight combined with the knowledge of the speed of light can be used to determine the range / di stance from LiDAR system 500 to the portion of object 506 where light pulse 502 scattered or reflected.

[0087] By directing many light pulses, as depicted in FIG. 5B, LiDAR system 500 scans the external environment (e.g., by directing light pulses 502, 522, 526, 530 along light paths 504, 524, 528, 532, respectively). As depicted in FIG. 5C, LiDAR system 500 receives return light pulses 508, 542, 548 (which correspond to transmitted light pulses 502, 522, 530, respectively). Return light pulses 508, 542, and 548 are formed by scattering or reflecting the transmitted light pulses by one of objects 506 and 514. Return light pulses 508, 542, and 548 may return to LiDAR system 500 along light paths 510, 544, and 546, respectively. Based on the direction of the transmitted light pulses (as determined by LiDAR system 500) as well as the calculated range from LiDAR system 500 to the portion of objects that scatter or reflect the light pulses (e.g., the portions of objects 506 and 514), the external environment within the detectable range (e.g., the field of view between path 504 and 532, inclusively) can be precisely mapped or plotted (e.g., by generating a 3D point cloud or images).

[0088] If a corresponding light pulse is not received for a particular transmitted light pulse, then LiDAR system 500 may determine that there are no objects within a detectable range of LiDAR system 500 (e.g., an object is beyond the maximum scanning distance of LiDAR system 500). For example, in FIG. 5B, light pulse 526 may not have a corresponding return light pulse (as illustrated in FIG. 5C) because light pulse 526 may not produce a scattering event along its transmission path 528 within the predetermined detection range. LiDAR system 500, or an external system in communication with LiDAR system 500 (e.g., a cloud system or service), can interpret the lack of return light pulse as no object being disposed along light path 528 within the detectable range of LiDAR system 500.

[0089] In FIG. 5B, light pulses 502, 522, 526, and 530 can be transmitted in any order, serially, in parallel, or based on other timings with respect to each other. Additionally, while FIG. 5B depicts transmitted light pulses as being directed in one dimension or one plane (e.g., the plane of the paper), LiDAR system 500 can also direct transmitted light pulses along other dimension(s) or plane(s). For example, LiDAR system 500 can also direct transmitted light pulses in a dimension or plane that is perpendicular to the dimension or plane shown in FIG. 5B, thereby forming a 2-dimensional transmission of the light pulses. This 2-dimensional transmission of the light pulses can be point-by-point, line-by-line, all at once, or in some other manner. That is, LiDAR system 500 can be configured to perform a point scan, a line scan, a one-shot without scanning, or a combination thereof. A point cloud or image from a 1-dimensional transmission of light pulses (e.g., a single horizontal line) can generate 2- dimensional data (e.g., (1) data from the horizontal transmission direction and (2) the range or distance to objects). Similarly, a point cloud or image from a 2-dimensional transmission of light pulses can generate 3-dimensional data (e.g., (1) data from the horizontal transmission direction, (2) data from the vertical transmission direction, and (3) the range or distance to objects). In general, a LiDAR system performing an / / -dimensional transmission of light pulses generates ( / / + 1 ) dimensional data. This is because the LiDAR system can measure the depth of an object or the range / di stance to the object, which provides the extra dimension of data. Therefore, a 2D scanning by a LiDAR system can generate a 3D point cloud for mapping the external environment of the LiDAR system.

[0090] The density of a point cloud refers to the number of measurements (data points) per area performed by the LiDAR system. A point cloud density relates to the LiDAR scanningresolution. Typically, a larger point cloud density, and therefore a higher resolution, is desired at least for the region of interest (ROI). The density of points in a point cloud or image generated by a LiDAR system is equal to the number of pulses divided by the field of view. In some embodiments, the field of view can be fixed. Therefore, to increase the density of points generated by one set of transmission-receiving optics (or transceiver optics), the LiDAR system may need to generate a pulse more frequently. In other words, a light source in the LiDAR system may have a higher pulse repetition rate (PRR). On the other hand, by generating and transmitting pulses more frequently, the farthest distance that the LiDAR system can detect may be limited. For example, if a return signal from a distant object is received after the system transmits the next pulse, the return signals may be detected in a different order than the order in which the corresponding signals are transmitted, thereby causing ambiguity if the system cannot correctly correlate the return signals with the transmitted signals.

[0091] To illustrate, consider an example LiDAR system that can transmit laser pulses with a pulse repetition rate between 500 kHz and 1 MHz. Based on the time it takes for a pulse to return to the LiDAR system and to avoid mix-up of return pulses from consecutive pulses in a typical LiDAR design, the farthest distance the LiDAR system can detect may be 300 meters and 150 meters for 500 kHz and 1 MHz, respectively. The density of points of a LiDAR system with 500 kHz repetition rate is half of that with 1 MHz. Thus, this example demonstrates that, if the system cannot correctly correlate return signals that arrive out of order, increasing the repetition rate from 500 kHz to 1 MHz (and thus improving the density of points of the system) may reduce the detection range of the system. Various techniques are used to mitigate the tradeoff between higher PRR and limited detection range. For example, multiple wavelengths can be used for detecting objects in different ranges. Optical and / or signal processing techniques (e.g., pulse encoding techniques) are also used to correlate between transmitted and return light signals.

[0092] Various systems, apparatus, and methods described herein may be implemented using digital circuitry, or using one or more computers using well-known computer processors, memory units, storage devices, computer software, and other components. Typically, a computer includes a processor for executing instructions and one or more memories for storing instructions and data. A computer may also include, or be coupled to, one or more mass storage devices, such as one or more magnetic disks, internal hard disks and removable disks, magnetooptical disks, optical disks, etc.

[0093] Various systems, apparatus, and methods described herein may be implemented using computers operating in a client-server relationship. Typically, in such a system, the client computers are located remotely from the server computers and interact via a network. The client-server relationship may be defined and controlled by computer programs running on the respective client and server computers. Examples of client computers can include desktop computers, workstations, portable computers, cellular smartphones, tablets, or other types of computing devices.

[0094] Various systems, apparatus, and methods described herein may be implemented using a computer program product tangibly embodied in an information carrier, e.g., in a non-transitory machine-readable storage device, for execution by a programmable processor; and the method processes and steps described herein, including one or more of the steps of at least some of the FIGS. 1-5 and 7A-13, may be implemented using one or more computer programs that are executable by such a processor. A computer program is a set of computer program instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0095] A high-level block diagram of an example apparatus that may be used to implement systems, apparatus and methods described herein is illustrated in FIG. 6. Apparatus 600 comprises a processor 610 operatively coupled to a persistent storage device 620 and a main memory device 630. Processor 610 controls the overall operation of apparatus 600 by executing computer program instructions that define such operations. The computer program instructions may be stored in persistent storage device 620, or other computer-readable medium, and loaded into main memory device 630 when execution of the computer program instructions is desired. For example, processor 610 may be used to implement one or more components and systems described herein, such as control circuitry 350 (shown in FIG. 3), vehicle perception and planning system 220 (shown in FIG. 2), and vehicle control system 280 (shown in FIG. 2).Thus, the method steps of at least some of FIGS. 1-5 and 7A-13 can be defined by the computer program instructions stored in main memory device 630 and / or persistent storage device 620 and controlled by processor 610 executing the computer program instructions. For example, thecomputer program instructions can be implemented as computer executable code programmed by one skilled in the art to perform an algorithm defined by the method steps discussed herein in connection with at least some of FIGS. 1-5 and 7A-13. Accordingly, by executing the computer program instructions, the processor 610 executes an algorithm defined by the method steps of the aforementioned figures. Apparatus 600 also includes one or more network interfaces 680 for communicating with other devices via a network. Apparatus 600 may also include one or more input / output devices 690 that enable user interaction with apparatus 600 (e.g., display, keyboard, mouse, speakers, buttons, etc.).

[0096] Processor 610 may include both general and special purpose microprocessors and may be the sole processor or one of multiple processors of apparatus 600. Processor 610 may comprise one or more central processing units (CPUs), and one or more graphics processing units (GPUs), which, for example, may work separately from and / or multi-task with one or more CPUs to accelerate processing, e.g., for various image processing applications described herein.Processor 610, persistent storage device 620, and / or main memory device 630 may include, be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs) and / or one or more field programmable gate arrays (FPGAs).

[0097] Persistent storage device 620 and main memory device 630 each comprise a tangible non-transitory computer readable storage medium. Persistent storage device 620, and main memory device 630, may each include high-speed random access memory, such as dynamic random access memory (DRAM), static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices such as internal hard disks and removable disks, magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory (DVD-ROM) disks, or other non-volatile solid state storage devices.

[0098] Input / output devices 690 may include peripherals, such as a printer, scanner, display screen, etc. For example, input / output devices 690 may include a display device such as acathode ray tube (CRT), plasma or liquid crystal display (LCD) monitor for displaying information to a user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to apparatus 600.

[0099] Any or all of the functions of the systems and apparatuses discussed herein may be performed by processor 610, and / or incorporated in, an apparatus or a system such as LiDAR system 300. Further, LiDAR system 300 and / or apparatus 600 may utilize one or more neural networks or other deep-learning techniques performed by processor 610 or other systems or apparatuses discussed herein.

[0100] One skilled in the art will recognize that an implementation of an actual computer or computer system may have other structures and may contain other components as well, and that FIG. 6 is a high-level representation of some of the components of such a computer for illustrative purposes.

[0101] FIGs. 7A-7C illustrate an example of blooming in a LiDAR system point cloud image caused by a reflective test target. LiDAR is an active sensor which emits laser and measures a range between the sensor and the object. The output data of a LiDAR is called a point cloud. The point cloud is similar to a 3D camera. In a point cloud, each pixel represents a distance, rather than a color.

[0102] FIG. 7A illustrates an example of a point cloud 700, e.g., a Seyond Falcon LiDAR point cloud, where point cloud data is generated based on an image of return light in a field-of-view (FOV) captured by an optical receiver. Because the LiDAR is operative to emit laser light onto various targets, e.g., the illuminated buildings 710 in point cloud 700, and in varying conditions, the resulting point cloud 700 may lead to recognition problems when the reflectivity of one or more targets is too high. For example, the point cloud 700 may include a retroreflective target, i.e., a highly reflective target that reflects light or other radiation back to its source with minimum scattering. Such a target will reflect most of its return light back to an emitting LiDAR, which can have the effect of making the target look larger in the resulting point cloud than it would otherwise be as a non-retroreflective target (i.e., a target that reflects light with typical levels of scattering). This phenomenon is referred to herein as blooming. Blooming, as used herein, can include both single pixel enlargement due to laser beam expansion and / or crosschannel optical crosstalk among different pixels.

[0103] FIG. 7B illustrates an image of a scene 720 used to generate a point cloud image, as illustrated in FIG. 7C. For example, the (yellow) target 730 is representative of a highly retroreflective target as described above, where surface characteristics or other factors cause light emitted from a LiDAR to be reflected back to the LiDAR detector with minimum scattering. In the resulting point cloud image 740, the target 730 can appear to be much taller or larger than optimal for proper recognition. For example, the first outline (white) box 750 shows where the target 730 should be in point cloud image 740, but the second (red) outline box 760 shows the observed result. Thus, FIG. 7C clearly illustrates the distortive effects of blooming, which can be caused, e.g., by optical crosstalk among different pixels. Because the return light from the retroreflective target 730 is too strong, pixels in other channels also receive the reflected light from the target channels.

[0104] FIG. 8 is a flow diagram illustrating a conventional blooming cancellation process in a LiDAR perception system. For example, a raw data LiDAR point cloud 810 may have blooming cancellation applied 820 to construct a modified LiDAR point cloud with blooming cancellation applied 830 before feature extraction 840 is used to determine a result array 850 for target object recognition. The resulting point cloud with blooming cancellation applied is more akin to a human-perceived image of a scene, e.g., a scene perceived as having a combination of light surfaces, dark surfaces, and retroreflective surfaces. This processed point cloud may then be used by object feature extraction circuitry to generate a results array of major and / or secondary object types relevant to a particular application. FIGS. 9A-9C illustrate an example of a conventional blooming cancellation method in a LiDAR system point cloud image for blooming caused by a retroreflective test target. Conventional blooming cancellation methods have been employed in LiDAR systems to improve the image quality of point clouds. FIG. 9A illustrates an image of a scene 900 having a target 910 used to generate a point cloud image, as illustrated in FIGS. 9B and 9C. FIG. 9B shows a point cloud 920 with distortive effects from blooming. For example, the first outline (white) box 930 shows where the target 910 should be in point cloud image 920, but the second (red) outline box 940 shows the observed result, which can be caused, e.g., by optical crosstalk among different pixels. In FIG. 9C, a recovered point cloud 950 is shown after blooming cancellation is used to recover the actual profile 960 of target 910. In typical LiDAR systems, blooming cancellation typically takes place before employing recognition circuitry thatis operative to determine whether images of target objects are present in the point cloud data, e.g., based on the weight probabilities.

[0105] Referring back to FIG. 8, each object recognition result in a results array 850 may include a likelihood indicator, e.g., a numerical indicator between 0 to 1, corresponding to each major and / or secondary object type. For example, in the context of a vehicle-based LiDAR system, the main object types in the results array 850 may include, e.g., a car 860, a traffic sign 870, traffic cones 880, and / or other objects 890 (e.g., including a “none of the above” result). The object recognition results for each of the main object types may include a likelihood indicator, e.g., 0.2, 0.5, 0.2, and 0.1 for the car 860, traffic sign 870, traffic cones 880, and other objects 890, respectively. For example, in the instant example, the traffic sign 870 with a corresponding likelihood indication of 0.5 would be determined to be the most likely object in the point cloud image 830.

[0106] However, the embodiments herein propose to employ blooming cancellation methods after recognition circuitry is employed to determine whether images of target objects are present in the point cloud data. As opposed to the typical methods that aim to cancel blooming effects before object recognition, the embodiments herein propose to take advantage of blooming effects by using them to help recognition circuitry determine whether images of identified target objects are present in the point cloud data.

[0107] FIGS. 10A-10D illustrate an example of blooming in a LiDAR system point cloud image caused by a reflective portion of an automotive target. For example, FIG. 10A shows a first image of a vehicle 1000 having obscured reflective portions such that the point cloud image of the vehicle 1000 is correctly rendered without blooming effects, as shown in point cloud image 1010 in FIG. 10B. FIG. 10C illustrates a second image of the vehicle 1000 where the retroreflective elements 1020A and 1020B (e.g., safety reflectors) are not obscured. In this case, the point cloud image of the vehicle 1000 is rendered with blooming effects, e.g., single pixel enlargement due to laser beam expansion and / or cross channel crosstalk, as shown in point cloud image 1030 in FIG. 10D. Particularly, the point cloud image 1030 includes two legs-like features 1040A and 1040B, as part of the point cloud image 1030 representing the vehicle 1000. However, the embodiments here can take advantage of the blooming features and increase perception accuracy because of the unique point cloud image 1030 of the vehicle 1000. In otherwords, the fact that vehicles typically include retroreflective elements (e.g., safety reflectors, light housings, etc.) that will be rendered to give the point cloud images of vehicles with such elements a unique pattern can be used to advantageously aid rather than hinder object recognition. For example, the point cloud image of a vehicle without the retroreflective elements filtered out can be more unique for recognizing a vehicle (versus other objects) and can reduce occurrences of object misrecognition / misidentification.

[0108] In an embodiment, after the raw point cloud data (without blooming cancellation), an intermediate layer of the network can be added for extracting the blooming features of a target object, where the other layers can be for processing the blooming-free portions of the target object. For example, the extracted blooming features can be used as special information to identify different types of target objects, e.g., vehicles, traffic signs, warning cones, etc. In some embodiments, the extracted blooming feature portion of the point cloud data can be used as training data to train a neural network to derive more general object type data from a point cloud image. For example, the extracted blooming feature portion of the point cloud data may be used for recognizing target objects known for having blooming-generating portions at different locations, such as a vehicle known to include retroreflective elements (e.g., safety reflectors, light housings, etc.). In another example, the level of blooming for a target object may vary when there is rain or other environmental conditions (e.g., variations in the type and condition of a roadway) that affect the point cloud data returning from the target object. In some embodiments, these variable blooming levels in the point cloud data may be used to recognize such environmental conditions while the blooming-free portion of the point cloud data may be used for general object recognition / identification. Thus, a neural network can be used for extracting the blooming features from point cloud data, as in some conventional object recognition methods, but the obtained blooming correction / cancellation data may also be used to aid object recognition / identification by helping, e.g., to identify different types of objects or different environmental conditions, or to train / derive more general object types.

[0109] In some embodiments, other point correction mechanisms, such as intensity compensation, elongation correction due to shallow approaching angle to the surface, sun or other background noise cancellation (e.g., by elevating background level / noise filters, etc.), may be used in a way similar to the use of blooming data described herein. For example, raw point cloud data that includes uncorrected / unfiltered data indicative of fog, dust, rain splash inrainy weather, etc., can also be used to identify different types of objects or different environmental conditions, or to train / derive more general object types. As discussed above, a neural network can be used to extract and decouple such features, or the deterministic method herein can be used as part of an object recognition / identification process.

[0110] In one embodiment, a LiDAR system may comprise image processing circuitry operative to generate point cloud data based on an image of return light in a field-of-view (FOV) captured by an optical receiver, where the point cloud data comprises one or more blooming features. The LiDAR system may further comprise feature extraction circuitry, coupled to the image processing circuitry, operative to weight probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features, and provide the weight probabilities to recognition circuitry operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities.

[0111] FIG. 11 is a flow diagram illustrating a process for LiDAR perception in accordance with an embodiment. The process 1100 illustrated in FIG. 11 takes advantage of blooming features included in an imaged scene, e.g., the scene including the vehicle 1000 illustrated in FIG. 10C, that are returned in raw point cloud data for object recognition and identification. For example, a LiDAR point cloud image 1110 without blooming feature cancellation, e.g., the LiDAR point cloud image 1030 illustrated in FIG. 10D, is passed directly to feature extraction circuitry, where, for example, the object(s) with blooming features 1 120 (e g., blooming features 1040A and 1040B illustrated in FIG. 10D) may contain shape and / or intensity distribution. The feature extraction circuitry is then operative to output a result array 1130 directly based on the raw point cloud data. For example, the image processing circuitry may be operative to weight probabilities of images of target objects, e.g., the probability of image 1131 corresponding to a car 1132, the probability of image 1133 corresponding to a traffic sign 1134, the probability of image 1135 corresponding to traffic cones 1136, or other objects 1138, being present in the raw point cloud data containing blooming features based at least in part on one or more of the blooming features. The weighted probabilities may then be provided to circuitry for further processing, e.g., to recognition circuitry operative to determine whether images of particular target objects are present in the point cloud data based on the weighted probabilities.

[0112] FIG. 12 is a block diagram of a system 1200 for LiDAR perception in accordance with an embodiment. LiDAR system 1200 may comprise image processing circuitry 1210, feature extraction circuitry 1220, or recognition circuitry 1230.

[0113] In an embodiment, the image processing circuitry 1210 may be operative to generate point cloud data based on an image of return light, e.g., return light that is a consequence of a light transmission in a field-of-view (FOV) captured by an optical receiver. For example, the point cloud data generated by the image processing circuitry 1210 may comprise one or more blooming features, e.g., blooming effects caused by single pixel enlargement due to return light beam expansion, cross-channel optical crosstalk, or the like.

[0114] The feature extraction circuitry 1220 may be coupled to the image processing circuitry 1210 may be operative to use one or more feature extraction algorithms and / or one or more bloom filters to recognize the one or more blooming features and / or a neural network (i.e., machine learning) to recognize the one or more blooming features. For example, feature extraction circuitry 1220 may comprise a neural network trained to recognize the one or more blooming features in the point cloud data based on, e.g., shape or intensity distribution. In some embodiments, the feature extraction circuitry 1220 may be operative to determine a blooming perception parameter based on at least one environmental condition (e.g., rain, snow, fog, hail, a type of road, or a condition of a road, etc.) present within the FOV, and weight probabilities of images of target objects being present in the point cloud data based at least in part on the blooming perception parameter. In addition, or alternatively, the feature extraction circuitry 1220 may be operative to extract blooming correction or cancellation data (e.g., intensity compensation data, elongation correction data, sun or other background noise cancellation data, noise filter data, or the like) from the point cloud data, and recognize the one or more blooming features in the point cloud data based on the blooming correction or cancellation data. In an embodiment, the feature extraction circuitry 1220 may be operative to weight probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features and provide the weight probabilities to the recognition circuitry 1230.

[0115] The recognition circuitry 1230 may be operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities. For example, the weight probabilities may each be represented by a corresponding numerical value between 0 and1 (e g., 0.2, 0.5, 0.8, etc.). In the context of a vehicle-based LiDAR system, as discussed in reference to FIG. 8 above, the main object types in the results array 850 may include, e.g., a car 860, a traffic sign 870, traffic cones 880, and / or other objects 890 (e.g., including a “none of the above” result). Thus, the object recognition results for each of the main object types may include a likelihood indicator, e.g., 0.2, 0.5, 0.2, and 0.1 for the car 860, traffic sign 870, traffic cones 880, and other objects 890, respectively. For example, in the instant example of FIG. 8, the traffic sign 870 with a corresponding likelihood indication of 0.5 would be determined to be the most likely object in the point cloud image 830.

[0116] FIG. 13 is a flow diagram illustrating a process 1300 for LiDAR perception in accordance with an embodiment. For example, a vehicle may comprise a system for light ranging and detection (LiDAR) (i.e., a LiDAR system). In an embodiment, the LiDAR system may comprise image processing circuitry, e.g., image processing circuitry 1210, operative to generate point cloud data based on an image of return light that is a consequence of a light transmission in a field-of-view (FOV) captured by an optical receiver at block 1310, where the point cloud data comprises one or more blooming features. The LiDAR system may further comprise feature extraction circuitry, e.g., feature extraction circuitry 1220, coupled to the image processing circuitry. At block 1320, the feature extraction circuitry is operative to weight probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features. In some embodiments, the feature extraction circuitry may be further operative to use at least one blooming feature extraction algorithm to recognize the one or more blooming features. Further, the feature extraction circuitry may be further operative to use a neural network (i.e., machine learning) to recognize the one or more blooming features. For example, the neural network may be trained to recognize the one or more blooming features in the point cloud data based on at least one of a shape or intensity distribution. As discussed above, the one or more blooming features may comprise at least one of single pixel enlargement due to return light beam expansion, cross-channel optical crosstalk, or the like. In some embodiments, the feature extraction circuitry is further operative to determine a blooming perception parameter based on at least one environmental condition (e.g., rain, snow, fog, hail, a type of road, or a condition of a road, etc.) present within the FOV. For example, the feature extraction circuitry may be further operative to weight probabilities of images of target objects being present in the point cloud data based at least in part on theblooming perception parameter. In addition, or alternatively, the feature extraction circuitry may be further operative to extract blooming correction or cancellation data (e.g., intensity compensation data, elongation correction data, sun or other background noise cancellation data, noise filter data, or the like) from the point cloud data. For example, the feature extraction circuitry may be further operative to recognize the one or more blooming features in the point cloud data based on the blooming correction or cancellation data.

[0117] At block 1330, the feature extraction circuitry is further operative to provide the weight probabilities to recognition circuitry, e.g., recognition circuitry 1230, operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities. For example, the weight probabilities may each be represented by a corresponding numerical value between 0 and 1 (e.g., 0.2, 0.5, 0.8, etc.). In some embodiments, the recognition circuitry may be further operative to identify images of different types of target objects based on the weight probabilities at block 1340. For example, in the context of a vehicle-based LiDAR system, the different types of target objects may comprise at least one of a car, a traffic sign, a traffic cone, or other types of objects relevant to the particular context of the LiDAR system.

[0118] Additional embodiments are described below.

[0119] (1) A system for light ranging and detection (LiDAR), comprising: image processing circuitry operative to generate point cloud data based on an image of return light that is a consequence of a light transmission in a field-of-view (FOV) captured by an optical receiver, wherein the point cloud data comprises one or more blooming features; and feature extraction circuitry coupled to the image processing circuitry, the feature extraction circuitry operative to: weight probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features; and provide the weight probabilities to recognition circuitry operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities.

[0120] (2) The system of (1), wherein the recognition circuitry is further operative to identify images of different types of target objects based on the weight probabilities.

[0121] (3) The system of (2), wherein the different types of target objects comprise at least one of the following objects: a car, a sign, or a cone.

[0122] (4) The system of any one of ( 1 )-(3), wherein the weight probabilities are each represented by a numerical value between 0 and 1.

[0123] (5) The system of any one of (1 )-(4), wherein the feature extraction circuitry is further operative to use at least one blooming feature extraction algorithm to recognize the one or more blooming features.

[0124] (6) The system of any one of ( 1 )-(5), wherein the feature extraction circuitry is further operative to use a neural network to recognize the one or more blooming features.

[0125] (7) The system of 6, wherein the neural network is trained to recognize the one or more blooming features in the point cloud data based on at least one of a shape or intensity distribution.

[0126] (8) The system of any one of (1 )-(7), wherein the one or more blooming features comprise at least one of the following features: single pixel enlargement due to return light beam expansion, or cross-channel optical crosstalk.

[0127] (9) The system of any one of ( 1 )-(8), wherein the feature extraction circuitry is further operative to determine a blooming perception parameter based on at least one environmental condition present within the FOV.

[0128] (10) The system of (9), wherein the at least one environmental condition comprises at least one of the following: rain, snow, fog, hail, a type of road, or a condition of a road.

[0129] (11) The system of (9), wherein the feature extraction circuitry is further operative to weight probabilities of images of target objects being present in the point cloud data based at least in part on the blooming perception parameter.

[0130] (12) The system of any one of (l)-(l 1), wherein the feature extraction circuitry is further operative to extract blooming correction or cancellation data from the point cloud data.

[0131] (13) The system of (12), wherein the feature extraction circuitry is further operative to recognize the one or more blooming features in the point cloud data based on the blooming correction or cancellation data.

[0132] (14) The system of (12), wherein the blooming correction or cancellation data comprises one or more of the following: intensity compensation data, elongation correction data, sun or other background noise cancellation data, or noise filter data.

[0133] (15) A vehicle comprising: a system for light ranging and detection (LiDAR), comprising: image processing circuitry operative to generate point cloud data based on an image of return light that is a consequence of a light transmission in a field-of-view (FOV) captured by an optical receiver, wherein the point cloud data comprises one or more blooming features; and feature extraction circuitry coupled to the image processing circuitry, the feature extraction circuitry operative to: weight probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features; and provide the weight probabilities to recognition circuitry operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities.

[0134] (16) A method for blooming perception in a LiDAR system, the method comprising: generating, by image processing circuitry, point cloud data based on an image of return light in a field-of-view (FOV) captured by an optical receiver, wherein the point cloud data comprises one or more blooming features; weighting, by feature extraction circuitry coupled to the image processing circuitry, probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features; and providing the weight probabilities to recognition circuitry operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities.

[0135] (17) A non-transitory computer-readable medium having computer instructions stored thereon, which, when executed by one or more processors, cause the one or more processors to perform one or more operations comprising:generating, by image processing circuitry, point cloud data based on an image of return light in a field-of-view (FOV) captured by an optical receiver, wherein the point cloud data comprises one or more blooming features; weighting, by feature extraction circuitry coupled to the image processing circuitry, probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features; and providing the weight probabilities to recognition circuitry operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities.

[0136] The foregoing specification is to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined from the specification, but rather from the claims as interpreted according to the full breadth permitted by the patent laws. It is to be understood that the embodiments shown and described herein are only illustrative of the principles of the present invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention. Those skilled in the art could implement various other feature combinations without departing from the scope and spirit of the invention.

Claims

CLAIMS1. A system for light ranging and detection (LiDAR), comprising: image processing circuitry operative to generate point cloud data based on an image of return light that is a consequence of a light transmission in a field-of-view (FOV) captured by an optical receiver, wherein the point cloud data comprises one or more blooming features; and feature extraction circuitry coupled to the image processing circuitry, the feature extraction circuitry operative to: weight probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features; and provide the weight probabilities to recognition circuitry operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities.

2. The system of embodiment 1, wherein the recognition circuitry is further operative to identify images of different types of target objects based on the weight probabilities.

3. The system of embodiment 2, wherein the different types of target objects comprise at least one of the following objects: a car, a sign, or a cone.

4. The system of any one of embodiments 1-3, wherein the weight probabilities are each represented by a numerical value between 0 and 1.

5. The system of any one of embodiments 1-4, wherein the feature extraction circuitry is further operative to use at least one blooming feature extraction algorithm to recognize the one or more blooming features.

6. The system of any one of embodiments 1-5, wherein the feature extraction circuitry is further operative to use a neural network to recognize the one or more blooming features.

7. The system of embodiment 6, wherein the neural network is trained to recognize the one or more blooming features in the point cloud data based on at least one of a shape or intensity distribution.

8. The system of any one of embodiments 1-7, wherein the one or more blooming features comprise at least one of the following features: single pixel enlargement due to return light beam expansion, or cross-channel optical crosstalk.

9. The system of any one of embodiments 1-8, wherein the feature extraction circuitry is further operative to determine a blooming perception parameter based on at least one environmental condition present within the FOV.

10. The system of embodiment 9, wherein the at least one environmental condition comprises at least one of the following: rain, snow, fog, hail, a type of road, or a condition of a road.

11. The system of embodiment 9, wherein the feature extraction circuitry is further operative to weight probabilities of images of target objects being present in the point cloud data based at least in part on the blooming perception parameter.

12. The system of any one of embodiments 1-11, wherein the feature extraction circuitry is further operative to extract blooming correction or cancellation data from the point cloud data.

13. The system of embodiment 12, wherein the feature extraction circuitry is further operative to recognize the one or more blooming features in the point cloud data based on the blooming correction or cancellation data.

14. The system of embodiment 12, wherein the blooming correction or cancellation data comprises one or more of the following: intensity compensation data, elongation correction data, sun or other background noise cancellation data, or noise filter data.

15. A vehicle comprising: a system for light ranging and detection (LiDAR), comprising: image processing circuitry operative to generate point cloud data based on an image of return light that is a consequence of a light transmission in a field-of-view (FOV) captured by an optical receiver, wherein the point cloud data comprises one or more blooming features; and feature extraction circuitry coupled to the image processing circuitry, the feature extraction circuitry operative to:weight probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features; and provide the weight probabilities to recognition circuitry operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities.

16. A method for blooming perception in a LiDAR system, the method comprising: generating, by image processing circuitry, point cloud data based on an image of return light in a field-of-view (FOV) captured by an optical receiver, wherein the point cloud data comprises one or more blooming features; weighting, by feature extraction circuitry coupled to the image processing circuitry, probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features; and providing the weight probabilities to recognition circuitry operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities.

17. A non-transitory computer-readable medium having computer instructions stored thereon, which, when executed by one or more processors, cause the one or more processors to perform one or more operations comprising: generating, by image processing circuitry, point cloud data based on an image of return light in a field-of-view (FOV) captured by an optical receiver, wherein the point cloud data comprises one or more blooming features; weighting, by feature extraction circuitry coupled to the image processing circuitry, probabilities of images of target objects being present in the point cloud data based at least in part on the one or more blooming features; and providing the weight probabilities to recognition circuitry operative to determine whether images of target objects are present in the point cloud data based on the weight probabilities.

Images