Dynamic calibration of lidar system

Abstract

Embodiments of the present disclosure provide dynamic calibration systems, methods, and computer program products for dynamic calibration of a LiDAR system in which calibration can be checked while the vehicle is moving in a typical environment (e.g., while a car is driving on a road in a rural or urban setting). In some embodiments, line features in an operating environment of a vehicle to which the LiDAR system is mounted are used to perform dynamic calibration. In some embodiments, planar features in an operating environment of a vehicle to this the LiDAR system is mounted are used to perform dynamic calibration. These and other embodiments are more fully disclosed herein.

Description

DYNAMIC CALIBRATION OF LIDAR SYSTEMCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Application No. 63 / 614,890 filed on December 26, 2023. The contents of this application are hereby incorporated by reference in their entireties for all purposes.FIELD OF THE TECHNOLOGY

[0002] This disclosure relates generally to light detection and, more particularly, to dynamic calibration of a light detection and ranging (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 illuminates an entire field-of-view (FOV) rather than scanning through the FOV. An example of the non-scanning LiDAR system is a flash LiDAR, which can also use the ToF technique to measure the distance to an object. LiDAR systems can also use techniques other than time-of-flight and scanning to measure the surrounding environment.

[0004] Many LiDAR systems are made sufficiently compact to attach to a movable platform (e.g., a car or other vehicle) and the data generated from the LiDAR system may be used to control movement of the movable platform.SUMMARY

[0005] When a LiDAR system is attached to a vehicle (or other moveable platform) and used to aid in controlling that vehicle, it must be calibrated so that its position data is generated based on the frame of reference of the vehicle relative to the LiDAR system. However, when a vehicle operates over time, cumulative effects of vibrations, temperature changes, humidity, and / or other disturbances might change the position of the LiDAR system relative to the vehicle on which it is mounted. The LiDAR systems calibration might therefore degrade over time. Typically, calibration issues can be identified and addressed at a factory or service center in a predefined environment with pre-determined objects used as a frame of reference.

[0006] However, there is a need to identify and, in some cases, address calibration issues dynamically without having to bring the vehicle to a service center. Embodiments of the present disclosure provide for a dynamic LiDAR calibration system in which calibration can be checked while the vehicle is moving in a typical environment (e.g., while a car is driving on a road in a rural, urban, or other setting).BRIEF DESCRIPTION OF THE DRAWINGS

[0007] 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.

[0008] FIG. 1 illustrates one or more example LiDAR systems disposed or included in a motor vehicle.

[0009] FIG. 2 is a block diagram illustrating interactions between an example LiDAR system and multiple other systems including a vehicle perception and planning system.

[0010] FIG. 3 is a block diagram illustrating an example LiDAR system.

[0011] FIG. 4 is a block diagram illustrating a multimodal detection system according to some embodiments.

[0012] FIG. 5A is a block diagram illustrating an example fiber-based laser source.

[0013] FIG. 5B is a block diagram illustrating an example semiconductor-based laser source.

[0014] FIG. 6 illustrates an example light collection and distribution device, according to some embodiments.

[0015] FIG. 7 illustrates an example signal separation device, according to some embodiments.

[0016] FIG. 8 illustrates configurations of integrated sensors of a multimodal sensor, according to various embodiments.

[0017] FIG. 9 illustrates example packaging configurations for integrated sensors of a multimodal sensor, according to various embodiments.

[0018] FIGs. 10A-10C illustrate an example LiDAR system using pulse signals to measure distances to objects disposed in a field-of-view (FOV).

[0019] FIG. 11 is a block diagram illustrating an example apparatus used to implement systems, apparatus, and methods in various embodiments.

[0020] FIG. 12 is a block diagram illustrating a moveable platform having a LiDAR system having extrinsic degradations with respect to the moveable platform, according to various embodiments.

[0021] FIGs. 13 and 14 are flowcharts illustrating methods for detecting extrinsic calibration degradation of a LiDAR system mounted to a moveable platform, according to various embodiments.

[0022] FIGs. 15A-15E are diagrams illustrating an example method of detecting extrinsic calibration degradation of a LiDAR system using parallel line features extending along a road surface, according to various embodiments.

[0023] FIGs. 15F is a diagram illustrating an example method of detecting extrinsic calibration degradation of a LiDAR system using curved features extending along a road surface, according to various embodiments.

[0024] FIG. 16 is a flowchart illustrating a method for detecting extrinsic calibration degradation of a LiDAR system mounted to a moveable platform, according to various embodiments.

[0025] FIG. 17 is a diagram illustrating examples of detecting extrinsic calibration degradation of a LiDAR system using planar features identified in a driving environment.DETAILED DESCRIPTION

[0026] 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.

[0027] Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise:

[0028] 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.

[0029] 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.

[0030] The term “based on” is not exclusive and allows for being based on additional factors not described unless the context clearly dictates otherwise.

[0031] 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.

[0032] Although the following description uses the terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first light signal could be termed a second light signal and, similarly, a second light signal could be termed a first light signal, without departing from the scope of the various described examples. The first light signal and the second light signal can both be light signal and, in some cases, can be separate and different light signals.

[0033] In addition, throughout the specification, the meaning of “a”, “an”, and “the” includes plural references, and the meaning of “in” includes “in” and “on”.

[0034] Although some of the various embodiments presented herein constitute a single combination of inventive elements, it should be appreciated that the inventive subject matter is considered to include all possible combinations of the disclosed elements. As such, if one embodiment comprises elements A, B, and C, and another embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly discussed herein. Further, the transitional term “comprising” means to have as parts or members, or to be those parts or members. As used herein, the transitional term “comprising” is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.

[0035] 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.

[0036] 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.

[0037] As described above, a LiDAR system may include an integrated LiDAR sensor and one or more other sensors to form a multimodal sensor. The multimodal sensor may be configured to be compact so that it can be easily mounted to a moveable platform like a vehicle. A LiDAR system may have one or more degradation factors that affect its performance over time. LiDAR system extrinsic calibration degradation is one such example.

[0038] Extrinsic calibration parameters may degrade as the moveable platform (e g., a vehicle) operates in harsh environmental conditions throughout its life. The extrinsic calibration measures the relation between the LiDAR system and the moveable platform to which the LiDAR system is mounted. When the LiDAR system is first manufactured and mounted to the moveable platform, the LiDAR system is calibrated to have the correct position and orientation such that it can operate to accurately detect objects in its FOV. Over time, the extrinsic calibration of the LiDAR system may change due to various factors like environmental conditions, wear and tear, user induced errors, etc. Accordingly, the extrinsic calibration of the LiDAR system may degrade over time, and in turn the performance of the LiDAR system may be negatively affected.

[0039] Embodiments of the present invention are described below. In various embodiments of the present invention, a Light Detection and Ranging (LiDAR) system configured for detecting signals with multiple wavelengths is provided. The LiDAR system comprises a laser light sourceproviding laser light signals; an aperture window; and one or more steering mechanisms configured to perform: directing the laser light signals toward the aperture window, receiving first return light signals formed based on at least a portion of the laser light signals provided by the laser light source, and receiving second return light signals formed from light provided by one or more light sources external to the LiDAR system. The LiDAR system comprises a LiDAR sensor. The LiDAR sensor is configured to detect the first return light signals to obtain one or more frames of point cloud data. The LiDAR system further includes a controller configured to perform: detecting one or more extrinsic calibration degradation factors affecting the LiDAR system’s performance, in response to detecting the one or more degradation factors, and causing adjustment of a device configuration or an operational condition of the LiDAR system to remove or reduce effects of the degradation factors. Example LiDAR systems and various technologies for detecting performance degradations are described below in greater detail, beginning with description of a LiDAR system, which is often included in a LiDAR system.

[0040] 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 thedriver to take over when necessary. A driverless vehicle can operate on its own without any driver intervention.

[0041] 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.

[0042] 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-1201. 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 corner; 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 comer; 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 left comer; 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.

[0043] In some embodiments, LiDAR systems 110 and 120A-120I are independent LiDAR systems having their own respective laser sources, control electronics, transmitters, receivers, and / or steering mechanisms. In other embodiments, some of LiDAR 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.

[0044] 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 system 220. 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.

[0045] 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.

[0046] 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, or the 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.).

[0047] Other vehicle onboard sensor(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.).

[0048] 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 via communication 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.).

[0049] 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 positioningsystems (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.

[0050] 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).

[0051] 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.

[0052] 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.

[0053] 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 communications described above are examples of vehicle-to-X (V2X) communications, where the “X” represents any other devices, systems, sensors, infrastructure, or the like that can share data with a vehicle.

[0054] 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 asensor 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.

[0055] 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.

[0056] 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).

[0057] 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).

[0058] 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 the 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.

[0059] 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 their moving 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 Filterbased models, recurrent neural network (RNN) based models, long short-term memory (LSTM) neural network based models, encoder-decoder RNN models, or the like.

[0060] 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.

[0061] 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. It is 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.

[0062] 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 control circuitry 350. Thesecomponents are coupled together using communications paths 312, 314, 322, 332, 342, 352, 362, and 372. 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).

[0063] 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 330 of the LiDAR system 300. The second portion is also referred to as the light that is kept local (LO) to the LiDAR system 300. The transmission light is scattered or reflected by various objects in the FOV and at least a portion of it forms return light. The return light is subsequently detected and interferometrically recombined with the second portion of the transmission light that was kept local. Coherent LiDAR provides a means of optically sensing an object’s range as well as its relative velocity along the line-of-sight (LOS).

[0064] 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 communicationconnections 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.

[0065] 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 700 nm 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 vertical - extemal-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.

[0066] 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 laser diode (directly or using a fiber) to obtain the desired output power. A microchip laser can be based on neodymium-doped yttrium aluminum garnet (Y3AI5O12) laser crystals (i.e., Nd:YAG), or neodymium-doped vanadate (i.e., NDiYVCL) 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 controlledsuch that the power gain can be varied to achieve any desired laser output power. An example of light source 310 is described in more detail below.

[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, multiple LiDAR systems may be attached to a vehicle, which may be an electrical-powered vehicle or a vehicle otherwise having limited fuel or battery power supply. Therefore, high WPE and intelligent ways to use laser power are often among the important considerations when selecting and configuring light 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 someexamples, 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 or a lens group configured to collect the diverging laser beams and produce more parallel optical beams with reduced or minimum divergence. The collimated optical beams can then be further directed through various optics such as mirrors and lens. A collimating lens may be, for example, a single plano-convex lens or a lens group. The collimating lens can be configured to achieve any desired properties such as the beam diameter, divergence, numerical aperture, focal length, or the like. A beam propagation ratio or beam quality factor (also referred to as the M2factor) is used for measurement of laser beam quality. In many LiDAR applications, it is important to have good laser beam quality in the generated transmitting laser beam. The M2factor represents a degree of variation of a beam from an ideal Gaussian beam. Thus, the M2factor reflects how well a collimated laser beam can be focused on a small spot, or how well a divergent laser beam can be collimated. Therefore, light 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 an undoped intrinsic semiconductor region (i.e., an “i” region) between a p-type semiconductor and an n-type semiconductor region. Other light detector structures comprise, for example, an APD (avalanche photodiode) based structure, a PMT (photomultiplier tube) based structure, a SiPM (Silicon photomultiplier) based structure, a SPAD (single-photon avalanche diode) based structure, and / or quantum wires. For material systems used in a light detector, Si, InGaAs, and / or Si / Ge based materials can be used. It is understood that many other detector structures and / or material systems can be used in optical receiver and light 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 used for a light detector. Coherent detection allows for detecting amplitude and phase information of the received light by interfering the received light with a local oscillator. Coherent detection can improve detection sensitivity and noise immunity.

[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 also referred to as a raster mechanism, a scanning mechanism, or simply a light scanner. Scanning light beams in multiple directions (e.g., in both the horizontal and vertical directions) facilitates a LiDAR system to map the environment by generating an image or a 3D point cloud. A steering mechanism can be based onmechanical 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 combinationthereof, 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 circuitry 350 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. Control circuitry 350 may include digital and / or analog circuitry (e.g., ADC, amplifier, filter, etc.) for processing data representing return light signals received by a LiDAR system or a LiDAR system.

[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 componentsnot 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, 362, and 372. 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] FIG. 4 is a block diagram illustrating an exemplary multimodal detection system 400 with integrated sensors, according to various embodiments. Multimodal detection system 400 can be a part of a LiDAR system (e.g., system 300) or includes a part of a LiDAR system (e.g., system 300). System 400 can also include one or more other sensors such as cameras. In one example where system 400 includes a LiDAR sensor and an image sensor (or includes a LiDAR sensor and one or more other types of sensors), it can be referred to as a Hybrid Detection and Ranging (LiDAR) system. As shown in FIG. 4, in some embodiments, on the transmission side, system 400 can include a light source 402, a transmitter 404, and a steering mechanism 406. These components can form a transmission light path. On the receiver side, system 400 can include an optical receiver and light detector 430, which comprises one or more of a light collection and distribution device 410, a signal separation device 440, and a multimodal sensor 450. In some examples, steering mechanism 406 is also used for receiving light signals from the FOV 470. Therefore, steering mechanism 406 and optical receiver and light detector 430 can form a receiving light path. Light source 402, transmitter 404, and steering mechanism 406 canbe substantially the same or similar as light source 310, transmitter 320, and steering mechanism 340, respectively, as described above in connection with FIG. 3.

[0086] In some examples, light source 402 is an internal light source that generates light for the multimodal detection system 400. Examples of internal light sources include active illumination devices such as laser (e.g., fiber laser or semiconductor based laser used in one or more LiDAR transmission channels of system 400), light emitting diodes, headlights / taillights, etc. An example of light source 402 is described below in more detail in connection with FIGs. 5A and 5B. In some examples, system 400 also receives light from light sources that are external to system 400. These external light sources may not be a part of the system 400. Examples of external light sources include sunlight, streetlight, and other illuminations from light sources external to system 400 (e.g., light from other LiDARs).

[0087] As illustrated in FIG. 4, light generated by light source 402 (e.g., laser from a LiDAR system) is provided to transmitter 404. The light generated by light source 402 can include visible light, near infrared (NIR) light, short wavelength IR (SWIR) light, medium wavelength IR (MWIR) light, long wavelength IR (LWIR) light, and / or light in any other wavelengths. The visible light has a wavelength range of about 400 nm - 700 nm; the near infrared (NIR) light has a wavelength range of about 700 nm - 1.4 pm; the short- wavelength infrared (SWIR) has a wavelength range of about 1.4 pm - 3 pm; the mid-wavelength infrared (MWIR) has a wavelength range of about 3 pm - 8 pm; and a long- wavelength infrared (LWIR) has a wavelength range of about 8 pm - 15 pm.

[0088] FIG. 5A is a block diagram illustrating an example fiber-based laser source 500 for implementing light source 310 depicted in FIG. 3 and / or light source 402 depicted in FIG. 4. Fiber-based laser source 500 has a seed laser and one or more pumps (e.g., laser diodes) for pumping desired output power. In some embodiments, fiber-based laser source 500 comprises a seed laser 502 configured 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) 504 via an optical fiber 503. Fiber-based laser source 500 further comprises a pump 506 for providing laser power (e.g., of a different wavelength, such as 980 nm) to WDM 504 via an optical fiber 505. WDM 504 multiplexes the light pulses provided by seed laser 502 and the laser power provided by pump 506 onto a single optical fiber 507. The output of WDM 504 canthen be provided to one or more pre-amplifier(s) 508 via optical fiber 507. Pre-amplifier(s) 508 can be optical amplifier(s) that amplify optical signals (e.g., with about 10-30 dB gain). In some embodiments, pre-amplifier(s) 508 are low noise amplifiers. Pre-amplifier(s) 508 output to an optical combiner 510 via an optical fiber 509. Combiner 510 combines the output laser light of pre-amplifier(s) 508 with the laser power provided by pump 512 via an optical fiber 511. Combiner 510 can combine optical signals having the same wavelength or different wavelengths. One example of a combiner is a WDM. Combiner 510 provides combined optical signals to a booster amplifier 514, which produces output light pulses via optical fiber 515. The booster amplifier 514 provides further amplification of the optical signals (e.g., another 20-40 dB). The output light pulses can then be transmitted to transmitter 320, transmitter 404, steering mechanism 340, and / or steering mechanism 406 (shown in FIGs. 3 and 4). It is understood that FIG. 5A illustrates one example configuration of fiber-based laser source 500. Laser source 500 can have many other configurations using different combinations of one or more components shown in FIG. 5A and / or other components not shown in FIG. 5A (e.g., other components such as power supplies, lens(es), filters, splitters, combiners, etc.).

[0089] In some variations, fiber-based laser source 500 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 500. Communication path 312 couples fiber-based laser source 500 to control circuitry 350 (shown in FIG. 3) so that components of fiber-based laser source 500 can be controlled by or otherwise communicate with control circuitry 350. Alternatively, fiber-based laser source 500 may include its own dedicated controller. Instead of control circuitry 350 communicating directly with components of fiber-based laser source 500, a dedicated controller of fiber-based laser source 500 communicates with control circuitry 350 and controls and / or communicates with the components of fiber-based laser source 500. Fiber-based laser source 500 can also include other components not shown, such as one or more power connectors, power supplies, and / or power lines.

[0090] FIG. 5B is a block diagram illustrating an example semiconductor-based laser source 540. Semiconductor-based laser source 540 is an example of light source 310 depicted in FIG. 3 and / or light source 402 depicted in FIG. 4. In the example shown in FIG. 5B, laser source 540 is a Vertical-Cavity Surface-Emitting Laser (VCSEL), which is a type of semiconductor laser diode with a distinctive structure that allows it to emit light vertically from the surface of the chip,rather than through the edge of the chip like the edge-emitting laser (EEL) diodes. VCSELs have advantages like high-speed operation and easy integration into semiconductor devices. FIG. 5B shows a cross-sectional view of an example VCSEL 540. In this example, the VCSEL 540 includes a metal contact layer 542, an upper Bragg reflector 544, an active region 546, a lower Bragg reflector 548, a substrate 550, and another metal contact 552. In the VCSEL 540, the metal contacts 542 and 552 are for making electrical contacts so that electrical current and / or voltage can be provided to VCSEL 540 for generating laser light. The substrate layer 550 is a semiconductor substrate, which can be, for example, a gallium arsenide (GaAs) substrate. VCSEL 540 uses a laser resonator, which includes two distributed Bragg reflector (DBR) reflectors (i.e., upper Bragg reflector 544 and lower Bragg reflector 548) with an active region 546 sandwiched between the DBR reflectors. The active region 546 includes, for example, one or more quantum wells for the laser light generation. The planar DBR-reflectors can be mirrors having layers with alternating high and low refractive indices. Each layer has a thickness of a quarter of the laser wavelength in the material, yielding intensity reflectivities above e.g., 99%. High reflectivity mirrors in VCSELs can balance the short axial length of the gain region. In one example of VCSEL 540, the upper and lower DBR reflectors 544 and 548 can be doped as p- type and n-type materials, forming a diode junction. In another example, the p-type and n-type regions may be embedded between the reflectors, requiring a more complex semiconductor process to make electrical contact to the active region, but eliminating electrical power loss in the DBR structure. The active region 546 is sandwiched between the DBR reflectors 544 and 548 of the VCSEL 540. The active region is where the laser light generation occurs. The active region 546 typically has a quantum well or quantum dot structure, which contains the gain medium responsible for light amplification. When an electric current is applied to the active region 546, it generates photons by stimulated emission. The distance between the upper and lower DBR reflectors 544 and 548 defines the cavity length of the VCSEL 540. The cavity length in turn determines the wavelength of the emitted light and influences the laser's performance characteristics. When an electrical current is applied to the VCSEL 540, it generates light that bounces between the DBR reflectors 544 and 548 and exits the VCSEL 540 through, for example, the lower DBR reflector 548, producing a highly coherent and vertically emitted laser beam 554. VCSEL 540 can provide an improved beam quality, low threshold current, and the ability to produce single-mode or multi-mode output.

[0091] In some variations, VCSEL 540 can be controlled (e.g., by control circuitry 350) to produce pulses of different amplitudes. Communication path 312 couples VCSEL 540 to control circuitry 350 (shown in FIG. 3) so that components of VCSEL 540 can be controlled by or otherwise communicate with control circuitry 350. Alternatively, VCSEL 540 may include its own dedicated controller. Instead of control circuitry 350 communicating directly with components of VCSEL 540, a dedicated controller of VCSEL 540 communicates with control circuitry 350 and controls and / or communicates with the components of VCSEL 540. VCSEL 540 can also include other components not shown, such as one or more power connectors, power supplies, and / or power lines.

[0092] VCSEL 540 can be used to generate laser pulses or continuous wave (CW) lasers. To generate laser pulses, control circuitry 350 modulates the current supplied to the VCSEL 540. By rapidly turning the supply current on and off, pulses of laser light can be generated. The duration, repetition rate, and shape of the pulses can be controlled by adjusting the modulation parameters. As another example, VCSEL 540 can also be a mode-locked VCSEL that uses a combination of current modulation and optical feedback to obtain ultra-short pulses. The mode-locked VCSEL may also be controlled to synchronize the phases of the laser modes to produce very short and high-intensity pulses. As another example, VCSEL 540 can use Q-Switching techniques, which includes an optical switch in the laser cavity, temporarily blocking the lasing action and allows energy to build up in the cavity. When the switch is opened, a high-intensity pulse is emitted. As another example, VCSEL 540 can also have external modulation performed by an external modulator (not shown), such as an electro-optic or acousto-optic modulator. The external modulation can be used in combination with the VCSEL itself to create pulsed output. The external modulator can be used to control the pulse duration and repetition rate. The type of VCSEL used as at least a part of light source 310 or light source 402 depends on the application and the required pulse characteristics, such as pulse duration, repetition rate, and peak power.

[0093] With reference back to FIG. 4, multimodal detection system 400 includes a transmitter 404. In some examples, transmitter 404 can include one or more transmission channels, each carrying a light beam. The transmitter 404 may also include one or more optics (e.g., mirrors, lens, fiber arrays, etc.) and / or electrical components (e.g., PCB board, power supply, actuators, etc.) to form the transmission channels. Transmitter 404 can transmit the light from each channel to a steering mechanism 406, which scans the light from each channel to an FOV 470.Steering mechanism 406 may include one or more optical or electrical scanners configured to perform at least one of a point scan or a line scan of the FOV 470.

[0094] Light source 402, transmitter 404, and steering mechanism 406 may be a part of a LiDAR or LiDAR system that scans light into FOV 470. The scanning performed by steering mechanism 406 can include, for example, line scanning and / or point scanning. For example, the steering mechanism 406 can be configured to scan all points in lines or an area; scan some points in certain lines or an area, while skip scanning other points; or scan certain lines while skipping other lines. As another example, the steering mechanism 406 of multimodal detection system 400 can be configured to scan certain points / lines in higher resolution while scan other points / lines in lower resolution. For instance, the high resolution scanning may be applied to regions of interest (ROIs) while the low resolution scanning or no scanning may be applied to other regions of the FOV. In some embodiments, to scan an ROI, the steering mechanism 406 containing one or more optical or electrical scanners can be controlled to have different characteristics than those for scanning a non-ROI. For instance, for scanning the ROI, a scanner may be controlled to have slower scanning rate and / or a smaller scanning step, thereby increasing the scanning resolution. Furthermore, the light source 402 may also be configured to increase the pulse repetition rate, thereby increasing the scanning resolution.

[0095] With reference to FIG. 4, in some embodiments, if a sensor in a multimodal detection system 400 does not require actively transmitting light and / or scanning the light, one or more of light source 402, transmitter 404, and steering mechanism 406 may not be required for that particular sensor. For example, if system 400 includes a passive image sensor or video sensor (e.g., a camera), it may not require actively sending out light and / or scanning light to the FOV in order to form an image of the FOV. In this disclosure, the terms “image sensor” and “video sensor” are used interchangeably, both referring to a passive sensor that can capture images and / or videos. As a passive sensor, the image sensor may just sense light from the FOV and use the sensed light to form an image. It may not transmit light out to the FOV itself. In some other examples, the image sensor may require a light source (e.g., a flashlight or other illuminations) to provide sufficient light conditions for sensing (e.g., capturing an image with enough brightness). In some examples, an image sensor can also perform a point scan or a line scan to obtain better performance such as an improved detection limit and a larger dynamic range. Such an image sensor may have a high image resolution and complex imaging structures and may thus beexpensive. However, as described below, integrating such an image sensor with, for example, a LiDAR sensor in the multimodal detection system 400 can reduce the overall cost as compared to two discrete sensors.

[0096] FIG. 4 further illustrates that system 400 includes an optical receiver and light detector 430 to receive and detect light from FOV 470. As described above, the transmission side of system 400 may transmit light to FOV 470. A portion of the light transmitted may be reflected or scattered by objects in the FOV 470 to form return light signals. The return light signals may be received by optical receiver and light detector 430. In addition, optical receiver and light detector 430 may also receive light signals from other external light sources, including, for example, sunlight, ambient light, streetlight, and / or other sources of illuminations such as light from other LiDAR or LiDAR systems. The various light signals received by optical receiver and light detector 430 are collectively referred to as the received light signals or collected light signals. The received or collected light signals may include both return light signals formed based on transmitted light of system 400 and other light signals from other light sources. The received light signals may have a narrow or wide spectral range comprising, for example, one or more of visible light, NIR light, SWIR light, MWIR light, and / or LWTR light, etc. One or more of these received light signals can be detected by different types of light detectors (e g., a LiDAR sensor for detecting JR light signals, and an image sensor for detecting visible light signals). In the present disclosure, one or more of these light detectors can be integrated to form a hybrid detector. The light collection distribution device 410, signal separation device 440, and multimodal sensor 450 of optical receivers and light detector 430 are described in greater detail below.

[0097] FIG. 6 illustrates an example light collection and distribution device 410. Light collection and distribution device 410 can be configured to perform at least one of collecting light signals from a field-of-view (FOV) and distributing the light signals to a plurality of sensors of a multimodal sensor (e.g., sensor 450). The light signals collected and distributed by device 410 may have a plurality of wavelengths. At least one wavelength is different from one or more other wavelengths. As illustrated in FIG. 6, device 410 can include light collection optics 602, refraction optics 610, diffractive optics 620, reflection optics 630, and / or optical fibers 640. While FIG. 4 illustrates that steering mechanism 406 is a separate device from light collection and distribution device 410, in some embodiments, steering mechanism 406 may be integratedwith, or a part of, device 410. For example, steering mechanism 406 may be shared between the transmitter 404 and the optical receiver and light detector 430 for both transmitting light signals to the FOV and for receiving / redirecting light signals from the FOV. This type of configuration is also referred to as a coaxial configuration because the transmitting light path and the receiving light path share some common optical components. Thus, while FIG. 6 does not explicitly illustrate, light collection optics 602 may include a steering mechanism that is shared between the transmitter and receiver.

[0098] With reference to FIG. 6, light signals from the FOV can be received or collected by light collection optics 602 (e.g., by a steering mechanism 406). Light collection optics 602 include optics that are configured to collect and focus received light signals. Light collection optics 602 can be optimized to maximize the number of light signals collected from the FOV and direct the light signals toward a specific target, such as one of the refraction optics, diffractive optics, reflection optics, a detector, a sensor, and / or an imaging system. Light collection optics 602 may include one or more types of light collection optics, including one or more lenses, one or more lens groups, one or more mirrors, and one or more optical fibers. For instance, a collection lens or a lens group can be used to collect light signals from a distant object in the FOV and focus the light signals onto another optical components or a detector. Mirrors are another optical component that can be used in light collection optics 602. They can be used to reflect and redirect light toward a specific target. Mirrors can be used alone or in combination with lenses to form complex optical structures for collecting light signals.

[0099] In some embodiments as shown in FIG. 6, light collection optics 602 directs the collected light signals to one or more of refraction optics 610, diffractive optics 620, reflection optics 630, and / or optical fibers 640. In some embodiments, light collection optics 602 may be optional or integrated with refraction optics 610, diffractive optics 620, reflection optics 630, and / or optical fibers 640. For instance, the collected light signals can be directed (with or without light collection optics 602) to refraction optics 610. Refraction optics 610 can include optics that bend the light signals as they pass from one medium (e.g., air) to another medium (e.g., glass) with a different refractive index. A refractive index is a measure of how much a medium can bend light signals. When light signals pass from a medium with a high refractive index to a medium with a lower refractive index, the light signals bend away from the normal direction (e.g., the direction perpendicular to the surface at the point where the light enters the second medium). When thelight signals pass from a medium with a low refractive index to a medium with a higher refractive index, the light bends toward the normal direction. The amount of bending depends on the angle of incidence (the angle between the incoming light signals and the normal direction of a surface of the medium) and the refractive indices of the two media. The relationship between these variables is described by the Snell’s law, which states that the ratio of the sine of the angle of incidence to the sine of the angle of refraction is equal to the ratio of the refractive indices of the two media.

[0100] In some embodiments, refraction optics 610 can be implemented using a beam splitter, which can be configured to perform optical refraction such that it transmits a first portion of the incident light signals from the FOV (e.g., received directly or via light collection optics) to a first sensor and reflects a second portion of the received light signals to a second sensor. The first sensor and second sensor can be different sensors located at two different positions.

[0101] With continued reference to FIG. 6, light collection and distribution device 410 may also include diffractive optics 620 configured to separate the incident light signals to portions having different wavelengths, intensities, or polarizations. Diffractive optics 620 may include optics having diffractive structures such as a diffractive gratings. Diffractive structures can be made of thin layers of materials that contain features, such as grooves, ridges, or other microstructures, that are configured to manipulate the phase of the incident light signals. These diffractive structures can be used to manipulate the properties of light signals, such as the direction, intensity, polarization, and wavelength. In some examples, diffractive optics 620 can include diffractive gratings, which is a periodic structure that separates light into its spectral components based on its wavelength. In some examples, diffractive optics 620 may also include diffractive lenses, beam splitters, and polarizers. Diffractive lenses can be configured to correct for chromatic aberration and other types of optical distortion, and can be used to provide lightweight and compact optical systems. Diffractive optics 620 can be used to create complex optical elements with a high degree of precision. As a result, they can be used in the multimodal detection system to precisely separate and direct light signals having different properties (e.g., wavelengths, intensities, polarizations, etc.) to different sensors.

[0102] FIG. 6 also illustrates that light collection and distribution device 410 may include reflective optics 630. Reflection optics 630 comprises one or more optical components that canreflect the incident light signals. The angle of incidence determines the angle of reflection. The properties of the surface of reflection optics, such as the roughness, shape, and material, can affect the reflection of the incident light signals. In one example, reflection optics 630 comprises a Schmidt-Cassegrain based reflection device configured to direct a portion of the incident light signals to a first sensor and direct another portion of the incident light signals to a second sensor. In some examples, reflection optics 630 includes a Newtonian-based reflection device configured to direct a portion of the incident light signals to a first sensor and direct another portion of the incident light signals to a second sensor. The first sensor and second sensor can be different sensors located at different physical positions. They can also be different types of sensors (e.g., a LiDAR sensor and an image sensor).

[0103] In another embodiments, the incident light signals collected by light collection optics 602 can be directed to different sensors by using optical fibers 640. Optical fibers 640 can be flexible and have any desired lengths. Therefore, using optical fibers 640, the incident light signals can be directed to different sensors located at different physical positions.

[0104] As described above and shown in FIG. 4, multimodal detection system 400 may include a signal separation device 440. FIG. 7 illustrates an example of such a signal separation device 440. Signal separation device 440 is configured to separate the incident light signals to form separated light signals having a plurality of different light characteristics. The signal separation device 440 can perform a variety of separations including spatial separation, intensity separation, spectrum separation, polarization separation, etc. While FIG. 4 illustrates that signal separation device 440 and light collection and distribution device 410 are two different devices, in some embodiments, signal separation device 440 may be at least partially combined with light collection and distribution device 410. For instance, as described above, light collection and distribution device 410 can include one or more of refraction optics, diffractive optics, reflection optics, etc., to perform spatial distribution of the incident light signals. Thus, these optical components may form a part of the signal separation device 440 (e.g., as spatial separation device) to separate incident light signals to different portions and direct the different portions to different detectors at different physical locations.

[0105] With reference to FIG. 7, signal separation device 440 may include a spatial separation device 706, a spectrum separation device 704, a polarization separation device 708, and / or otherseparation devices (not shown). Spatial separation device 706 is configured to separate light signals to form separated light signals corresponding to at least one of different spatial positions of the plurality of sensors or different angular directions of the light signals. Thus, the light signals from spatial separation device 706 can have different physical locations and / or different angular directions. The spectrum separation device 704 is configured to separate the light signals to form separated light signals having different wavelengths (e.g., NIR light, visible light, SWTR light, etc.). The polarization separation device 708 is configured to separate the light signals to form the separated light signals having different polarizations (e.g., horizontal or vertical).

[0106] The devices included in signal separation device 440 can be configured and structured in any desired manner. In one embodiment, spatial separation device 706 may be disposed upstream to receive the incident light signals 702 and to direct the spatially separated light signals to spectrum separation device 704 and / or polarization separation device 708. In another embodiment, spectrum separation device 704 may be disposed upstream to receive the incident light signals 702 and to direct the spectrally separated light signals to spatial separation device 706 and / or polarization separation device 708. Similarly, polarization separation device 706 can be disposed upstream. In other words, signal separation device 440 can be configured such that the spectrum separation, spatial separation, polarization separation, and / or any other separations can be performed in any desired order. In other embodiments, two or more types of separations can be performed together. For example, as described above, a prism or a beam splitter may separate light signals both spectrally and spatially. Each of the devices 704, 706, and 708 is described in greater detail below.

[0107] One example of a spatial separation device 706 is a fiber bundle. The incident light signals 702 are coupled to the optical fiber bundle, which may include many optical fibers bundled together such that they are physically located close to each other at one end of the fiber bundle. Different optical fibers of the fiber bundle can then be routed to different sensors located at different physical locations. Another example of a spatial separation device 706 shown in FIG. 7 comprises a micro lens array configured to separate the incident light signals to form the separated light signals and direct the separated light signals to respective sensors. The micro lens array is an optical component comprising an array of small lenses. These small lenses typically have diameters ranging from tens to hundreds of micrometers. Each lens in the micro lens arrayfocuses light signals onto a specific point or a sensor, and the overall effect of the array is to shape or manipulate the light signals in a particular way. A micro lens array can be used to enhance the resolution and sensitivity of imaging systems by focusing light signals onto a detector array or improving light collection efficiency. A micro lens array can also be used to shape light into specific patterns or distributions for use in applications such as image sensing or depth sensing. A micro lens array can also be used to couple light between optical fibers or to improve the coupling efficiency between optical components. A micro lens array can be made from materials such as glass, silicon, or plastic, and can be customized in terms of lens size, shape, and spacing to achieve the desired optical performance.

[0108] With continued reference to FIG. 7, signal separation device 440 may also include a spectrum separation device 704, which is configured to separate light signals to form the separated light signals having different wavelengths or colors. Spectrum separation device 704 comprises one or more of a Dichroic mirror, a dual-band mirror, a dual-wavelength mirror, a Dichroic reflector, a red-green-blue (RGB) filter, an infrared light filter, a colored glass filter, an interference filter, a diffractive optics, a prism, diffraction gratings, blazed gratings, holographic gratings, and a Czerny-Turner monochromator. For example, a prism can refract light signals at different angles depending on the wavelength of the light signals. Using the visible light as an example, when a beam of incident light signals is passed through a prism, the light signals may be separated to different colors for different channels including a red channel, a green channel, and a blue channel. As another example, diffraction gratings can also be used for spectrum separation. They include a series of closely spaced parallel lines or slits that diffract light at different angles depending on the wavelength of the light. Using diffraction gratings, incident light signals can similarly be separated into a red channel, a green channel, and a blue channel. The separated light signals have different wavelengths, which may carry different information that can be more easily processed by a computer vision algorithm.

[0109] FIG. 7 also illustrates that signal separation device 440 can include a polarization separation device 708, which is configured to separate light signals to form the separated light signals having different polarizations. In one embodiment, the polarization separation device 708 comprises one or more of absorptive polarizers including crystal-based polarizers, beamsplitting polarizers, Fresnel reflection based polarizers, Birefringent polarizers, thin film based polarizers, wire-grid polarizers, and circular polarizers. For instance, polarization separation canbe achieved using polarizing filters, which are optical filters that only transmit light waves with a specific polarization orientation. Polarizing filters can be made from materials such as polarizing films, wire grids, or birefringent crystals. When unpolarized light is passed through a polarizing filter, only the component of the light with the same polarization orientation as the filter is transmitted, while the other polarization component is blocked. This results in polarized light with a specific polarization orientation. For instance, when light signals pass through the polarization separation device 708, the light signals can be separated to light signals having a horizontal polarization, light signals having a vertical polarization, and light signals having all polarizations. Image data formed by light signals having different polarizations can include different information such as different contrast, brightness, color, etc.

[0110] Using one or more of the above types of separation devices and other types of separation / processing devices (e.g., an image sensor such as a CCD array), signal separation device 440 can process the incident light signals to differentiate light intensities and / or reflectivity. Light signals reflected or received at different angles by an optical receiver may have different light intensities. The different light intensities may be sensed and represented by signal separation device 440 by, for example, different brightness / colors of the image captured.[0U1] With reference back to FIG. 4, as described above, in some embodiments, light collection and distribution device 410 and signal separation device 440 may be two separate devices. For example, device 410 is configured to collect light signals from the FOV 470 and spatially distribute the received light signals, while device 440 is configured to spectrally separate the received light signals. In some embodiments, light collection and distribution device 410 and signal separation device 440 may be combined together, at least partially, to perform one of more of spatial separation, spectrum separation, polarization separation, etc. In another embodiment, light collection and distribution device 410 can directly distribute the light signals to multimodal sensor 450 without using a signal separation device 440.

[0112] With continued reference to FIG. 4, when the received light signals are processed by light collection and distribution device 410 and optionally signal separation device 440, they are passed onto multimodal sensor 450. In some embodiments, multimodal sensor 450 includes a plurality of sensors that are positioned corresponding to the respective light emitters to improve the light collection effectiveness. For example, each sensor of the plurality of sensors may beangularly positioned differently corresponding to the different angular positions of a plurality of transmitter channels directing a plurality of transmission light beams to different directions. As such, the receiving aperture for receiving return light signals formed by different transmission light beams can be maximized. Each sensor of multimodal sensor 450 may include one or more detectors or detector elements. The plurality of sensors may have different types. For instance, the plurality of sensors may comprise at least a light sensor of a first type and a light sensor of a second type. The light sensor of the first type can be configured to detect light signals having a first light characteristic, where the light sensor of the second type is configured to detect light signals having a second light characteristic. The first light characteristic can be different from the second light characteristic. For instance, the light sensor of the first type can include a sensor configured to detect light signals having an NIR wavelength for a LiDAR system. The light sensor of the second type can include a sensor configured to detect light signals having the visible light wavelength for a camera. As described above, the NIR wavelength signals can be used by the LiDAR sensor to generate point cloud data for distance measurements; while the visible light can be used by an image sensor to generate image data for visual computing.

[0113] In some embodiments, the plurality of sensors of multimodal sensor 450 can be combined or integrated together. FIG. 8 illustrates example configurations of integrated detectors of a multimodal sensor 450, according to various embodiments of the present disclosure. As shown in FIG. 8, two or more sensors of a multimodal sensor can be integrated in a single device package, detector assembly, a semiconductor chip, or a single printed circuit board (PCB). For instance, a semiconductor chip 800 may include many dies sharing a semiconductor substrate. The dies can be located in the same wafer. At least a part of semiconductor chip 800 may be used as sensors for a multimodal sensor 450. In the embodiment shown in FIG. 8, chip 800 may include four sensors 802, 804, 806, and 808. Sensors 802 and 804 may be disposed in a respective die of chip 800 (one die in chip 800 is illustrated as a small square). Sensors 806 and 808 may be disposed in multiple dies. For example, sensor 806 may include 4 detectors that are disposed across 4 dies horizontally, while sensor 808 may include 4 detectors that are disposed across 4 dies both horizontally and vertically forming a 2x2 array. It is understood that a sensor can be disposed in any desired manner across any number dies. The chip 800 may also include other sensors or circuits. For instance, readout circuits for processing the sensor generatedsignals can be integrated in chip 800, thereby improving the degree of integration of multimodal sensor 450 and reducing cost.

[0114] Sensors that can be integrated in chip 800 may include photodiode-based detectors, avalanche photodiodes (APDs) based detectors, charge-coupled devices (CCDs) based detectors, etc. For example, photodiodes based detectors may be made from Silicon or Germanium materials; APD-based detectors may be made from Silicon, Germanium, Indium Gallium Arsenide (InGaAs), Mercury Cadmium Telluride (MCT); and CCD-based detectors can be made from Silicon, Gallium Arsenide (GaAs), Indium Phosphide (InP), and MCT. In some examples, APDs can be used for sensing infrared light for a LiDAR device, and CCD can be used for sensing visible light for a camera. Therefore, multiple sensors can be integrated together on chip 800 by using semiconductor chip fabrication techniques. It is understood that a sensor included in multimodal sensor 450 can also use other suitable semiconductor materials such as Silicon Germanium (SiGe).

[0115] With continued reference to FIG. 8, in some embodiments, chip 800 may also integrate a photonic crystal structure, which is a type of artificial periodic structure that can manipulate the flow of light in a similar way to how crystals manipulate the flow of electrons in solid-state materials. Photonic crystals are made by creating a pattern of periodic variations in the refractive index of a material. This pattern creates a photonic band gap, which is a range of frequencies of light that cannot propagate through the material. The photonic band gap arises from the interference of waves reflected by the periodic structure, leading to destructive interference at certain frequencies and constructive interference at others. The result is a range of frequencies where light cannot propagate, similar to how electronic band gaps prevent the flow of electrons in semiconductors. Photonic crystals can be made from a variety of materials, including semiconductors, metals, and polymers. A photonic crystal structure can be used to implement optical filters, detectors, waveguides, and laser emitters. For instance, the photonic band gap can be used to create optical filters; and the sensitivity of photonic crystals to changes in refractive index can be used to create highly sensitive sensors. Therefore, by using photonic crystal structure, chip 800 can integrate not only sensors or detectors, but also other optical components such as filters, waveguides, and light sources, thereby further improving the degree of integration. Various dies or modules disposed in chip 800 can thus implement differentfunctions. Chip 800 can be bonded to other components (e.g., a readout circuitry, a PCB) using wire bonding, flip-chip bonding, BGA bonding, or any other suitable packaging techniques.

[0116] As described above, a multimodal sensor 450 (shown in FIGs. 4 and 8) may include multiple sensors. A sensor includes one or more detectors, one or more other optical elements (e.g., lens, fdter, etc.) and / or electrical elements (e.g., ADC, DAC, processors, etc.). In the example shown in FIG. 8, multiple sensors can be integrated or disposed together to form a multimodal sensor 450. Multimodal sensor 450 may be included in a detector assembly, a device package, a device module, or a PCB. The multiple sensors are mounted to the same assembly, device package, device module, or PCB. In other embodiments, multimodal sensor 450 may include two or more assemblies, device packages, modules, or PCBs. Each of the multiple sensors may be mounted to a different assembly, device package, device module, or PCB. The different assemblies, device packages, modules, or PCBs may be disposed close to each other or in a housing to form an integrated multimodal sensor package.

[0117] In the example shown in FIG. 8, the multiple sensors included in multimodal sensor 450 comprise an imaging sensor 812, an illuminance sensor 814, a LiDAR sensor 816, and one or more other sensors 818. An imaging sensor 812 can include a detector that detects light signals and converts the light signals to electrical signals to form images. Therefore, imaging sensor 812 can be used as a part of cameras. The imaging sensor 812 can be a CCD sensor, a CMOS sensor, an active-pixel sensor, a thermal -imaging sensor, etc. An illuminance sensor 814 can include a detector that facilitates measuring the amount of light falling on a surface per unit area, referred to as illuminance. Illuminance can be represented for example, by the amount of lumen per square meter. Illuminance sensor 814 can include detectors comprising photodiodes, phototransistors, photovoltaic cells, photoresistors, etc. Illuminance sensor 814 can be used for lighting control, brightness control, environmental monitoring, etc.

[0118] LiDAR sensor 816 can include detectors that detect laser light (e.g., in the infrared wavelength range). The detected laser light can be used to determine the distance of an object from the LiDAR sensor. LiDAR sensor 816 can be used to generate a 3D point cloud of the surrounding area. The detectors used for a LiDAR sensor can be an avalanche photodiode, Mercury-Cadmium-Telluride (HgCdTe) based infrared detectors, Indium Antimonide (InSb) based detectors, etc. LiDAR sensor 816 can be implemented using one or more components ofLiDAR system 300 described above. FIG. 8 also illustrates that multimodal sensor 450 may include one or more other sensors 818. These other sensors 818 can facilitate temperature sensing, chemical sensing, pressure sensing, motion sensing, light sensing, proximity sensing, etc. One or more sensors 818 can include detectors such as light emitting diodes (LEDs), photoresistors, photodiodes, phototransistors, pinned photodiodes, quantum dot photoconductors / photodiodes, silicon drift detectors, photovoltaic based detectors, avalanche photodiode (APD), thermal based detectors, Bolometers, microbolometers, cryogenic detectors, pyroelectric detectors, thermopiles, Golay cells, photoreceptor cells, chemical-based detectors, polarization-sensitive photodetectors, and graphene / silicon photodetectors, etc.

[0119] With reference to FIGs. 4 and 8, The plurality of sensors of multimodal sensor 450 can include multiple types of sensors integrated or mounted together to share, for example, a semiconductor wafer, a module, a printed circuit board, and / or a semiconductor package. The sensors may also share one or more components the transmission light path (e.g., light source 402, transmitter 404, and / or steering mechanism 406) and / or in the receiving light path (e g., light collection and distribution device 410, signal separation device 440). As a result, the multimodal sensor 450 can have a compact dimension, thereby enabling the multimodal detection system to be also compact. A compact multimodal detection system can be disposed in, or mounted to, any location within a moveable platform such as a motor vehicle. For instance, comparing to mounting multiple discreate sensors like one or more cameras, one or more LiDARs, one or more thermal imaging devices, one or more ultrasonic devices, etc., mounting a compact multimodal detection system can significantly reduce the complexity of integration of the multiple sensing capabilities into a vehicle, and / or also reduce the cost. As illustrated in FIGs. 4 and 8, a multimodal detection system (e.g., system 400) that includes a multimodal sensor (e.g., sensor 450) is also sometimes referred to as a hybrid detection and ranging system (LiDAR).

[0120] With continued reference to FIGs. 4 and 8, in some embodiments, the plurality of detectors or sensors of a multimodal sensor 450 can be configured to detect light signals received from the same FOV. For instance, FIG. 4 illustrates that light signals received from the same FOV 470 may include two or more of NIR light, visible light, SWIR light, MWIR light, LWIR light, and other light. These light signals are mixed together but can be detected by the same multimodal sensor 450. For instance, as described above, the mixed light signals can becollected and distributed by device 410, and then separated according to one or more of the light characteristics (e.g., wavelength, polarization, angle of incidence, etc.) by signal separation device 440. The separated light signals can then be detected by a corresponding light sensor included in multimodal sensor 450. In this manner, multimodal detection system 400 provides integrated multimodal sensing capabilities, reducing or eliminating the need for multiple discreate or separate sensors like cameras, LiDARs, thermal imaging devices, etc. This will make the sensing device more integrated and compact, reducing the cost, and improving the sensing efficiency. As one example, when discreate sensors are separately mounted to a vehicle (or another moveable platform), data captured by different sensors (e.g., a LiDAR sensor and an image sensor like a camera) often need to be time synchronized and / or converted to use the same coordinate system. This data fusion process can be cumbersome, error prone, inefficient, and power consuming. By integrating multiple sensors together in a multimodal sensor disclosed herein, at least some of the above problems can be solved. For instance, if a LiDAR sensor and an image sensor are integrated together (e.g., disposed in one device package, PCB, and sharing at least a part of the transmitting / receiving light paths), data from the two sensors can be fused together directly without having to perform time synchronization or coordinates conversion first, or with minimum fusion effort.

[0121] As described above, multimodal sensor 450 can include an integrated sensor array comprising multiple sensors having different types. FIG. 9 illustrates example packaging configurations for integrated sensors, according to various embodiments of the present disclosure. As shown in FIG. 9, a multimodal sensor device 904 may include a plurality of sensors 906, each of which is disposed on a heatsink 912. The sensors 906 may be of the same type of different types. Each of the sensors 906 can be wired bonded to an integrated circuit chip 908. The IC chip 908 can be used to process electrical signals generated by the sensors 906, thereby implementing a readout circuitry. The IC chip 908 can further include other signal processing circuits such as rendering images, performing digital signal processing functions, etc. In this configuration, the sensor array is integrated with the readout circuity in the same device package (e.g., both IC chip 908 and sensor array 906 are disposed on the same PCB 914). In other embodiments, the sensor array and the readout circuitry may be individually packaged in separate modules. The two separate modules can then be mounted to a PCB board so that signals can be passed between the two modules.

[0122] FIG. 9 also illustrates another packaging configuration where the readout circuits 920 are disposed in one semiconductor chip and the integrated sensor array 924 are disposed in another semiconductor chip 926. The two chips 920 and 926 are bonded together via flip-chip technologies so that electrical signals can be delivered from the sensor array 924 to the readout circuits 920 via solder bumps 922. Once bonded, the two chips 920 and 926 can be packaged together as a single device 930. It is understood that other packaging techniques can also be used, for example, through-hole packaging, surface-mounting packaging, ball grid array packaging, chip-scale packaging, etc.

[0123] As described above, some LiDAR or 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. The following description uses LiDAR system 1000 as an example. It is understood that the LiDAR device or sensor in a LiDAR system may operate similarly. For example, with reference to FIG. 10A, an example LiDAR system 1000 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 1000 can be implemented using, for example, LiDAR system 300 described above. LiDAR system 1000 transmits a light pulse 1002 along light path 1004 as determined by the steering mechanism of LiDAR system 1000. In the depicted example, light pulse 1002, which is generated by the laser light source, is a short pulse of laser light. Further, the signal steering mechanism of the LiDAR system 1000 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.

[0124] Referring back to FIG. 10A (e.g., illustrating a time-of-flight LiDAR system that uses light pulses), when light pulse 1002 reaches object 1006, light pulse 1002 scatters or reflects to form a return light pulse 1008. Return light pulse 1008 may return to system 1000 along light path 1010. The time from when transmitted light pulse 1002 leaves LiDAR system 1000 to when return light pulse 1008 arrives back at LiDAR system 1000 can be measured (e.g., by aprocessor 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 1000 to the portion of object 1006 where light pulse 1002 scattered or reflected.

[0125] By directing many light pulses, as depicted in FIG. 10B, LiDAR system 1000 scans the external environment (e.g., by directing light pulses 1002, 1022, 1026, 1030 along light paths 1004, 1024, 1028, 1032, respectively). As depicted in FIG. 10C, LiDAR system 1000 receives return light pulses 1008, 1042, 1048 (which correspond to transmitted light pulses 1002, 1022, 1030, respectively). Return light pulses 1008, 1042, and 1048 are formed by scattering or reflecting the transmitted light pulses by one of objects 1006 and 1014. Return light pulses 1008, 1042, and 1048 may return to LiDAR system 1000 along light paths 1010, 1044, and 1046, respectively. Based on the direction of the transmitted light pulses (as determined by LiDAR system 1000) as well as the calculated range from LiDAR system 1000 to the portion of objects that scatter or reflect the light pulses (e.g., the portions of objects 1006 and 1014), the external environment within the detectable range (e.g., the field of view between path 1004 and 1032, inclusively) can be precisely mapped or plotted (e.g., by generating a 3D point cloud or images).

[0126] If a corresponding light pulse is not received for a particular transmitted light pulse, then LiDAR system 1000 may determine that there are no objects within a detectable range of LiDAR system 1000 (e.g., an object is beyond the maximum scanning distance of LiDAR system 1000). For example, in FIG. 10B, light pulse 1026 may not have a corresponding return light pulse (as illustrated in FIG. 10C) because light pulse 1026 may not produce a scattering event along its transmission path 1028 within the predetermined detection range. LiDAR system 1000, or an external system in communication with LiDAR system 1000 (e.g., a cloud system or service), can interpret the lack of return light pulse as no object being disposed along light path 1028 within the detectable range of LiDAR system 1000.

[0127] In FIG. 10B, light pulses 1002, 1022, 1026, and 1030 can be transmitted in any order, serially, in parallel, or based on other timings with respect to each other. Additionally, while FIG. 10B depicts transmitted light pulses as being directed in one dimension or one plane (e.g., the plane of the paper), LiDAR system 1000 can also direct transmitted light pulses along other dimension(s) or plane(s). For example, LiDAR system 1000 can also direct transmitted lightpulses in a dimension or plane that is perpendicular to the dimension or plane shown in FIG. 10B, thereby forming a 2-dimensional transmission of the light pulses. This 2-dimensional transmission of the light pulses can be point-by-point, line-by-line, all at once, or in some other manner. That is, LiDAR system 1000 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 ( / / + ! ) 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.

[0128] The density of a point cloud refers to the number of measurements (data points) per area performed by the LiDAR system. A point cloud density relates to the LiDAR scanning resolution. Typically, a larger point cloud density, and therefore a higher resolution, is desired at least for the region of interest (RO I). 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.

[0129] 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 toreturn 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.

[0130] 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.

[0131] 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.

[0132] 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. 1-17, 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 beused, 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.

[0133] 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. 11. Apparatus 1100 comprises a processor 1110 operatively coupled to a persistent storage device 1120 and a main memory device 1130. Processor 1110 controls the overall operation of apparatus 1100 by executing computer program instructions that define such operations. The computer program instructions may be stored in persistent storage device 1120, or other computer-readable medium, and loaded into main memory device 1130 when execution of the computer program instructions is desired. For example, processor 1110 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. 1-23 can be defined by the computer program instructions stored in main memory device 1130 and / or persistent storage device 1120 and controlled by processor 1110 executing the computer program instructions. For example, the computer program instructions can be implemented as computer executable code programmed by one skilled in the art to perform an algorithm defined by the method steps discussed herein in connection with at least some of FIGS. 1-17. Accordingly, by executing the computer program instructions, the processor 1110 executes an algorithm defined by the method steps of these aforementioned figures. Apparatus 1100 also includes one or more network interfaces 1180 for communicating with other devices via a network. Apparatus 1100 may also include one or more input / output devices 1190 that enable user interaction with apparatus 1100 (e.g., display, keyboard, mouse, speakers, buttons, etc.).

[0134] Processor 1110 may include both general and special purpose microprocessors and may be the sole processor or one of multiple processors of apparatus 1100. Processor 1110 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 11 10, persistent storage device 1120, and / or main memory device 1130 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).

[0135] Persistent storage device 1120 and main memory device 1130 each comprise a tangible non-transitory computer readable storage medium. Persistent storage device 1120, and main memory device 1130, 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.

[0136] Input / output devices 1190 may include peripherals, such as a printer, scanner, display screen, etc. For example, input / output devices 1190 may include a display device such as a cathode ray tube (CRT), plasma or liquid crystal display (LCD) monitor for displaying information to a user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to apparatus 1100.

[0137] Any or all of the functions of the systems and apparatuses discussed herein may be performed by processor 1110, and / or incorporated in, an apparatus or a system such as LiDAR system 300. Further, LiDAR system 300 and / or apparatus 1100 may utilize one or more neural networks or other deep-learning techniques performed by processor 1110 or other systems or apparatuses discussed herein.

[0138] 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. 11 is a high-level representation of some of the components of such a computer for illustrative purposes.

[0139] FIG. 12 is a block diagram illustrating a moveable platform 1200 mounted with a LiDAR system 1202 and various sensors, according to various embodiments. The moveable platform 1200 can be a vehicle, a robot, etc.. LiDAR system 1202 can be substantially the same or similar to systems 300 or 400 described above. Sensors 1204, 1206, and 1208 may include, for example, cameras, ultrasonic sensors, radars, inertial measurement units (IMUs) etc. or a combination thereof. There may be other components mounted to the moveable platform 1200 and are not shown. When LiDAR system 1202 is manufactured and mounted to moveable platform 1200, its position and orientation is calibrated such that it can correctly operate to detect the objects in the desired FOV around the moveable platform 1200. For example, the LiDAR system 1202’s roll, pitch, yaw are set and calibrated to the desired values for the system to operate in a desired manner. This type of calibration is referred to as the extrinsic calibration of LiDAR system 1202, because the calibration is with respect to the moveable platform 1200 (or its components, other sensors mounted thereto, etc.) located external to LiDAR system 1202.

[0140] After moveable platform 1200 operates for some time, LiDAR system 1202 may change its position and orientation relative to the vehicle due to, for example, vibration, shock, humidity, temperature, or other environmental, operational, or user related factors. As shown in FIG. 12, LiDAR system 1202 may change its position and orientation (e.g., roll, pitch, and yaw) with respect to its original position (and therefore also with respect to the moveable platform 1200). In some examples, over time, one or more components in the LiDAR system 1202 may change their positions and orientations with respect to moveable platform 1200, sensors 1204, 1206, and / or 1208; and / or other components mounted to the moveable platform 1200. Accordingly, there is a need to detect the extrinsic calibration degradation and cause adjustments to compensate for the degradation. If the extrinsic degradation is within a predetermined threshold, the LiDAR system can adjust itself during operation. Thus, it minimizes the impact on the vehicle’s operation (e.g., the vehicle can keep operating with the LiDAR system continuously providing detection results). Dynamic calibration methods in some embodiments herein are carried out in a typical “use-environment” of the movable platform (e.g., a vehicle) such as, for example, while driving on a road in an urban, rural, or suburban environment.

[0141] FIGs. 13 and 14 are flowcharts illustrating methods for detecting extrinsic calibration degradation of a LiDAR system mounted to a moveable platform, according to various embodiments. FIGs. 15A-15E are diagrams illustrating an example method of detectingextrinsic calibration degradation of a LiDAR system using parallel line features extending along a road surface, according to various embodiments. The flowchart of FIG. 13 is described first with the examples shown in FIGs. 15A-15E.

[0142] Fig. 13 shows a method 1900. In some embodiments, a controller begins method 1900 by obtaining the point cloud data generated by the LiDAR sensor.

[0143] In block 1904, the controller segments a space-of-interest based on the point cloud data. Comparing image 1930 in FIG. 15A and image 1931 in FIG. 15B, the controller extracts the space-of-interest 1933 and removes (e.g., filters out) other features that are not of interest. In this example, the space-of-interest 1933 corresponds to the features of a road surface on which a vehicle mounted with the LiDAR system operates. Other features in image 1930 are removed or filtered out. Such features may include, for example, other vehicles operating on the road surface, the sky, trees, and other roadside objects. Extracting the features from the point cloud data to segment the space of interest can be performed using, for example, a machine-learning based algorithm and / or other pattern recognition algorithms. For example, the point cloud data may include distance and height information of the objects in image 1930. For instance, in image 1930, any objects that has a height less than a threshold (e.g., 0.1 m) can be considered the road surface. Based on these types of information contained in the point cloud data, the controller can accurately identify the road surface and segment the road surface (i.e., the space- of-interest in this example) from the other features (e.g., trees, vehicles, sky, etc.). In some examples, the controller may also use other data to determine the operating conditions before performing the segmentation to obtain a space of interest. For instance, based on GPS data and / or the point cloud data, the controller may determine that the vehicle is moving forward; that there is no apparent turning; that the vehicle speed is within a certain threshold; etc. When all these operating conditions are satisfied, the controller segments the space-of-interest 1933 (e.g., road surface) based on the point cloud data and, in some embodiments, also based on other available data, if any.

[0144] Next, with reference back to FIG. 13, in block 1906, the controller detects parallel line features in the space-of-interest 1933 based on the point cloud data. This is illustrated in FIG. 15D, where parallel line features 1932 are identified from the space-of-interest 1933. In one embodiment, the parallel line features 1932 can correspond to the lane lines on a road surface.The lane lines can be identified using point cloud data. For example, the lane lines may have higher reflectivity (e.g., because they include reflection materials) and are thus distinguishable from other features of the road surface. The parallel line features 1932 may also correspond to other objects like curbs, isolating islands, buildings, or any other objects disposed along the road surface. In one example, a Hough Line transformation can be performed to extract the parallel line features 1932. In one example, For more accurate extraction, freeway lane lines may be better because they are straight, and their lane curvature is more stable or slow changing.

[0145] With reference back to FIG. 13, in block 1908, the controller identifies an intersection position of the detected parallel line features 1932. This is illustrated in FIG. 15D. For example, the parallel line features 1932 can be extrapolated to find the intersection position 1934 at the horizon 1937. The controller can identify the intersection position 1934 by its coordinates (x, y). FIGs. 15F is a diagram illustrating another example where the lane features are curved (compared to straight lines in FIG. 15D). In this case, the controller may identify a set of intersection positions 1952A-1952C) based on the curved line features. The set of intersection positions 1952A-1952C can be identified by their coordinates (e.g., A(xl, yl); B(x2, y2); and C(x3, y3)). For example, based on the curvatures of lanes at different locations, the controller can identify a set of intersection points 1952A-1952C.

[0146] With reference back to FIG. 13, the process including blocks 1904, 1906, and 1908 can be repeated multiple times to obtain multiple intersection positions. This is illustrated in FIG. 15E, where multiple intersection positions 1934A-1934N, each corresponding to a respective horizon 1937A-1937N at different time points are calculated. In one example, each dataset corresponds to one frame of point cloud data. Each dataset can be processed by the controller to obtain an intersection position. Thus, multiple datasets 1935 can be used to obtain multiple intersection positions 1934A-1934N.

[0147] With reference back to FIG. 13, based on the multiple intersection positions, the controller can estimate if the relation between the LiDAR system (e.g., system 300, 400, or 1202) and the moveable platform (e.g., platform 1200) to which the LiDAR system is mounted has shifted from an original configuration. For example, the controller can compare the multiple intersection positions with one or more corresponding stored extrinsic positions of the LiDAR system. The stored extrinsic positions may be the calibration results obtained at the time theLiDAR system was mounted to the moveable platform at a factory and thus may be factory- calibrated positions (also referred to as ground truth). The stored extrinsic positions may also be calibration results obtained at any other previous time points (e.g., during a later vehicle maintenance service). In some examples, the positions are used to calculate current calibration parameters such as yaw, pitch, and or roll angles and those current calibration parameters are compared to previously-stored calibration parameters (e.g., from a prior calibration) to determine if the orientation of the system has changed.

[0148] In some examples the calculation of the intersection positions may be compared with calculations based on a high definition (HD) map. The calculation of the intersection positions may also be refined by using an inertial measurement unit (TMU) sensor to improve accuracy. For example, the HD map and / or the IMU sensor may provide additional information about the slope of the road surface, which may be used to improve the calculation accuracy.

[0149] In some embodiments, point cloud data used for dynamic calibration is enriched by carrying out the following processing with respect to a first frame of point cloud data captured at a first time and a second frame of point cloud data captured at a second time. The processing includes using data from an inertial measurement unit (IMU) (which can be used to determine change in position over time) (the IMU is mounted to the same movable platform to which the LiDAR system is mounted) and the second frame of point cloud data to compute a compensated frame of data by estimating point cloud data at the first time based on the second frame of point cloud data and information regarding movement between the first time and the second time (which can be obtained based on the EMU data). Then, merging the first frame of point cloud data with the compensated frame of data to obtain a merged frame of point cloud data. Then, using at least the merged frame of point cloud data to evaluate the calibration of the system. In one embodiment, the merged frame of point cloud data is obtained by including both the first frame of point cloud data and the compensated frame of data in the merged frame of point cloud data.

[0150] Comparison results of current and prior intersection locations and / or calibration parameters can be used to determine if the relation between the LiDAR system and the moveable platform (e.g., platform 1200) to which the LiDAR system is mounted has shifted from an original configuration. If the comparison results are within a predetermined threshold,the controller can determine that the LiDAR system has not shifted and the extrinsic calibration has not degraded (or not degraded beyond a threshold). If the comparison results are greater than a predetermined threshold, the controller can determine that the extrinsic calibration has degraded beyond the threshold. As such, the LiDAR system may need to be adjusted or recalibrated.

[0151] FIG. 14 illustrates a particular example method 1920 of detecting the extrinsic calibration degradation for a LiDAR system mounted to a vehicle. In block 1921, the controller of the LiDAR system may detect that the vehicle is traveling through a long straight road section with a speed more than a threshold speed (e.g., 10 m / s). In block 1922, the controller of the LiDAR system may obtain a frame of the LiDAR data, which includes a dataset. The dataset has point cloud data generated by a LiDAR sensor. In block 1923 the controller segments the point cloud data. As described above, the point cloud data may include 3D information like the horizontal and vertical coordinates and distance information. It may also have other information, such as return signal strength (which may correspond to reflectance information). The controller can segment a space-of-interest using the point cloud data. FIG. 14 further provides some examples of segmentations that can be performed by the controller of the LiDAR system. In one example, the controller may perform the segmentation to obtain a space-of-interest based on a set of criteria including (1) the vertical coordinates needs to be 1.5 m lower than the sensor; (2) the elevation angle is less than the azimuth angle * 0.5; (3) the elevation angle is less than the azimuth angle * (-0.5); and (4) the reflectance (which can correspond to return signal intensity) is greater than a threshold. In another example, the controller may perform the segmentation to obtain a space-of-interest based on a set of criteria including (1) the 3D coordinates need to be within 0.1 meter from a plane Ax+By+Cz+d=0; (2) the elevation angle and azimuth angle needs to satisfy condition K*elevation+M*azimuth+n>0; (3) the reflectance is greater than a threshold. (4) gradient of point cloud values (for intensity of the return light) in a horizontal direction is greater than a threshold. In the above equations, A, B, C, d, K, M, and n are configurable constants.

[0152] In block 1924, the controller identifies the line features that are parallel within the segmented space-of-interest. The line features may correspond to lane lines on a freeway, for example. In bock 1925, the controller computes the position of the intersection point (also referred to as the vanish point). In block 1926, the controller accumulates the positions of theintersection points, also referred to as the vanishing points, (e.g., by repeating blocks 1922-1925) to create a distribution. In block 1927, the controller determines if the distribution of the positions of the vanishing points are expected. This may be performed by comparing the vanish points’ positions distribution with a distribution obtained by factory calibration (ground truth). If the controller determines the distribution is as expected (e.g., it is within a threshold from the ground truth), it reports (block 1929) that the extrinsic parameters are consistent. That is, the extrinsic calibration did not degrade (or degraded slightly but still within a tolerance range). Otherwise, the controller reports (block 1928) that the extrinsic calibration has drifted from the expected values).

[0153] Fig. 16 shows a method 1600. Method 1600 will be described in reference to the example scene shown in FIG. 17. Method 1600 uses planar features, rather than parallel lines, to dynamically (e.g., while the vehicle is driving) detect whether extrinsic calibration has degraded.

[0154] As shown in FIG. 17, car 1701 driving on road 1703 has a LiDAR system 1702 mounted on it. In an area 1700 of interest, there might be one or more planar features of interest that can be identified using point cloud data generated by LiDAR system 1702. For example, the ground surface 1704 of road 1703 may be a planar feature of interest. Alternatively, or in addition, a road-facing surface 1707 of curb 1706 might be a planar feature of interest.

[0155] With reference to FIG. 16, in some embodiments, a controller begins method 1600 by obtaining the point cloud data generated by a LiDAR system.

[0156] In block 1604, the controller segments a space-of-interest based on the point cloud data. As previously described in the context of FIGs. 13 and 15A-C the controller extracts the space- of-interest and removes (e.g., filters out) other features that are not of interest. Extracting the features from the point cloud data to segment the space of interest can be performed using, for example, a machine-learning based algorithm and / or other pattern recognition algorithms. In method 1600 of FIG. 16, rather than detecting parallel line features in the space of interest, block 1606 detects planar features.

[0157] For example, the point cloud data in the dataset may include distance and height information of the objects in scene 1700 of FIG. 17. For instance, in scene 1700, point cloud points that have a height less than a threshold (e.g., 0.1 m) can be considered to correspond to the road surface. Based on this type of information contained in the point cloud data, the controllercan accurately identify the road surface and segment the road surface (i.e., the space-of-interest in this example) from the other features (e g., trees, vehicles, sky, etc.). In some examples, the controller may also use other data to determine the operating conditions before performing the segmentation to obtain a space of interest.

[0158] For instance, based on GPS data and / or the point cloud data, the controller may determine that the vehicle is moving forward; that there is no apparent turning; that the vehicle speed is within a certain threshold; etc. When all these operating conditions are satisfied, the controller segments the space-of-interest (e.g., road surface, curb, wall, etc.) based on the point cloud data and, in some embodiments, also based on other available data, if any.

[0159] Next, with reference to FIG. 16, in block 1606, the controller detects planar features in the space-of-interest based on the point cloud data. This might be done, for example, by identifying a sufficient number of points that are sufficiently close to being in a planar arrangement to suggest the presence of an extended flat surface.

[0160] At blockl608, the controller fits a plane to the point cloud points associated with the planar feature. In some embodiments, this can be done using a random sample consensus (RANSAC) algorithm, principal component analysis (PC A), or a combination of both methods.

[0161] At block 1610, the controller computes a line that is normal to the fitted plane. For example, in FIG. 17, line 1705 is normal to a fitted plane corresponding to road surface 1704. As another example, line 1708 is normal to a fitted plane corresponding to road-facing curb surface 1707. In one embodiment, the normal direction is identified using PCA and finding the direction with the lowest eigenvalue.

[0162] With reference to FIG. 16, at block 1612, the orientation of an identified normal direction to the fitted plane is compared with an expected orientation to determine a deviation in roll and or pitch angles of the LiDAR system (i.e., the roll or pitch angles used to calibrate the LiDAR system relative to the moveable platform). For example, a line normal to the road surface (such as line 1705 in FIG. 17) would be expected to be parallel to a line normal to a horizontal plane of the movable platform (or perpendicular to a direction of movement of the movable platform). If the normal line is leaning towards or away from the movable platform, then there is likely a deviation in the pitch angle of the LiDAR system. If the normal line is leaning to the left or right of the movable platform, then there is likely a deviation in the roll angle of the LiDAR system.

[0163] As another example, a line normal to a plane fitted to a road-facing curb surface or wall along a road (such as line 1708 in FIG. 17) would be expected to be perpendicular to a line normal to a horizontal plane of the movable platform. If the line is leaning towards or away from the movable platform in a horizontal direction, then there is likely a deviation in the pitch angle of the LiDAR system. If the line is leaning toward or away from the movable platform in the vertical direction, then there is likely a deviation in the roll angle of the LiDAR system.

[0164] In one embodiment, determining the deviation in pitch, roll, and / or yaw angles can be accomplished by comparing orientations of relevant normal lines computed during dynamic calibration to orientation of relevant normal lines stored in the system from a prior calibration.

[0165] In some embodiments, one or more of steps 1604-1612 can be repeated to obtain multiple deviation values for pitch and roll angles based on, for example, multiple frames of point cloud data before executing block 1612. At block 1612, the controller estimates whether the physical relationship between the LiDAR system and the movable platform to which it is mounted has shifted from an original configuration. This estimation may be done by comparing pitch and / or roll angle deviations to threshold values. In one example, a threshold value is a few degrees. In a particular example, a threshold value could be 5 degrees. In some embodiments, deviations in yaw angle can also be identified and considered including, for example, deviations in yaw angle that may be computed using, for example, analysis of parallel line convergence in the methods illustrated in FIGs 13 and 14. Also, in some examples, orientations relative to curb or guardrail planar features can be used to estimate yaw angle deviations.

[0166] In some examples the calculation of the angle deviations may be compared with calculations based on a high definition (HD) map. The calculation of the angle deviations may also be refined by using an IMU sensor to improve accuracy. For example, the HD map and / or the IMU sensor may provide additional information about the slope of the road surface, which may be used to improve the calculation accuracy.

[0167] The deviation results can be used to determine if the relation between the LiDAR system and the moveable platform (e.g., platform 1200) to which the LiDAR system is mounted has shifted from an original configuration. If the deviations are within a predetermined threshold, the controller can determine that the LiDAR system has not shifted and the extrinsic calibration has not degraded (or not degraded beyond a threshold). If the comparison results are greater thana predetermined threshold, the controller can determine that the extrinsic calibration has degraded beyond the threshold. As such, the LiDAR system may need to be adjusted or recalibrated, by, for example modifying pitch, roll, or yaw offset angles used as calibration parameters. If the necessary modification is small enough, it can be done simply by modifying the calibration parameters in software or configurable hardware. However, depending on the system, if the necessary modification exceed a threshold, a notification can require visit to a dealer or manufacturer service center as high deviations from a prior calibration might indicate level of displacement of the LiDAR system that requires professional examination. Such thresholds can be specified by a manufacturer based on characteristics of a specific system.

[0168] The above-described methods 1900 (FIG. 13) and 1920 (FIG. 14) for detecting extrinsic calibration degradation can be performed during vehicle operation using line features of a road surface and the above described method 1600 (FIG. 16) can be performed during vehicle operation using planar features of a road, other ground surface, or a standing structure (e.g., a building, a wall, a barrier). Such methods are referred to herein as dynamic extrinsic calibration or simply dynamic calibration.

[0169] 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.ADDITIONAL EMBODIMENTS

[0170] Embodiment 1. A system for light ranging and detection (LiDAR) mounted on a moveable platform, the system having a prior calibration comprising calibration parameters based on a prior orientation of the system relative to the movable platform, the system comprising: a transceiver transmitting laser light toward a field of view (FOV) and receiving return light from the FOV; and a controller configured to: generate point cloud data in responseto the transceiver receiving return light from the FOV, the point cloud data comprising a plurality of respective frames of point cloud data collected at a plurality of respective times; identify, using the point cloud data, one or more reference objects in a use-environment of the movable platform; and evaluate calibration of the system by determining, using point cloud data associated with the one or more reference objects, whether an orientation of the system relative to the movable platform has changed from the prior calibration such that recalibration is needed.

[0171] Embodiment 2. The system of embodiment 1 wherein: the one or more reference objects include substantially parallel lane lines; and the controller is configured to, for each of a plurality of frames of point cloud data: identify a horizon line; and use the horizon line and point cloud data associated with the lane lines to evaluate calibration of the system.

[0172] Embodiment 3. The system of embodiment 2 wherein the controller is configured to evaluate calibration of the system by determining, for a plurality of frames, current location values of intersection points of the lane lines with the horizon lines and comparing the current location values to other location values determined during at least one of: a previously-executed calibration process and a calibration process executed using data from a non-LiDAR sensor.

[0173] Embodiment 4. The system of embodiment 2 wherein the controller is configured to evaluate calibration of the system by: determining, for a plurality of frames, current location values of intersection points of the lane lines with the horizon lines; using the current location values to compute current calibration parameter values comprising pitch, yaw, and / or roll values; and comparing the current calibration parameters to other calibration parameter values determined during at least one of: a previously-executed calibration process and a calibration process executed using data from a non-LiDAR sensor.

[0174] Embodiment 5. The system of embodiment 3 wherein the other location values are determined during a calibration process that is executed using data from the non-LiDAR sensor, the non-LiDAR sensor comprising a camera.

[0175] Embodiment 6. The system of embodiment 3 wherein the other location values are determined during a previously-executed calibration process.

[0176] Embodiment 7. The system of embodiment 4 wherein the other calibration parameter values are determined during a calibration process that is executed using data from the non- LiDAR sensor, the non-LiDAR sensor comprising a camera.

[0177] Embodiment 8. The system of embodiment 4 wherein the other calibration parameter values are determined during a previously-executed calibration process.

[0178] Embodiment 9. The system of any of embodiments 1-8 wherein the controller is further configured to: with respect to a first frame of data captured at a first time and a second frame of data captured at a second time, use inertial measurement unit (IMU) data to modify the second frame of data to create a compensated frame of data by estimating point cloud data at the first time based on the second frame of data and system movement between the first time and the second time obtained from the IMU data; merge the first frame of point cloud data with the compensated frame of data to obtain a merged frame of point cloud data; and use at least the merged frame of point cloud data to evaluate the calibration of the system.

[0179] Embodiment 10. The system of embodiment 9 wherein the merged frame of point cloud data is obtained by including both the first frame of point cloud data and the compensated frame of data.

[0180] Embodiment 11. The system of any of embodiments 3-10 wherein the other location values are determined during a calibration process that is executed using data from the non- LiDAR sensor, the non-LiDAR sensor not being fully integrated with the LiDAR system.

[0181] Embodiment 12. The system of any of embodiments 3-11 wherein the other location values are determined during a calibration process that is executed using data from the non- LiDAR sensor, the non-LiDAR sensor being separately mounted to the moveable platform at a location on the moveable platform that is different from a location at which the LiDAR system is mounted.

[0182] Embodiment 13. The system of embodiment 1 wherein: the one or more reference objects include one or more planar features; and the controller is further configured to: fit a plane to points in the point cloud data corresponding to a planar feature of the one or more planar features; compute an orientation of a line normal to the plane fitted to the points in the pointcloud data; and use the orientation of the line normal to the planar feature relative to an expected orientation to evaluate the planar feature.

[0183] Embodiment 14. The system of embodiment 13 wherein the one or more planar features include a ground surface on which the movable platform is moving and the expected orientation is a stored value from a prior calibration of the system.

[0184] Embodiment 15. The system of embodiment 14 wherein the one or more planar features further include a vertical structure along a side of a road on which the movable platform is moving and the expected orientation a stored value from a prior calibration of the system.

[0185] Embodiment 16. The system of embodiment 15 wherein the one or more planar features include a curb.

[0186] Embodiment 17. The system of embodiment 15 wherein the one or more planar features include a wall.

[0187] Embodiment 18. A method for dynamic calibration of a light ranging and detection (LiDAR) system mounted on a moveable platform, the system associated with a prior calibration comprising calibration parameters based on a prior orientation of the system relative to the movable platform, the method comprising: transmitting laser light toward a field of view (FOV) and receiving return light from the FOV; generating point cloud data in response to the transceiver receiving return light from the FOV, the point cloud data comprising a plurality of respective frames of point cloud data collected at a plurality of respective times; identifying, using the point cloud data, one or more reference objects in a use-environment of the movable platform; and evaluating calibration of the system by determining, using point cloud data associated with the one or more reference objects, whether an orientation of the system relative to the movable platform has changed from the prior calibration such that recalibration is needed.

[0188] Embodiment 19. The method of embodiment 18 wherein the one or more reference objects include substantially parallel lane lines and the method comprises, for each of a plurality of frames of point cloud data: identifying a horizon line; and using the horizon line and point cloud data associated with the lane lines to evaluate calibration of the system.

[0189] Embodiment 20. The method of embodiment 19 wherein evaluating calibration of the system comprises, for a plurality of frames, determining current location values of intersectionpoints of the lane lines with the horizon lines and comparing the current location values to other location values determined during at least one of: a previously-executed calibration process and a calibration process executed using data from a non-LiDAR sensor.

[0190] Embodiment 21. The method of embodiment 19 wherein evaluating calibration of the system comprises, for a plurality of frames: determining current location values of intersection points of the lane lines with the horizon lines; using the current location values to compute current calibration parameter values comprising pitch, yaw, and / or roll values; and comparing the current calibration parameters to other calibration parameter values determined during at least one of: a previously-executed calibration process and a calibration process executed using data from a non-LiDAR sensor.

[0191] Embodiment 22. The method of embodiment 20 wherein the other location values are determined during a calibration process that is executed using data from the non-LiDAR sensor, the non-LiDAR sensor comprising a camera.

[0192] Embodiment 23. The method of embodiment 20 wherein the other location values are determined during a previously-executed calibration process.

[0193] Embodiment 24. The method of embodiment 21 wherein the other calibration parameter values are determined during a calibration process that is executed using data from the non- LiDAR sensor, the non-LiDAR sensor comprising a camera.

[0194] Embodiment 25. The method of embodiment 21 wherein the other calibration parameter values are determined during a previously-executed calibration process.

[0195] Embodiment 26. The method of any of embodiments 18-25 wherein the method comprises: with respect to a first frame of data captured at a first time and a second frame of data captured at a second time, using inertial measurement unit (IMU) data to modify the second frame of data to create a compensated frame of data by estimating point cloud data at the first time based on the second frame of data and system movement between the first time and the second time obtained from the IMU data; merging the first frame of point cloud data with the compensated frame of data to obtain a merged frame of point cloud data; and using at least the merged frame of point cloud data to evaluate the calibration of the system.

[0196] Embodiment 27. The method of embodiment 26 wherein the merged frame of point cloud data is obtained by including both the first frame of point cloud data and the compensated frame of data in the merged frame of point cloud data.

[0197] Embodiment 28. The method of any of embodiments 20-27 wherein the other location values are determined during a calibration process that is executed using data from the non- LiDAR sensor, the non-LiDAR sensor not being fully integrated with the LiDAR system.

[0198] Embodiment 29. The method of any of embodiments 20-28 wherein the other location values are determined during a calibration process that is executed using data from the non- LiDAR sensor, the non-LiDAR sensor being separately mounted to the moveable platform at a location on the moveable platform that is different from a location at which the LiDAR system is mounted.

[0199] Embodiment 30. The method of embodiment 18 wherein the one or more reference objects include one or more planar features and the method comprises: fitting a plane to points in the point cloud data corresponding to a planar feature of the one or more planar features; computing an orientation of a line normal to the plane fitted to the points in the point cloud data; and using the orientation of the line normal to the planar feature relative to an expected orientation to evaluate the planar feature.

[0200] Embodiment 31 . The method of embodiment 30 wherein the one or more planar features include a ground surface on which the movable platform is moving and the expected orientation is a stored value from a prior calibration of the system.

[0201] Embodiment 32. The method of embodiment 31 wherein the one or more planar features further include a vertical structure along a side of a road on which the movable platform is moving and the expected orientation a stored value from a prior calibration of the system.

[0202] Embodiment 33. The method of embodiment 32 wherein the one or more planar features include a curb.

[0203] Embodiment 34. The method of embodiment 32 wherein the one or more planar features include a wall.

[0204] Embodiment 35. A computer program product stored in a non-transitory computer readable medium comprising computer-executable instruction code that, when executed by oneor more processors of a LiDAR system, causes the one or more processors to execute a method according to any of embodiments 18-34.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A system for light ranging and detection (LiDAR) mounted on a moveable platform, the system having a prior calibration comprising calibration parameters based on a prior orientation of the system relative to the movable platform, the system comprising: a transceiver transmitting laser light toward a field of view (FOV) and receiving return light from the FOV; and a controller configured to: generate point cloud data in response to the transceiver receiving return light from the FOV, the point cloud data comprising a plurality of respective frames of point cloud data collected at a plurality of respective times; identify, using the point cloud data, one or more reference objects in a useenvironment of the movable platform; and evaluate calibration of the system by determining, using point cloud data associated with the one or more reference objects, whether an orientation of the system relative to the movable platform has changed from the prior calibration such that recalibration is needed.

2. The system of claim 1 wherein: the one or more reference objects include substantially parallel lane lines; and the controller is configured to, for each of a plurality of frames of point cloud data: identify a horizon line; and use the horizon line and point cloud data associated with the lane lines to evaluate calibration of the system.

3. The system of claim 2 wherein the controller is configured to evaluate calibration of the system by determining, for a plurality of frames, current location values of intersection points of the lane lines with the horizon lines and comparing the current location values to other locationvalues determined during at least one of: a previously-executed calibration process and a calibration process executed using data from a non-LiDAR sensor.

4. The system of claim 2 wherein the controller is configured to evaluate calibration of the system by: determining, for a plurality of frames, current location values of intersection points of the lane lines with the horizon lines; using the current location values to compute current calibration parameter values comprising pitch, yaw, and / or roll values; and comparing the current calibration parameters to other calibration parameter values determined during at least one of: a previously-executed calibration process and a calibration process executed using data from a non-LiDAR sensor.

5. The system of claim 3 wherein the other location values are determined during a calibration process that is executed using data from the non-LiDAR sensor, the non-LiDAR sensor comprising a camera.

6. The system of claim 3 wherein the other location values are determined during a previously-executed calibration process.

7. The system of claim 4 wherein the other calibration parameter values are determined during a calibration process that is executed using data from the non-LiDAR sensor, the non- LiDAR sensor comprising a camera.

8. The system of claim 4 wherein the other calibration parameter values are determined during a previously-executed calibration process.

9. The system of claim 1 wherein the controller is configured to: with respect to a first frame of data captured at a first time and a second frame of data captured at a second time, use inertial measurement unit (IMU) data to modify the second frame of data to create a compensated frame of data by estimating point cloud data at the first timebased on the second frame of data and system movement between the first time and the second time obtained from the IMU data; merge the first frame of point cloud data with the compensated frame of data to obtain a merged frame of point cloud data; and use at least the merged frame of point cloud data to evaluate calibration of the system.

10. The system of claim 9 wherein the merged frame of point cloud data is obtained by including both the first frame of point cloud data and the compensated frame of data.

11. The system of claim 3 wherein the other location values are determined during a calibration process that is executed using data from the non-LiDAR sensor, the non-LiDAR sensor not being fully integrated with the LiDAR system.

12. The system of claim 3 wherein the other location values are determined during a calibration process that is executed using data from the non-LiDAR sensor, the non-LiDAR sensor being separately mounted to the moveable platform at a location on the moveable platform that is different from a location at which the LiDAR system is mounted.

13. The system of claim 1 wherein: the one or more reference objects include one or more planar features; and the controller is further configured to: fit a plane to points in the point cloud data corresponding to a planar feature of the one or more planar features; compute an orientation of a line normal to the plane fitted to the points in the point cloud data; and use the orientation of the line normal to the planar feature relative to an expected orientation to evaluate the planar feature.

14. The system of claim 13 wherein the one or more planar features include a ground surface on which the movable platform is moving and the expected orientation is a stored value from a prior calibration of the system.

15. The system of claim 14 wherein the one or more planar features further include a vertical structure along a side of a road on which the movable platform is moving and the expected orientation a stored value from a prior calibration of the system.

16. The system of claim 15 wherein the one or more planar features include a curb.

17. The system of claim 15 wherein the one or more planar features include a wall.

18. A method for dynamic calibration of a light ranging and detection (LiDAR) system mounted on a moveable platform, the system associated with a prior calibration comprising calibration parameters based on a prior orientation of the system relative to the movable platform, the method comprising: transmitting laser light toward a field of view (FOV) and receiving return light from the FOV; generating point cloud data in response to the transceiver receiving return light from the FOV, the point cloud data comprising a plurality of respective frames of point cloud data collected at a plurality of respective times; identifying, using the point cloud data, one or more reference objects in a useenvironment of the movable platform; and evaluating calibration of the system by determining, using point cloud data associated with the one or more reference objects, whether an orientation of the system relative to the movable platform has changed from the prior calibration such that recalibration is needed.

19. The method of claim 18 wherein the one or more reference objects include substantially parallel lane lines and the method comprises, for each of a plurality of frames of point cloud data: identifying a horizon line; andusing the horizon line and point cloud data associated with the lane lines to evaluate calibration of the system.

20. The method of claim 19 wherein evaluating calibration of the system comprises, for a plurality of frames, determining current location values of intersection points of the lane lines with the horizon lines and comparing the current location values to other location values determined during at least one of: a previously-executed calibration process and a calibration process executed using data from a non-LiDAR sensor.

21. The method of claim 19 wherein evaluating calibration of the system comprises, for a plurality of frames: determining current location values of intersection points of the lane lines with the horizon lines; using the current location values to compute current calibration parameter values comprising pitch, yaw, and / or roll values; and comparing the current calibration parameters to other calibration parameter values determined during at least one of: a previously-executed calibration process and a calibration process executed using data from a non-LiDAR sensor.

22. The method of claim 20 wherein the other location values are determined during a calibration process that is executed using data from the non-LiDAR sensor, the non-LiDAR sensor comprising a camera.

23. The method of claim 20 wherein the other location values are determined during a previously-executed calibration process.

24. The method of claim 21 wherein the other calibration parameter values are determined during a calibration process that is executed using data from the non-LiDAR sensor, the non- LiDAR sensor comprising a camera.

25. The method of claim 21 wherein the other calibration parameter values are determined during a previously-executed calibration process.

26. The method of claim 18 wherein the method comprises: with respect to a first frame of data captured at a first time and a second frame of data captured at a second time, using inertial measurement unit (IMU) data to modify the second frame of data to create a compensated frame of data by estimating point cloud data at the first time based on the second frame of data and system movement between the first time and the second time obtained from the IMU data; merging the first frame of point cloud data with the compensated frame of data to obtain a merged frame of point cloud data; and using at least the merged frame of point cloud data to evaluate the calibration of the system.

27. The method of claim 26 wherein the merged frame of point cloud data is obtained by including both the first frame of point cloud data and the compensated frame of data in the merged frame of point cloud data.

28. The method of claims 20 wherein the other location values are determined during a calibration process that is executed using data from the non-LiDAR sensor, the non-LiDAR sensor not being fully integrated with the LiDAR system.

29. The method of claim 20 wherein the other location values are determined during a calibration process that is executed using data from the non-LiDAR sensor, the non-LiDAR sensor being separately mounted to the moveable platform at a location on the moveable platform that is different from a location at which the LiDAR system is mounted.

30. The method of claim 18 wherein the one or more reference objects include one or more planar features and the method comprises: fitting a plane to points in the point cloud data corresponding to a planar feature of the one or more planar features;computing an orientation of a line normal to the plane fitted to the points in the point cloud data; and using the orientation of the line normal to the planar feature relative to an expected orientation to evaluate the planar feature.

31. The method of claim 30 wherein the one or more planar features include a ground surface on which the movable platform is moving and the expected orientation is a stored value from a prior calibration of the system.

32. The method of claim 31 wherein the one or more planar features further include a vertical structure along a side of a road on which the movable platform is moving and the expected orientation a stored value from a prior calibration of the system.

33. The method of claim 32 wherein the one or more planar features include a curb.

34. The method of claim 32 wherein the one or more planar features include a wall.

35. A computer program product stored in a non-transitory computer readable medium comprising computer-executable instruction code that, when executed by one or more processors of a LiDAR system, causes the one or more processors to execute a method according to claim 18.

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