Dynamic calibration method for avalanche photodiode on laser radar

Abstract

A method for dynamically calibrating a light detector of a light detection and ranging (LiDAR) system is disclosed. The method includes obtaining a usage indication. The method further includes determining, based on the indication, whether to perform calibration on the photodetector operating at the first bias voltage. The method further includes initiating a multi-point calibration of the photodetector within the bias voltage scan range in accordance with the determination to perform the calibration, where the multi-point calibration includes determining a second bias voltage corresponding to a current temperature in the operating environment of the photodetector. The method further includes determining whether to update the first bias voltage based on the second bias voltage based on the multi-point calibration.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Non-Provisional Patent Application No. 18 / 137,954, filed on April 21, 2023, entitled “DYNAMIC CALIBRATION METHOD OF AVALANCHE PHOTODIODES ON LIDAR”, U.S. Provisional Patent Application Serial No. 63 / 334,050, filed on April 22, 2022, entitled “DYNAMIC CALIBRATION METHOD OF AVALANCHE PHOTODIODES ON LIDAR”, and U.S. Provisional Patent Application Serial No. 63 / 414,348, filed on October 7, 2022, entitled “LIDAR DETECTOR LIVE CALIBRATION”. The contents of all applications are hereby incorporated by reference in their entirety for all purposes. Technical Field

[0003] The present disclosure relates generally to light detection, and more particularly to a method for dynamically calibrating an avalanche photodiode (APD) for a light detection and ranging (LiDAR) system. Background Art

[0004] Light detection and ranging (LiDAR) systems use light pulses to create images or point clouds of the external environment. LiDAR systems can be scanning or non-scanning systems. Some typical scanning LiDAR systems include a light source, a light emitter, a light steering system, and a light detector. The light source generates a light beam, which is guided in a specific direction by the light steering system when emitted from the LiDAR system. When the emitted light beam is scattered or reflected by an object, a portion of the scattered light 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 when the return light pulse is detected and the time when the corresponding light pulse in the light beam is emitted, the LiDAR system can determine the distance to the object based on the speed of light. This technique for determining distance is called time of flight (ToF) technology. The light steering system can guide the light beam along different paths to allow the LiDAR system to scan the surrounding environment and generate an image or point cloud. A typical non-scanning LiDAR system illuminates the entire field of view (FOV) instead of scanning the entire FOV. An example of a non-scanning LiDAR system is a flash LiDAR, which can also use ToF technology to measure the distance to an object. LiDAR systems can also use techniques other than time-of-flight and scanning to measure their surroundings. Summary of the invention

[0005] Avalanche photodiodes (APDs), which convert optical signals into electrical signals, are widely used as optical sensors in LiDAR systems and other optical sensing systems. APDs can also amplify optical signals so that the optical signals are detectable. The inherent physical properties of APDs allow APDs to have high detection performance, but also make APDs sensitive to temperature fluctuations. For example, the output gain of an APD varies based on the bias voltage applied to it and its surrounding temperature. In a thermal static environment, the output gain of an APD depends primarily on its bias voltage. Therefore, in a thermal static environment, it is relatively easy to find an optimized bias voltage that allows the APD to operate normally without avalanches.

[0006] When the temperature changes, it becomes more difficult to find the optimal bias voltage for normal operation of the APD. Currently, calibration of the APD bias voltage with respect to temperature changes is typically performed only once when the APD is manufactured. However, in practice, the temperature in an operating environment (e.g., the operating environment of a vehicle equipped with a LiDAR including an APD optical sensor) can vary over a wide range and sometimes change rapidly. Thus, when a LiDAR system including an APD optical sensor is operated in the field and the temperature changes, the APD bias voltage may deviate from the optimized value. At high temperatures, in order to obtain the same gain as at lower temperatures, a higher bias voltage may need to be provided so that the APD can operate properly.

[0007] Embodiments of the present disclosure provide a method for dynamically calibrating an APD of a LiDAR system so that an appropriate bias voltage can be provided and updated to the APD. The method includes, for example, the step of dynamically optimizing the APD bias voltage when the operating temperature changes. In order to dynamically calibrate the APD, a multi-point calibration of the APD is initiated within a bias voltage scanning range. Multiple data points are obtained at each of a plurality of bias voltage values ​​within the bias voltage scanning range, and are used to determine a voltage correction curve. The voltage correction curve can be a linear fit of the inverse of the output intensity (1 / Io) of the LiDAR system (e.g., the output intensity from a LiDAR receiver including an APD, an amplifier, and an ADC) relative to the bias voltage within the bias voltage scanning range. The bias voltage corresponding to the current temperature in the operating environment of the APD is determined based on the voltage correction curve. Using the disclosed method, the voltage correction curve of the APD can be used to obtain an optimized bias voltage under various operating conditions, thereby facilitating providing the correct bias voltage to the APD.

[0008] In one embodiment, a method for dynamically calibrating an avalanche photodiode (APD) of a light detection and ranging (LiDAR) system is provided. The method includes obtaining a usage indication for determining whether to perform calibration of a first bias voltage for operating the APD. The method further includes determining whether to perform calibration based on the indication. The method further includes initiating a multi-point calibration of the APD within a bias voltage scanning range based on the determination to perform calibration. The multi-point calibration includes determining a second bias voltage corresponding to a current temperature in an operating environment of the APD. The method further includes determining whether to update the first bias voltage based on the second bias voltage based on the multi-point calibration.

[0009] In one embodiment, a light detection and ranging (LiDAR) system is provided. The LiDAR system is configured to perform a method for dynamically calibrating an avalanche photodiode (APD). The method includes obtaining a usage indication for determining whether to perform calibration of a first bias voltage for operating the APD. The method further includes determining whether to perform calibration based on the indication. The method further includes initiating a multi-point calibration of the APD within a bias voltage scanning range based on the determination to perform calibration. The multi-point calibration includes determining a second bias voltage corresponding to a current temperature in an operating environment of the APD. The method further includes determining whether to update the first bias voltage based on the second bias voltage based on the multi-point calibration.

[0010] In one embodiment, a vehicle is provided. The vehicle includes a LiDAR system configured to perform a method for dynamically calibrating an avalanche photodiode (APD). The method includes obtaining a usage indication for determining whether to perform calibration of a first bias voltage for operating the APD. The method further includes determining whether to perform calibration based on the indication. The method further includes initiating a multi-point calibration of the APD within a bias voltage scanning range based on the determination to perform calibration. The multi-point calibration includes determining a second bias voltage corresponding to a current temperature in an operating environment of the APD. The method further includes determining whether to update the first bias voltage based on the second bias voltage based on the multi-point calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The present application may be best understood by referring to the embodiments described below in conjunction with the accompanying drawings, in which like parts may be represented by like reference numerals.

[0012] Figure 1 One or more exemplary LiDAR systems disposed or included in a motor vehicle are illustrated.

[0013] Figure 2 is a block diagram illustrating the interaction between an exemplary LiDAR system and a number of other systems including a vehicle perception and planning system.

[0014] Figure 3 is a block diagram illustrating an exemplary LiDAR system.

[0015] Figure 4 is a block diagram illustrating an exemplary fiber-based laser source.

[0016] FIG. 5A to FIG. 5C An exemplary LiDAR system that uses pulse signals to measure the distance to objects disposed in a field of view (FOV) is illustrated.

[0017] Figure 6 is a block diagram illustrating an exemplary apparatus for implementing the systems, apparatus, and methods in various embodiments.

[0018] Figure 7 is a block diagram illustrating an exemplary LiDAR system having an optical receiver and a photodetector for detecting and processing return signals according to an embodiment.

[0019] Figure 8 is a schematic diagram illustrating the relationship between a reference intensity and a bias voltage of an APD according to an embodiment of the present invention.

[0020] Fig. 9 Illustrated is a graphical representation of the results of a multi-point APD calibration of an APD operating at 34° C. in accordance with one embodiment of the present invention.

[0021] Fig.10 Illustrated is a graphical representation of multi-point APD calibration results for an APD operating at 107° C. in accordance with one embodiment of the present invention.

[0022] Fig.11 Illustrated is a graphical representation of multi-point APD calibration results for an APD operating at -7°C in accordance with one embodiment of the present invention.

[0023] Fig.12 is a flow chart illustrating use of an exemplary method to dynamically calibrate an avalanche photodiode (APD) of a LiDAR system.

[0024] Fig.13 is a flow chart illustrating use of an exemplary method to dynamically calibrate an avalanche photodiode (APD) of a LiDAR system.

[0025] Although the present invention has been described with reference to the above drawings, the drawings are intended to be illustrative and other embodiments are consistent with the spirit of the invention and are within the scope of the invention. DETAILED DESCRIPTION

[0026] In order to more thoroughly understand various embodiments of the present invention, the following description sets forth many specific details, such as specific configurations, parameters, examples, etc. However, it should be appreciated that such description is not intended to limit the scope of the present invention, but to provide a better description of exemplary embodiments.

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

[0028] As used herein, the phrase "in one embodiment" does not necessarily refer to the same embodiment, although it may be the same embodiment. Therefore, as described below, the various embodiments of the present invention can be easily combined without departing from the scope or spirit of the present disclosure.

[0029] As used herein, the term "or" is an inclusive "or" operator, and is equivalent to the terms "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 indicates otherwise.

[0031] As used herein, the term "coupled to" is intended to include direct coupling (where two elements coupled to each other are in contact with each other) and indirect coupling (where at least one additional element is located between the two elements), unless the context indicates otherwise. Therefore, the terms "coupled to" and "coupled with..." are used synonymously. In 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 intermediate 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 these terms. These terms are only used to distinguish one element from another element. For example, without departing from the scope of the various described examples, a first sensor may be referred to as a second sensor, and similarly, a second sensor may be referred to as a first sensor. The first sensor and the second sensor may both be sensors, and in some cases, may be separate and different sensors.

[0033] In addition, throughout the specification, the meanings of “a,” “an,” and “the” include plural forms, and the meaning of “in” may include “in” and “on.”

[0034] Although some of the various embodiments given herein constitute a single combination of inventive elements, it should be understood that the inventive subject matter is considered to include all possible combinations of disclosed elements. Thus, if one embodiment includes elements A, B, and C, and another embodiment includes elements B and D, the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly discussed herein. In addition, the transitional term "comprising" means having parts or components, or those parts or components. As used herein, the transitional term "comprising" is inclusive or open-ended, and does not exclude additional, unlisted 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 perform the set of functions on target data or data objects stored in the memory.

[0036] It should be noted that any language for computers should be understood 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 alone or together). It should be understood that the computing device includes a processor configured to execute software instructions stored on a tangible non-transitory computer-readable storage medium (e.g., a hard drive, FPGA, PLA, solid-state drive, RAM, flash memory, ROM, or any other volatile or non-volatile storage device). The software instructions configure or program the computing device to provide roles, responsibilities, or other functions, as discussed below with respect to the disclosed apparatus. In addition, the disclosed technology can be embodied as a computer program product, which includes a non-transitory computer-readable medium storing software instructions, which causes the processor to perform the disclosed steps associated with the implementation of a computer-based algorithm, process, method, or other instruction. In some embodiments, various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, which may be based on HTTP, HTTPS, AES, public-private key exchange, network service API, known financial transaction protocols, or other electronic information exchange methods. Data exchange between devices may be performed via: a packet-switched network, the Internet, a LAN, a WAN, a VPN, or other types of packet-switched networks; a circuit-switched network; a cell-switched network; or other types of networks.

[0037] LiDAR systems are common components of motor vehicles. Avalanche photodiodes (APDs) that convert optical signals into electrical signals are widely used as optical sensors in LiDAR systems. APDs can also amplify optical signals so that the optical signals are detectable. The inherent physical properties of APDs allow APDs to have high detection performance, but also make APDs sensitive to temperature fluctuations. For example, the output gain of an APD varies based on the bias voltage applied to it and its surrounding temperature. In a thermal static environment, the output gain of an APD depends mainly on the bias voltage. Therefore, in a thermal static environment, it is relatively easy to find an optimized bias voltage that allows the APD to operate normally without avalanches. When the temperature changes, it becomes more difficult to find the optimal bias voltage for the normal operation of the APD. At present, the calibration of the APD bias voltage relative to temperature changes is usually performed only once when the APD is manufactured. However, in practice, the temperature in the operating environment (e.g., the operating environment of a vehicle equipped with a LiDAR including an APD optical sensor) can vary over a wide range, and sometimes changes rapidly. In this way, when a LiDAR system including an APD optical sensor is operated in the field and the temperature changes, the APD bias voltage may deviate from the optimized value. At high temperatures, to obtain the same gain as at lower temperatures, a higher bias voltage may need to be provided so that the APD can operate properly.

[0038] Embodiments of the present disclosure provide a method for dynamically calibrating an APD of a LiDAR system so that an appropriate bias voltage can be provided and updated to the APD. The method includes, for example, the step of dynamically optimizing the APD bias voltage when the operating temperature changes. In order to dynamically calibrate the APD, a multi-point calibration of the APD is initiated within a bias voltage scanning range. Multiple data points are obtained at each of a plurality of bias voltage values ​​within the bias voltage scanning range, and are used to determine a voltage correction curve. The voltage correction curve can be a linear fit of the inverse of the output intensity (1 / Io) of the LiDAR system (e.g., the output intensity from a LiDAR receiver including an APD, an amplifier, and an ADC) relative to the bias voltage within the bias voltage scanning range. The bias voltage corresponding to the current temperature in the operating environment of the APD is determined based on the voltage correction curve. Using the disclosed method, the voltage correction curve of the APD can be used to obtain an optimized bias voltage under various operating conditions, thereby facilitating providing the correct bias voltage to the APD.

[0039] Figure 1One or more exemplary LiDAR systems 110 are illustrated as being arranged or included in a motor vehicle 100. The vehicle 100 may be a car, a sport utility vehicle (SUV), a truck, a train, a van, a bicycle, a motorcycle, a tricycle, a bus, an electric scooter, a tram, a ship, a boat, an underwater vehicle, an airplane, a helicopter, an unmanned aerial vehicle (UAV), a spacecraft, and the like. The motor vehicle 100 may be a vehicle having any level of automation. For example, the motor vehicle 100 may be a partially automated vehicle, a highly automated vehicle, a fully automated vehicle, or an unmanned vehicle. A partially automated vehicle may perform some driving functions without human driver intervention. For example, a partially automated vehicle may perform blind spot monitoring, lane keeping and / or lane change operations, automatic emergency braking, smart cruise control, and / or traffic following, and the like. Certain operations of a partially automated vehicle may be limited to specific applications or driving scenarios (e.g., limited to highway driving). A highly automated vehicle may generally perform all operations of a partially automated vehicle, but with fewer restrictions. Highly automated vehicles can also detect their own limits when operating the vehicle and request the driver to take over control of the vehicle when necessary. Fully automated vehicles can perform all vehicle operations without driver intervention, but can also detect their own limits and request the driver to take over when necessary. Driverless vehicles can operate autonomously without any driver intervention.

[0040] In a typical configuration, the motor vehicle 100 includes one or more LiDAR systems 110 and 120A-120I. Each of the LiDAR systems 110 and 120A-120I can be a scanning-based LiDAR system and / or a non-scanning LiDAR system (e.g., 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 emits lasers to illuminate the FOV without scanning. For example, a flash LiDAR is a type of non-scanning-based LiDAR system. A flash LiDAR can emit lasers to illuminate the FOV simultaneously using a single light pulse or light beams.

[0041] LiDAR systems are common sensors used in at least partially automated vehicles. Figure 1As shown, the 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., on top of the vehicle). Placing the LiDAR system 110 on top of the vehicle facilitates 360-degree scanning around the vehicle 100. In some other embodiments, the motor vehicle 100 may include multiple LiDAR systems, including two or more of the systems 110 and / or 120A-120I. Figure 1 As shown, in one embodiment, multiple LiDAR systems 110 and / or 120A-120I are attached to the vehicle 100 at different locations of the vehicle. For example, LiDAR system 120A is attached to the vehicle 100 at the right front corner; LiDAR system 120B is attached to the vehicle 100 at the front center location; LiDAR system 120C is attached to the vehicle 100 at the left front corner; LiDAR system 120D is attached to the vehicle 100 at the right side mirror; LiDAR system 120E is attached to the vehicle 100 at the left side mirror; LiDAR system 120F is attached to the vehicle 100 at the rear center location; LiDAR system 120G is attached to the vehicle 100 at the right rear corner; LiDAR system 120H is attached to the vehicle 100 at the left rear corner; and / or LiDAR system 120I is attached to the vehicle 100 at the center toward the rear end (e.g., the rear end of the top of the vehicle). It should be understood that one or more LiDAR systems may be distributed and attached to a vehicle in any desired manner, and Figure 1 Only one embodiment is illustrated. As another example, LiDAR systems 120D and 120E may be attached to the B-pillars of vehicle 100 instead of rearview mirrors. As another example, LiDAR system 120B may be attached to the windshield of vehicle 100 instead of the front bumper.

[0042] In some embodiments, LiDAR systems 110 and 120A-120I are independent LiDAR systems with their respective laser sources, control electronics, transmitters, receivers and / or steering mechanisms. In other embodiments, some of LiDAR systems 110 and 120A-120I may share one or more components to form a distributed sensor system. In one example, optical fiber is used to deliver laser light from a centralized laser source to all LiDAR systems. For example, system 110 (or another system positioned in the center or any position of vehicle 100) includes a light source, a transmitter, and a light detector, but no steering mechanism. System 110 can distribute transmitted light to each of systems 120A-120I. Transmitted light can be distributed via optical fiber. Optical connectors can be used to couple optical fibers to each of systems 110 and 120A-120I. In some examples, one or more systems in systems 120A-120I include a steering mechanism, but no light source, transmitter, or light detector. The steering mechanism may include one or more movable mirrors, such as one or more polygonal mirrors, one or more single plane mirrors, one or more multi-plane mirrors, etc. The embodiments of the light source, the emitter, the steering mechanism and the light detector will be described in more detail below. Via the steering mechanism, one or more systems in the system 120A-120I scan the light into one or more corresponding FOVs and receive the corresponding return light. The return light is formed by scattering or reflecting the transmitted light by one or more objects in the FOV. The system 120A-120I may also include a collecting lens and / or other optical devices to focus and / or guide the return light into an optical fiber, and the optical fiber delivers the received return light to the system 110. The system 110 includes one or more light detectors for detecting the received return light. In some examples, the system 110 is arranged inside the vehicle so that it is in a temperature-controlled environment, and one or more systems 120A-120I may be at least partially exposed to the external environment.

[0043] Figure 2 2 is a block diagram 200 illustrating the interaction between an onboard LiDAR system 210 and a plurality of other systems including a vehicle perception and planning system 220. The LiDAR system 210 may be mounted on or integrated into a vehicle. The LiDAR system 210 includes a sensor that scans laser light into the surrounding environment to measure the distance, angle, and / or velocity of objects. Based on the scattered light returned to the LiDAR system 210, it may generate sensor data (e.g., image data or 3D point cloud data) representing the perceived external environment.

[0044] The LiDAR system 210 may include one or more of a short-range LiDAR sensor, a mid-range LiDAR sensor, and a long-range LiDAR sensor. The short-range LiDAR sensor measures objects up to about 20-50 meters from the LiDAR sensor. The short-range LiDAR sensor can be used, for example, to monitor nearby moving objects (e.g., pedestrians crossing the road in a school zone), parking assistance applications, etc. The medium-range LiDAR sensor measures objects up to about 70-200 meters from the LiDAR sensor. The medium-range LiDAR sensor can be used, for example, to monitor road intersections, assist in merging into or exiting a highway, etc. The long-range LiDAR sensor can measure objects located 200 meters and above. Long-range LiDAR sensors are typically used when the vehicle is traveling at high speeds (e.g., on a highway), so that the vehicle's control system may only have a few seconds (e.g., 6-8 seconds) to respond to any situation detected by the LiDAR sensor. Figure 2 As shown, in one embodiment, LiDAR sensor data can be provided to vehicle perception and planning system 220 via communication path 213 for further processing and control of vehicle operation. Communication path 213 can be any wired or wireless communication link capable of transmitting data.

[0045] Still reference Figure 2 In some embodiments, other onboard sensors 230 are configured to provide additional sensor data alone or in conjunction with the LiDAR system 210. Other onboard sensors 230 may include, for example, one or more cameras 232, one or more radars 234, one or more ultrasonic sensors 236, and / or other sensors 238. The camera 232 may capture images and / or videos of the external environment of the vehicle. The camera 232 may capture, for example, high-definition (HD) video with millions of pixels per frame. The camera includes an image sensor that facilitates the generation of monochrome or color images and videos. Color information may be important when interpreting data in certain situations (e.g., interpreting images of traffic lights). Color information may not be available from other sensors such as LiDAR or radar sensors. The camera 232 may include one or more of a narrow focal length camera, a wide focal length camera, a side camera, an infrared camera, a fisheye camera, and the like. The image and / or video data generated by the camera 232 may also be provided to the vehicle perception and planning system 220 via a communication path 233 for further processing and control of vehicle operation. The communication path 233 may be any wired or wireless communication link capable of transmitting data. The camera 232 may be mounted or integrated into the vehicle at any location (eg, rearview mirror, pillar, front grille and / or rear bumper, etc.).

[0046] Other on-board sensors 230 may also include radar sensors 234. Radar sensors 234 use radio waves to determine the distance, angle, and speed of an object. Radar sensors 234 generate electromagnetic waves in the radio or microwave spectrum. Electromagnetic waves are reflected by objects, and some of the reflected waves return to the radar sensor, thereby providing information about the position and speed of the object. Radar sensors 234 may include one or more of short-range radars, medium-range radars, and long-range radars. Short-range radars measure objects about 0.1-30 meters from the radar. Short-range radars are useful in detecting objects near vehicles (such as other vehicles, buildings, walls, pedestrians, cyclists, etc.). Short-range radars can be used to detect blind spots, assist in lane changes, provide rear-end collision alerts, assist in parking, provide emergency braking, etc. Medium-range radars measure objects about 30-80 meters from the radar. Long-range radars measure objects located at about 80-200 meters. Medium-range and / or long-range radars can be used, for example, for traffic tracking, adaptive cruise control, and / or highway automatic braking. Sensor data generated by radar sensor 234 may also be provided to vehicle perception and planning system 220 via communication path 233 for further processing and control of vehicle operation. Radar sensor 234 may be mounted or integrated into the vehicle at any location (e.g., rearview mirror, pillar, front grille and / or rear bumper, etc.).

[0047] Other on-board sensors 230 may also include ultrasonic sensors 236. Ultrasonic sensors 236 use sound waves or pulses to measure objects located outside the vehicle. The sound waves generated by ultrasonic sensors 236 are emitted to the surrounding environment. At least some of the emitted waves are reflected by the object and returned to the ultrasonic sensor 236. Based on the return signal, the distance of the object can be calculated. Ultrasonic sensors 236 can be used, for example, to check blind spots, identify parking spaces, provide lane change assistance in traffic, etc. The sensor data generated by ultrasonic sensors 236 can also be provided to the vehicle perception and planning system 220 via the communication path 233 for further processing and control of vehicle operation. Ultrasonic sensors 236 can be installed or integrated into the vehicle at any location (e.g., rearview mirrors, pillars, front grilles and / or rear bumpers, etc.).

[0048] In some embodiments, one or more other sensors 238 may be attached to the vehicle and may also generate sensor data. Other sensors 238 may include, for example, a global positioning system (GPS), an inertial measurement unit (IMU), etc. The sensor data generated by other sensors 238 may also be provided to the vehicle perception and planning system 220 via the communication path 233 for further processing and control of vehicle operation. It should be understood that the communication path 233 may include one or more communication links to transmit data between the various sensors 230 and the vehicle perception and planning system 220.

[0049] In some embodiments, Figure 2 As shown, sensor data from other onboard sensors 230 can be provided to the onboard LiDAR system 210 via communication path 231. The LiDAR system 210 can process the sensor data from the other onboard sensors 230. For example, sensor data from a camera 232, a radar sensor 234, an ultrasonic sensor 236, and / or other sensors 238 can be correlated or fused with the sensor data LiDAR system 210, thereby at least partially offloading the sensor fusion process performed by the vehicle perception and planning system 220. It should be understood that other configurations can also be implemented to transmit and process sensor data from 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 the LiDAR system 210).

[0050] Still reference Figure 2 In some embodiments, sensors on other vehicles 250 are used to provide additional sensor data alone or in conjunction with the LiDAR system 210. For example, two or more nearby vehicles may have their own LiDAR sensors, cameras, radar sensors, ultrasonic sensors, etc. Nearby vehicles can transmit and share sensor data with each other. Communication between vehicles is also known as V2V (vehicle-to-vehicle) communication. For example, Figure 2 As shown, sensor data generated by other vehicles 250 can be transmitted to vehicle perception and planning system 220 and / or onboard LiDAR system 210 via communication path 253 and / or communication path 251, respectively. Communication paths 253 and 251 can be any wired or wireless communication links capable of transmitting data.

[0051] Sharing sensor data facilitates better perception of the environment outside the vehicle. For example, the first vehicle may not sense a pedestrian that is behind the second vehicle but is approaching the first vehicle. The second vehicle can share sensor data related to the pedestrian with the first vehicle so that the first vehicle can have additional reaction time to avoid a collision with the pedestrian. In some embodiments, similar to the data generated by sensor 230, data generated by sensors on other vehicles 250 can be correlated or fused with sensor data generated by LiDAR system 210 (or other LiDAR systems located in other vehicles), thereby at least partially offloading the sensor fusion process performed by the vehicle perception and planning system 220.

[0052] In some embodiments, the smart infrastructure system 240 is used to provide sensor data alone or in conjunction with the LiDAR system 210. Certain infrastructures may be configured to communicate with vehicles to transfer information, and vice versa. Communication between vehicles and infrastructure is generally referred to as V2I (vehicle to infrastructure) communication. For example, the smart infrastructure system 240 may include a smart traffic light that can communicate its status to approaching vehicles with messages such as "turn yellow in 5 seconds". The smart infrastructure system 240 may also include its own LiDAR system installed near the intersection so that it can transmit traffic monitoring information to the vehicle. For example, a vehicle turning left 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 this case, the sensors of the smart infrastructure system 240 can provide useful data to the vehicle turning left. Such data may include, for example, traffic conditions, object information in the direction of the vehicle's turn, traffic light status and predictions, etc. These sensor data generated by the smart infrastructure system 240 can be provided to the vehicle perception and planning system 220 and / or the onboard LiDAR system 210 via communication paths 243 and / or 241, respectively. Communication paths 243 and / or 241 may include any wired or wireless communication links capable of transmitting data. For example, sensor data from the smart infrastructure system 240 can be transmitted to the LiDAR system 210 and correlated or fused with sensor data generated by the LiDAR system 210, thereby at least partially offloading the sensor fusion process performed by the vehicle perception and planning system 220. The above-mentioned V2V and V2I communications are examples of vehicle-to-X (V2X) communications, where "X" represents any other device, system, sensor, infrastructure, etc. that can share data with the vehicle.

[0053] Still reference Figure 2, via various communication paths, the vehicle perception and planning system 220 receives sensor data from one or more of the LiDAR system 210, other vehicle onboard sensors 230, other vehicles 250, and / or the smart infrastructure system 240. In some embodiments, different types of sensor data are correlated and / or fused by the sensor fusion subsystem 222. For example, the sensor fusion subsystem 222 can generate a 360-degree model using multiple images or videos captured by multiple cameras set at different locations of the vehicle. The sensor fusion subsystem 222 obtains sensor data from different types of sensors and uses the combined data to more accurately perceive the environment. For example, the onboard camera 232 may not capture a clear image because it is directly facing the sun or a light source (e.g., the headlights of another vehicle at night). The LiDAR system 210 may not be affected too much, and therefore the sensor fusion subsystem 222 can combine the sensor data provided by the camera 232 and the LiDAR system 210, and use the sensor data provided by the LiDAR system 210 to compensate for the unclear image captured by the camera 232. As another example, in rainy or foggy weather, radar sensor 234 may work better than camera 232 or LiDAR system 210. Accordingly, sensor fusion subsystem 222 may use sensor data provided by radar sensor 234 to compensate for sensor data provided by camera 232 or LiDAR system 210.

[0054] In other examples, sensor data generated by other onboard sensors 230 may have a lower resolution (e.g., radar sensor data) and, therefore, may need to be correlated and confirmed by the LiDAR system 210, which typically has a higher resolution. For example, the radar sensor 234 may detect a manhole cover (also known as a manhole cover) as an object that the vehicle is approaching. Due to the low resolution nature of the radar sensor 234, the vehicle perception and planning system 220 may not be able to determine whether the object is an obstacle that the vehicle needs to avoid. Therefore, the high resolution sensor data generated by the LiDAR system 210 can be used to correlate and confirm that the object is a manhole cover and will not cause damage to the vehicle.

[0055] The vehicle perception and planning system 220 further includes an object classifier 223. Using the raw sensor data and / or the associated / fused data provided by the sensor fusion subsystem 222, the object classifier 223 can use any computer vision technology to detect and classify objects and estimate the location of the objects. In some embodiments, the object classifier 223 can use machine learning-based techniques to detect and classify objects. Examples of machine learning-based techniques include using 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 networks (R-FCN), single shot detectors (SSD), spatial pyramid pooling (SPP-net) and / or "You Only Look Once" (Yolo).

[0056] The vehicle perception and planning system 220 further includes a road detection subsystem 224. The road detection subsystem 224 locates the road and identifies objects and / or markings on the road. For example, based on the raw or fused sensor data provided by the radar sensor 234, the camera 232, and / or the LiDAR system 210, the road detection subsystem 224 can build a 3D model of the road based on machine learning techniques (e.g., a pattern recognition algorithm for identifying lanes). Using the 3D model of the road, the road detection subsystem 224 can identify objects (e.g., obstacles or debris on the road) and / or markings (e.g., lane lines, turn signs, crosswalk signs, etc.) on the road.

[0057] The vehicle perception and planning system 220 further includes a positioning and vehicle attitude subsystem 225. Based on the original or fused sensor data, the positioning and vehicle attitude subsystem 225 can determine the position of the vehicle and the attitude of the vehicle. For example, using sensor data from the LiDAR system 210, the camera 232 and / or GPS data, the positioning and vehicle attitude subsystem 225 can determine the precise position of the vehicle on the road and the six degrees of freedom of the vehicle (e.g., whether the vehicle is moving forward or backward, up or down, left or right). In some embodiments, a high-definition (HD) map is used for vehicle positioning. The HD map can provide a very detailed three-dimensional computer map to accurately locate the position of the vehicle. For example, using the HD map, the positioning and vehicle attitude subsystem 225 can accurately determine the current position of the vehicle (e.g., which lane of the road the vehicle is currently in, how close it is to the curb or sidewalk) and predict the future position of the vehicle.

[0058] The vehicle perception and planning system 220 further includes an obstacle predictor 226. The objects identified by the object classifier 223 can be stationary (e.g., light poles, road signs) or dynamic (e.g., moving pedestrians, bicycles, another car). For moving objects, predicting their movement paths or future positions is important for avoiding collisions. The obstacle predictor 226 can predict obstacle trajectories and / or warn the driver or the vehicle planning subsystem 228 of potential collisions. For example, if the probability that the trajectory of the obstacle intersects the current movement path of the vehicle is high, the obstacle predictor 226 can generate such a warning. The obstacle predictor 226 can use various techniques to make such predictions. These techniques include, for example, constant speed or acceleration models, constant turning rate and speed / acceleration models, models based on Kalman filters and extended Kalman filters, models based on recursive neural networks (RNNs), models based on long short-term memory (LSTM) neural networks, encoder-decoder RNN models, etc.

[0059] Still reference Figure 2 In some embodiments, the vehicle perception and planning system 220 further includes a vehicle planning subsystem 228. The vehicle planning subsystem 228 may include one or more planners, such as a route planner, a driving behavior planner, and a motion planner. The route planner may plan the route of the vehicle based on the vehicle's current position data, target position data, traffic information, etc. The driving behavior planner uses the obstacle prediction results provided by the obstacle predictor 226 to adjust the timing and planned movements based on how other objects may move. The motion planner determines the specific operations that the vehicle needs to follow. The planning results are then transmitted to the vehicle control system 280 via the vehicle interface 270. Communications may be performed via communication paths 223 and 271, which may include any wired or wireless communication links that can transmit data.

[0060] The vehicle control system 280 controls the vehicle's steering mechanism, throttle, brakes, etc. to operate the vehicle according to the planned route and movement. In some examples, the vehicle perception and planning system 220 may further include a user interface 260 that provides access to the vehicle control system 280 to the user (e.g., the driver) to, for example, override or take over control of the vehicle when necessary. The user interface 260 may also be separate from the vehicle perception and planning system 220. The user interface 260 may communicate with the vehicle perception and planning system 220, for example, to obtain and display raw or fused sensor data, identified objects, the position / attitude of the vehicle, etc. These displayed data may help the user better operate the vehicle. The user interface 260 may communicate with the vehicle perception and planning system 220 and / or the vehicle control system 280 via communication paths 221 and 261, respectively, which include any wired or wireless communication links that can transmit data. It should be understood that Figure 2 The various systems, sensors, communication links and interfaces in the can be configured in any desired manner and are not limited to Figure 2 Configuration shown.

[0061] Figure 3 is a block diagram illustrating an exemplary LiDAR system 300. The LiDAR system 300 may be used to implement Figure 1 and Figure 2 . In one embodiment, the LiDAR system 300 includes a light source 310, a transmitter 320, an optical receiver and light detector 330, a steering system 340, and a control circuit 350. These components are coupled together using communication paths 312, 314, 322, 332, 342, 352, and 362. These communication paths include communication links (wired or wireless, bidirectional or unidirectional) between various LiDAR system components, but do not have to be physical components themselves. Although the communication path can be implemented by one or more wires, buses, or optical fibers, the communication path can also be a wireless channel or a free space optical path, so that there is no physical communication medium. For example, in one embodiment of the LiDAR system 300, the communication path 314 between the light source 310 and the transmitter 320 can be implemented using one or more optical fibers. Communication paths 332 and 352 can represent optical paths implemented using free space optical components and / or optical fibers. And the communication paths 312, 322, 342 and 362 can be implemented using one or more wires carrying electrical signals. The communication paths can also include one or more of the above-mentioned types of communication media (for example, they can include optical fibers and free space optical components, or include one or more optical fibers and one or more wires).

[0062] In some embodiments, LiDAR system 300 may be a coherent LiDAR system. Frequency modulated continuous wave (FMCW) LiDAR is one example. Coherent LiDAR detects objects by mixing the return light from the object with the light from a coherent laser emitter. Figure 3 As shown, if the LiDAR system 300 is a coherent LiDAR, it can include a route 372 that provides a portion of the transmitted light from the transmitter 320 to the optical receiver and light detector 330. The transmitted light provided by the transmitter 320 can be modulated light and can be divided into two parts. One part is emitted to the FOV, and the second part is sent to the optical receiver and light detector of the LiDAR system. The second part is also referred to as the light kept local (LO) to the LiDAR system. The transmitted 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 then detected and recombined by interference with the second part of the transmitted light that remains local. Coherent LiDAR provides a mechanism for optically sensing the range of objects and their relative speed along the line of sight (LOS).

[0063] The LiDAR system 300 may also include Figure 3 Other components not shown in the diagram may include a power bus, power supplies, LED indicators, switches, etc. Additionally, there may be other communication connections between components, such as a direct connection between the light source 310 and the optical receiver and light detector 330 to provide a reference signal so that the time from the emission of a light pulse to the detection until the return light pulse can be accurately measured.

[0064] The light source 310 outputs a laser for illuminating an object in the field of view (FOV). The laser may be an infrared light having a wavelength in the range of 700 nm to 1 mm. The light source 310 may be, for example, a semiconductor-based laser (e.g., a diode laser) and / or an optical fiber-based laser. The semiconductor-based laser may be, for example, an edge-emitting laser (EEL), a vertical cavity surface-emitting laser (VCSEL), an external cavity diode laser, a vertical external cavity surface-emitting laser, a distributed feedback (DFB) laser, a distributed Bragg reflector (DBR) laser, an interband cascade laser, a quantum cascade laser, a quantum well laser, a double heterostructure laser, etc. An optical 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, the optical fiber laser is based on a double-clad optical fiber in which the gain medium forms the core of an optical fiber surrounded by two layers of cladding. Double-clad fibers allow the core to be pumped with a high-power beam, thus enabling the laser source to become a high-power fiber laser source.

[0065] In some embodiments, the light source 310 includes a master oscillator (also referred to as a seed laser) and a power amplifier (MOPA). The power amplifier amplifies the output power of the seed laser. The power amplifier can be a fiber amplifier, a bulk amplifier, or a semiconductor optical amplifier. The seed laser can be a diode laser (e.g., a Fabry-Perot cavity laser, a distributed feedback laser), a solid-state bulk laser, or an external cavity tunable diode laser. In some embodiments, the light source 310 can be an optically pumped microchip laser. A microchip laser is an alignment-free monolithic solid-state laser in which the laser crystal is directly in contact with the end mirror of the laser resonator. Microchip lasers are typically pumped by a laser diode (directly or using an optical fiber) to obtain the desired output power. Microchip lasers can be based on a neodymium-doped yttrium aluminum garnet (Y3Al5O12) laser crystal (i.e., Nd:YAG), or a neodymium-doped vanadate (i.e., ND:YVO4) laser crystal. In some examples, the light source 310 can have multiple amplification stages to achieve high power gain so 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 the light source 310 can be controlled so that the power gain can be changed to achieve any desired laser output power.

[0066] Figure 4 4 is a block diagram illustrating an exemplary fiber-based laser source 400 having a seed laser and one or more pumps (eg, laser diodes) for pumping a desired output power. Figure 3An example of a light source 310 is shown. In some embodiments, the fiber-based laser source 400 includes a seed laser 402 to generate initial light pulses of one or more wavelengths (e.g., an infrared wavelength such as 1550 nm), which are provided to a wavelength division multiplexer (WDM) 404 via an optical fiber 403. The fiber-based laser source 400 further includes a pump 406 for providing laser power (e.g., a different wavelength, such as 980 nm) to the WDM 404 via an optical fiber 405. The WDM 404 multiplexes the light pulses provided by the seed laser 402 and the laser power provided by the pump 406 onto a single optical fiber 407. The output of the WDM 404 can then be provided to one or more preamplifiers 408 via the optical fiber 407. The preamplifier 408 can be an optical amplifier (e.g., having a gain of about 10-30 dB) that amplifies the optical signal. In some embodiments, the preamplifier 408 is a low noise amplifier. The preamplifier 408 outputs to the optical combiner 410 via an optical fiber 409. Combiner 410 combines the output laser of preamplifier 408 with the laser power provided by pump 412 via optical fiber 411. Combiner 410 can combine optical signals having the same wavelength or different wavelengths. An example of a combiner is a WDM. Combiner 410 provides the combined optical signal to boost amplifier 414, which produces output optical pulses via optical fiber 410. Boost amplifier 414 provides further amplification of the optical signal (e.g., another 20-40 dB). The output optical pulses can then be transmitted to transmitter 320 and / or steering mechanism 340 (e.g., Figure 3 It should be understood that Figure 4 An exemplary configuration of a fiber-based laser source 400 is shown. The laser source 400 may have a Figure 4 One or more components shown in and / or Figure 4 Many other configurations of different combinations of other components not shown (e.g., other components such as power supplies, lenses, filters, splitters, combiners, etc.).

[0067] In some variations, the fiber-based laser source 400 can be controlled (e.g., by the control circuit 350) to generate pulses of different amplitudes based on the fiber gain profile of the optical fiber used in the fiber-based laser source 400. The communication path 312 couples the fiber-based laser source 400 to the control circuit 350 (e.g., Figure 3350) so that the components of the fiber-based laser source 400 can be controlled by the control circuit 350 or otherwise communicate with the control circuit. Alternatively, the fiber-based laser source 400 can include its own dedicated controller. Instead of the control circuit 350 communicating directly with the components of the fiber-based laser source 400, the dedicated controller of the fiber-based laser source 400 communicates with the control circuit 350 and controls and / or communicates with the components of the fiber-based laser source 400. The fiber-based laser source 400 may also include other components not shown, such as one or more power connectors, power sources, and / or power lines.

[0068] refer to Figure 3 , typical operating wavelengths of the light source 310 include, for example, about 850nm, about 905nm, about 940nm, about 1064nm, and about 1550nm. For laser safety, the upper limit of the maximum available laser power is set by the U.S. Food and Drug Administration (FDA) regulations. The optical power limit of the 1550nm wavelength is much higher than the optical power limits of the other wavelengths mentioned above. In addition, at 1550nm, the optical power loss in the optical fiber is very low. These characteristics of the 1550nm wavelength make it more conducive to long-distance LiDAR applications. The amount of optical power output from the light source 310 can be characterized by its peak power, average power, pulse energy, and / or pulse energy density. Peak power is the ratio of pulse energy to pulse width (e.g., full width at half maximum or FWHM). Therefore, for a fixed amount of pulse energy, a smaller pulse width can provide a larger peak power. The pulse width can be in the range of nanoseconds or picoseconds. The average power is the product of the pulse energy and the pulse repetition rate (PRR). As described in more detail below, PRR represents the frequency of the pulsed laser. In general, the smaller the time interval between pulses, the higher the PRR. The PRR generally corresponds to the maximum range that the LiDAR system can measure. The light source 310 can be configured to generate pulses with a high PRR to meet the desired number of data points in the point cloud generated by the LiDAR system. The light source 310 can also be configured to generate pulses with a medium or low PRR to meet the desired maximum detection distance. Wall plug efficiency (WPE) is another factor in evaluating the total power consumption, which can be a useful metric for evaluating the efficiency of the laser. For example, if Figure 1 As shown, multiple LiDAR systems can be attached to a vehicle, which can be an electric vehicle or a vehicle with limited fuel or battery power. Therefore, high WPE and intelligent ways of using laser power are often important considerations when selecting and configuring light sources 310 and / or designing laser delivery systems for vehicle-mounted LiDAR applications.

[0069] It should be understood that the above description provides non-limiting examples of light sources 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 external cavity tunable diode lasers) that are configured to generate one or more optical signals of various wavelengths. In some examples, light source 310 includes an amplifier (e.g., a preamplifier and / or a boost amplifier), which can be a doped fiber amplifier, a solid-state bulk amplifier, and / or a semiconductor optical amplifier. The amplifier is configured to receive and amplify the optical signal at a desired gain.

[0070] Return to reference Figure 3 , the LiDAR system 300 further includes a transmitter 320. The light source 310 provides laser light (e.g., in the form of a laser beam) to the transmitter 320. The laser light provided by the light source 310 can be an amplified laser with a predetermined or controlled wavelength, pulse repetition rate, and / or power level. The transmitter 320 receives the laser light from the light source 310 and emits the laser light to the steering mechanism 340 with a low divergence. In some embodiments, the transmitter 320 may include, for example, optical components (e.g., lenses, optical fibers, mirrors, etc.) for emitting one or more laser beams into the field of view (FOV) directly or via the steering mechanism 340. Although Figure 3 The transmitter 320 and the steering mechanism 340 are illustrated as separate components, but in some embodiments, they may be combined or integrated into a system. The steering mechanism 340 will be described in more detail below.

[0071] The laser beam provided by the light source 310 may diverge when it propagates to the emitter 320. Therefore, the emitter 320 generally includes a collimating lens, which is configured to collect the divergent laser beam and produce a more parallel beam with reduced or minimal divergence. The collimated beam can then be further directed through various optical devices, such as mirrors and lenses. The collimating lens can be, for example, a single plano-convex lens or a lens array. The collimating lens can be configured to achieve any desired characteristics, such as beam diameter, divergence, numerical aperture, focal length, etc. The beam propagation ratio or beam quality factor (also referred to as M 2 Factor) is used to measure the laser beam quality. In many LiDAR applications, it is important to have good laser beam quality in the generated transmitted laser beam. 2 The factor represents the degree of change of the beam relative to the ideal Gaussian beam. Therefore, M 2 The factor reflects how well a collimated laser beam can be focused on a small spot, or how well a divergent laser beam can be collimated. Thus, the light source 310 and / or the emitter 320 can be configured to meet, for example, scanning resolution requirements while maintaining a desired M 2 factor.

[0072] One or more of the light beams provided by the transmitter 320 are scanned to the FOV by the steering mechanism 340. The steering mechanism 340 scans the light beam in multiple dimensions (e.g., in the horizontal dimension and the vertical dimension) so that the LiDAR system 300 can map the environment by generating a 3D point cloud. The horizontal dimension can be a dimension parallel to the horizon or a surface associated with the LiDAR system or vehicle (e.g., a road surface). The vertical dimension is perpendicular to the horizontal dimension (i.e., the vertical dimension forms a 90-degree angle with the horizontal dimension). The steering mechanism 340 will be described in more detail below. The laser light scanned to the FOV may be scattered or reflected by objects in the FOV. At least a portion of the scattered light or reflected light forms the return light returned to the LiDAR system 300. Figure 3 An optical receiver and light detector 330 configured to receive the return light is further illustrated. The optical receiver and light detector 330 include an optical receiver configured to collect the return light from the FOV. The optical receiver may include optics (e.g., lenses, optical fibers, mirrors, etc.) for receiving, redirecting, focusing, amplifying, and / or filtering the return light from the FOV. For example, the optical receiver typically includes a collection lens (e.g., a single plano-convex lens or a lens group) to collect the return light and / or focus the collected return light onto the light detector.

[0073] The photodetector detects the return light focused by the optical receiver and generates a current and / or voltage signal 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. An exemplary method for deriving such depth information is based on direct TOF (time of flight), which will be described in more detail below. The photodetector can be characterized by its detection sensitivity, quantum efficiency, detector bandwidth, linearity, signal-to-noise ratio (SNR), overload resistance, interference resistance, etc. Based on the application, the photodetector can be configured or customized to have any desired characteristics. For example, the optical receiver and the photodetector 330 can be configured so that the photodetector has a large dynamic range while having good linearity. The linearity of the photodetector indicates the ability of the detector to maintain a linear relationship between the input optical signal power and the detector output. A detector with good linearity can maintain a linear relationship within a large dynamic input optical signal range.

[0074] In order to achieve the desired detector characteristics, the structure of the photodetector and / or the material system of the detector can be configured or customized. Various detector structures can be used for the photodetector. For example, the photodetector structure can be a PIN-based structure having an undoped intrinsic semiconductor region (i.e., an "I" region) between the p-type semiconductor and the n-type semiconductor regions. Other photodetector structures include, for example, APD (avalanche photodiode)-based structures, PMT (photomultiplier tube)-based structures, SiPM (silicon photomultiplier tube)-based structures, SPAD (single photon avalanche diode)-based structures and / or quantum wires. For the material system used in the photodetector, Si, InGaAs and / or Si / Ge-based materials can be used. It should be understood that many other detector structures and / or material systems can be used in the optical receiver and the photodetector 330.

[0075] A photodetector (e.g., an APD-based detector) may have an internal gain so that the input signal is amplified when the output signal is generated. However, due to the internal gain of the photodetector, noise may also be amplified. Common types of noise include signal shot noise, dark current shot noise, thermal noise, and amplifier noise. In some embodiments, the optical receiver and photodetector 330 may include a preamplifier of a low noise amplifier (LNA). In some embodiments, the preamplifier may also include a transimpedance amplifier (TIA) that converts the current signal into a voltage signal. For linear detector systems, the input equivalent noise or noise equivalent power (NEP) measures the sensitivity of the photodetector to weak signals. Therefore, they can be used as an indicator of overall system performance. For example, the NEP of the photodetector specifies the power of the weakest signal that can be detected, and therefore it further specifies the maximum range of the LiDAR system. It should be understood that various photodetector optimization techniques can be used to meet the requirements of the 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, etc.). For example, in addition to or instead of direct detection using a return signal (e.g., by using ToF), coherent detection can also be used for a light detector. Coherent detection allows the amplitude and phase information of the received light to be detected by interfering the received light with a local oscillator. Coherent detection can improve detection sensitivity and noise immunity.

[0076] Figure 3The LiDAR system 300 is further illustrated to include a steering mechanism 340. As described above, the steering mechanism 340 guides the light beam from the transmitter 320 to scan the FOV in multiple dimensions. The steering mechanism is called a grating mechanism, a scanning mechanism, or simply a light scanner. Scanning the light beam in multiple directions (for example, in the horizontal direction and the vertical direction) helps the LiDAR system to draw the environment by generating an image or a 3D point cloud. The steering mechanism can be based on mechanical scanning and / or solid-state scanning. Mechanical scanning uses a rotating mirror to steer the laser beam or physically rotates the LiDAR transmitter and receiver (collectively referred to as a transceiver) to scan the laser beam. Solid-state scanning guides the laser beam to various positions through the FOV without mechanically moving any macroscopic components, such as a transceiver. The solid-state scanning mechanism includes, for example, steering based on an optical phased array and steering based on a flash LiDAR. In some embodiments, because the solid-state scanning mechanism does not physically move the macroscopic components, the steering performed by the solid-state scanning mechanism can 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 a non-scanning LiDAR system (a flash LiDAR system is an exemplary non-scanning LiDAR system).

[0077] The steering mechanism 340 can be used with a transceiver (e.g., a transmitter 320 and an optical receiver and light detector 330) to scan the FOV for generating an image or a 3D point cloud. As an example, to implement the steering mechanism 340, a two-dimensional mechanical scanner can be used with a single point or several single point transceivers. The single point transceiver transmits a single light beam or a small number of light beams (e.g., 2-8 light beams) to the steering mechanism. The two-dimensional mechanical steering mechanism includes, for example, a polygon mirror, an oscillating mirror, a rotating prism, a rotating tilt mirror, a single plane or a multi-plane mirror, or a combination thereof. In some embodiments, the steering mechanism 340 may include a non-mechanical steering mechanism, such as a solid-state steering mechanism. For example, the steering mechanism 340 can be based on a tuned wavelength of a laser combined with a refraction effect, and / or based on a reconfigurable grating / phase array. In some embodiments, the steering mechanism 340 can use a single scanning device to achieve two-dimensional scanning, or use a combination of multiple scanning devices to achieve two-dimensional scanning.

[0078] As another example, to implement the 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 a 360-degree horizontal field of view. Alternatively, a static transceiver array can be combined with a one-dimensional mechanical scanner. The one-dimensional mechanical scanner includes a polygon mirror, an oscillating mirror, a rotating prism, a rotating tilt mirror, or a combination thereof to obtain a forward horizontal field of view. A steering mechanism using a mechanical scanner can provide robustness and reliability in mass production for automotive applications.

[0079] As another example, to implement the steering mechanism 340, a two-dimensional transceiver can be used to directly generate a scanned image or a 3D point cloud. In some embodiments, a stitching or micro-displacement method can be used to increase the resolution of the scanned image or the scanned field of view. For example, using a two-dimensional transceiver, a signal generated in one direction (e.g., horizontally) and a signal generated in another direction (e.g., vertically) can be integrated, interleaved, and / or matched to generate a higher or full resolution image or 3D point cloud representing the scanned FOV.

[0080] Some embodiments of the steering mechanism 340 include one or more optical redirecting elements (e.g., mirrors or lenses) that redirect the return light signal along the receive path (e.g., by rotation, vibration, or guidance) to direct the return light signal to the optical receiver and light detector 330. The optical redirecting elements that direct the light signal along the transmit path and the receive path can be the same component (e.g., shared), separate components (e.g., dedicated), and / or a combination of shared and separate components. This means that in some cases, the transmit path and the receive path are different, although they may partially overlap (or in some cases, substantially overlap or completely overlap).

[0081] Still reference Figure 3 , the LiDAR system 300 further includes a control circuit 350. The control circuit 350 can be configured and / or programmed to control various parts of the LiDAR system 300 and / or perform signal processing. In a typical system, the control circuit 350 can be configured and / or programmed to perform one or more control operations, including, for example, controlling the light source 310 to obtain the desired laser pulse timing, pulse repetition rate, and power; controlling the steering mechanism 340 (e.g., controlling speed, direction, and / or other parameters) to scan the FOV and maintain pixel registration and / or alignment; controlling the optical receiver and light detector 330 (e.g., controlling sensitivity, noise reduction, filtering, and / or other parameters) so that it is in an optimal state; and monitoring the overall system health / functional safety status (e.g., monitoring the safety of the laser output power and / or steering mechanism operating state).

[0082] The control circuit 350 may also be configured and / or programmed to perform signal processing on the raw data generated by the optical receiver and light detector 330 to obtain distance and reflectivity information, and to package and communicate the data with the vehicle perception and planning system 220 (e.g., Figure 2For example, the control circuit 350 determines the time taken from transmitting a light pulse to receiving a corresponding return light pulse; determines when a return light pulse is not received for an transmitted light pulse; determines the direction (e.g., horizontal and / or vertical information) of the transmitted light pulse / return light pulse; determines an estimated range in a particular direction; derives reflectivity of objects in the FOV, and / or determines any other type of data related to the LiDAR system 300.

[0083] The LiDAR system 300 can be set in a vehicle that can operate in many different environments, including hot or cold weather, rough road conditions that may cause strong vibrations, high or low humidity, dusty areas, etc. Therefore, in some embodiments, the optical and / or electronic components of the LiDAR system 300 (e.g., the optical devices in the transmitter 320, the optical receiver and light detector 330, and the steering mechanism 340) are arranged and / or configured in a manner to maintain long-term mechanical and optical stability. For example, the components in the LiDAR system 300 can be fixed and sealed so that they can operate under all conditions that the vehicle may encounter. As an example, a moisture-proof coating and / or an airtight seal can be applied to the optical components of the transmitter 320, the optical receiver and light detector 330, and the steering mechanism 340 (as well as other components susceptible to moisture). As another example, a housing, a cover, a fairing, and / or a window can be used in the LiDAR system 300 to provide desired properties, such as hardness, foreign object protection level (IP), self-cleaning ability, chemical resistance, and impact resistance. Additionally, efficient and economical methods for assembling the LiDAR system 300 may be used to meet LiDAR operational requirements while keeping costs low.

[0084] Those skilled in the art should understand that Figure 3 The above description is for illustrative purposes only, and the LiDAR system may include other functional units, blocks, or segments, and may include variations or combinations of the above functional units, blocks, or segments. For example, the LiDAR system 300 may also include Figure 3 Other components not depicted, such as a power bus, power supply, LED indicators, switches, etc.

[0085] Additionally, there may be other connections between components, such as a direct connection between the light source 310 and the optical receiver and light detector 330 so that the light detector 330 can accurately measure the time from when the light source 310 emits a light pulse until the light detector 330 detects a returning light pulse. Figure 3The components are shown coupled together using communication paths 312, 314, 322, 332, 342, 352, and 362. These communication paths represent communications (bidirectional or unidirectional) between the various LiDAR system components, but are not necessarily the physical components themselves. Although communication paths can be implemented by one or more wires, buses, or optical fibers, communication paths can also be wireless channels or open-air optical paths so that no physical communication medium is present. For example, in one exemplary 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 wires that carry electrical signals. Communication paths can also include more than one of the above-mentioned types of communication media (e.g., they can include optical fibers and optical paths, or one or more optical fibers and one or more wires).

[0086] As mentioned above, some LiDAR systems use the time of flight (ToF) of light signals (e.g., light pulses) to determine the distance to objects in the light path. Figure 5A , an exemplary LiDAR system 500 includes a laser light source (e.g., a fiber laser), a steering mechanism (e.g., a system of one or more moving mirrors), and a light detector (e.g., a photodetector having one or more optical devices). The LiDAR system 500 can be implemented using, for example, the LiDAR system 300 described above. The LiDAR system 500 emits light pulses 502 along an optical path 504 determined by the steering mechanism of the LiDAR system 500. In the depicted example, the light pulses 502 generated by the laser light source are short pulses of laser light. In addition, the signal manipulation mechanism of the LiDAR system 500 is a pulse signal steering mechanism. However, it should be understood that the LiDAR system can operate by generating, emitting, and detecting non-pulsed light signals, and using techniques other than time of flight to derive the distance to objects in the surrounding environment. For example, some LiDAR systems use frequency modulated continuous waves (i.e., "FMCW"). It should also be understood that any of the techniques described herein for time-of-flight-based systems using pulsed signals can also be applied to LiDAR systems that do not use one or both of these techniques.

[0087] Return to reference Figure 5A(e.g., a time-of-flight LiDAR system using light pulses is illustrated), when light pulse 502 reaches object 506, light pulse 502 is scattered or reflected to form return light pulse 508. Return light pulse 508 may return to system 500 along optical path 510. The time from when the emitted light pulse 502 leaves the LiDAR system 500 to when the return light pulse 508 returns to the LiDAR system 500 may be measured (e.g., by a processor or other electronics within the LiDAR system, such as control circuit 350). This time of flight, combined with knowledge of the speed of light, may be used to determine the distance / distance from the LiDAR system 500 to the portion of the object 506 where the light pulse 502 was scattered or reflected.

[0088] By directing many light pulses, such as Figure 5B As depicted, LiDAR system 500 scans the external environment (e.g., by directing light pulses 502, 522, 526, 530 along optical paths 504, 524, 528, 532, respectively). Figure 5C As depicted, the LiDAR system 500 receives return light pulses 508, 542, 548 (corresponding to transmitted light pulses 502, 522, 530, respectively). The return light pulses 508, 542, and 548 are formed by scattering or reflecting the transmitted light pulses by one of the objects 506 and 514. The return light pulses 508, 542, and 548 may return to the LiDAR system 500 along optical paths 510, 544, and 546, respectively. Based on the direction of the transmitted light pulses (as determined by the LiDAR system 500) and the calculated distance from the LiDAR system 500 to the portion of the object that scattered or reflected the light pulses (e.g., the portion of objects 506 and 514), the external environment within the detectable range (e.g., the field of view between the paths 504 and 532, inclusive) may be accurately mapped or mapped (e.g., by generating a 3D point cloud or image).

[0089] If a corresponding light pulse is not received for a particular emitted light pulse, the LiDAR system 500 may determine that there is no object within the detectable range of the LiDAR system 500 (e.g., the object is outside the maximum scanning distance of the LiDAR system 500). Figure 5B In the example, light pulse 526 may not have a corresponding return light pulse (e.g. Figure 5C 528 within the detectable range of the LiDAR system 500).

[0090] exist Figure 5B, light pulses 502, 522, 526, and 530 may be emitted in any order, serially, in parallel, or based on other timings relative to each other. Additionally, although Figure 5B The emitted light pulses are depicted as being directed in one dimension or plane (e.g., the plane of the paper), but the LiDAR system 500 may also direct the emitted light pulses along other dimensions or planes. For example, the LiDAR system 500 may also direct the emitted light pulses in a direction perpendicular to the plane of the paper. Figure 5B The emitted light pulses are guided in the dimension or plane of the dimension or plane shown, thereby forming a 2-dimensional transmission of the light pulses. This 2-dimensional transmission of the light pulses can be performed point by point, line by line, once or in other ways. That is, the LiDAR system 500 can be configured to perform point scanning, line scanning, single scanning without scanning, or a combination thereof. A point cloud or image (e.g., a single horizontal line) from a 1-dimensional transmission of a light pulse can generate 2-dimensional data (e.g., (1) data from the horizontal transmission direction and (2) the range or distance to the object). Similarly, a point cloud or image from a 2-dimensional transmission of a light pulse 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 the object). Typically, a LiDAR system that performs n-dimensional transmission of light pulses generates (n+1)-dimensional data. This is because the LiDAR system can measure the depth of an object or the distance to an object, which provides an additional data dimension. Therefore, a 2D scan performed by a LiDAR system can generate a 3D point cloud that is used to map the external environment of the LiDAR system.

[0091] The density of a point cloud refers to the number of measurements (data points) per region performed by a LiDAR system. The point cloud density is related to the LiDAR scan resolution. Typically, at least for regions of interest (ROIs), a greater point cloud density is desired, and therefore a higher resolution is required. The point density 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 may be fixed. Therefore, in order to increase the density of points generated by a set of transmit-receive optical devices (or transceiver optical devices), the LiDAR system may need to generate pulses more frequently. In other words, the 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 a 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 signal may be detected in a different order from the order in which the corresponding signal was transmitted, resulting in ambiguity if the system cannot correctly associate the return signal with the transmitted signal.

[0092] To illustrate, consider an exemplary LiDAR system that can emit laser pulses with a pulse repetition rate between 500kHz and 1MHz. Based on the time it takes for the pulses to return to the LiDAR system, and to avoid confusion of return pulses from consecutive pulses in typical LiDAR designs, the maximum distance that the LiDAR system can detect can be 300 meters and 150 meters for 500kHz and 1MHz, respectively. The point density of a LiDAR system with a repetition rate of 500kHz is half that of 1MHz. Therefore, this example shows that if the system cannot correctly associate return signals that arrive out of order, increasing the repetition rate from 500kHz to 1MHz (and thus increasing the point density 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 to detect objects within different ranges. Optical and / or signal processing techniques (e.g., pulse coding techniques) are also used to associate between the emitted light signal and the returned light signal.

[0093] The various systems, devices, and methods described herein may be implemented using digital circuits or using one or more computers utilizing 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, magneto-optical disks, optical disks, and the like.

[0094] The various systems, devices, and methods described herein can be implemented using computers operating in a client-server relationship. Typically, in such a system, a client computer is located remotely from a server computer and interacts via a network. The client-server relationship can be defined and controlled by computer programs running on the respective client and server computers. Examples of client computers may include desktop computers, workstations, portable computers, cellular smartphones, tablet computers, or other types of computing devices.

[0095] The various systems, apparatuses, and methods described herein may be implemented using a computer program product tangibly embodied in an information carrier, such as in a non-transitory machine-readable storage device, for execution by a programmable processor; and the method processes and steps described herein (including Figure 12 to Figure 13At least some of the one or more steps in the above) can be implemented using one or more computer programs executable by such a processor. A computer program is a set of computer program instructions that can be used directly or indirectly in a computer to perform a specific activity or produce a specific 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.

[0096] Figure 6 A high-level block diagram of an exemplary apparatus that can be used to implement the systems, apparatus, and methods described herein is illustrated. Apparatus 600 includes a processor 610 that is operably coupled to a persistent storage device 620 and a main memory device 630. Processor 610 controls the overall operation of apparatus 600 by executing computer program instructions that define these operations. The computer program instructions may be stored in persistent storage device 620 or other computer-readable medium and loaded into main memory device 630 when execution of the computer program instructions is desired. For example, processor 610 may be used to implement one or more components and systems described herein, such as control circuit 350 ( Figure 3 As shown), vehicle perception and planning system 220 ( Figure 2 shown) and the vehicle control system 280 ( Figure 2 Therefore, Figure 12 to Figure 13 At least some of the method steps in the method may be defined by computer program instructions stored in the main memory device 630 and / or the persistent storage device 620 and controlled by the processor 610 executing the computer program instructions. For example, the computer program instructions may be implemented as a computer executable code programmed by a person skilled in the art to execute the method described in conjunction with the present invention. Figure 12 to Figure 13 6. Accordingly, by executing computer program instructions, processor 610 performs the algorithms defined by the method steps of these aforementioned figures. Apparatus 600 also includes one or more network interfaces 680 for communicating with other devices via a network. Apparatus 600 may also include one or more input / output devices 690 that enable a user to interact with apparatus 600 (e.g., a display, keyboard, mouse, speaker, buttons, etc.).

[0097] The processor 610 may include general and special purpose microprocessors and may be the sole processor of the device 600 or one of multiple processors. The processor 610 may include one or more central processing units (CPUs) and one or more graphics processing units (GPUs), which may, for example, work separately from the one or more CPUs and / or perform multitasking with the one or more CPUs to accelerate processing, for example, for various image processing applications described herein. The processor 610, the persistent storage device 620, and / or the main memory device 630 may include one or more application specific integrated circuits (ASICs) and / or one or more field programmable gate arrays (FPGAs), or may be supplemented by one or more ASICs and / or one or more FPGAs, or may be incorporated into one or more ASICs and / or one or more FPGAs.

[0098] The persistent storage device 620 and the main memory device 630 each include a tangible, non-transitory computer-readable storage medium. The persistent storage device 620 and the main memory device 630 may each include a high-speed random access memory, such as a dynamic random access memory (DRAM), a static random access memory (SRAM), a double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid-state memory devices, and may include non-volatile memory, for example, one or more disk storage devices, such as internal hard disks and removable disks, magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor storage devices (such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), compact disk read-only memory (CD-ROM), digital versatile disk read-only memory (DVD-ROM) disks, or other non-volatile solid-state storage devices.

[0099] Input / output devices 690 may include peripheral devices such as printers, scanners, display screens, etc. For example, input / output devices 690 may include a display device (such as a cathode ray tube (CRT), plasma or liquid crystal display (LCD) monitor, keyboard) for displaying information to a user, and a pointing device (such as a mouse or trackball) that a user may use to provide input to apparatus 600.

[0100] Any or all of the functionality of the systems and devices discussed herein may be performed by the processor 610, and / or incorporated into a device or system such as the LiDAR system 300. Additionally, the LiDAR system 300 and / or device 600 may utilize one or more neural networks or other deep learning techniques performed by the processor 610 or other systems or devices discussed herein.

[0101] Those skilled in the art will recognize that the actual computer or computer system implementation may have other structures and may also include other components, and Figure 6 is a high-level representation of some of the components of such a computer for purposes of illustration.

[0102] Figure 7 is a block diagram illustrating an exemplary LiDAR system 300 having an optical receiver and a light detector 330 for detecting and processing return signals, according to an embodiment. Figure 7 The LiDAR system 300 shown includes Figure 3 The system 300 shown may have the same or similar components and subsystems. Figure 7 In some embodiments, the LiDAR system 300 includes an optical receiver and a light detector 330. The optical receiver and light detector 330 include one or more optical devices (not shown) for receiving return signals, a light detector 704, an amplifier 706, an ADC 708, and a digital-to-analog converter (DAC) 702. The optical receiver is configured to collect return signals from the FOV using, for example, a collection lens. In some examples, the steering mechanism 340 directs the return signal to the optical receiver via a path 352. The return signal may include a return light pulse formed by reflecting transmitted light from an object 710 in the FOV, scattered light (e.g., light formed by a window of the LiDAR system 300), background noise, etc. The optical receiver may include one or more optical lenses, lens groups, mirrors, prisms, etc. to receive, focus, and direct the return signal. The light detector 704 detects the return signal received by the optical receiver and generates a current and / or voltage signal proportional to the incident intensity of the return signal. The light detector 704 may be an avalanche photodiode (APD). The light detector 704 may also be other types of light sensors, such as photoresistors, photovoltaic devices, phototransistors, and photodiodes. The light detector 704 may also include filters to selectively pass certain wavelengths of light. In some examples, the light detector 704 may transmit a signal to the control circuit 350 via the communication path 362 indicating when a returning light pulse is detected. Other data related to the returning light pulse (such as amplitude, power, pulse shape, and wavelength, etc.) may also be transmitted to the control circuit 350 via the communication path 362.

[0103] refer to Figure 7, the optical receiver and photodetector 330 includes a digital-to-analog converter (DAC) 702, an amplifier 706, and an analog-to-digital converter (ADC) 708. Alternatively or additionally, the optical receiver and photodetector 330 may include a time-to-digital converter (TDC) instead of an ADC. The DAC 702 may receive a digital control signal from, for example, the control circuit 350. Based on the digital control signal, the DAC 702 generates an analog signal as a bias voltage for the photodetector 704. For example, if the photodetector 704 is an APD, a bias voltage is applied to the APD to generate the electric field required for avalanche multiplication to occur. The gain of the APD varies depending on the bias voltage applied to it and the ambient temperature of the APD. At high temperatures, in order to have the same amount of gain as at lower temperatures, the DAC 702 needs to provide a higher bias voltage. Since LiDAR scanning systems typically operate in environments with large temperature changes, it is necessary to provide an appropriate bias voltage to the APD, especially when the vehicle is started. If the bias voltage is too low, the APD may not provide the desired sensitivity and gain. If the bias voltage is too high, the APD may be damaged, or its performance may be degraded due to excess noise or other factors.By proper control of the input to DAC 702, an appropriate bias voltage may be selected to place the APD in its optimal operating range.

[0104] In some examples, the analog current and / or voltage signal generated by the photodetector 704 is provided to the amplifier 706. The amplifier 706 can amplify the signal for further processing. In one embodiment, the amplifier 706 can be a transimpedance amplifier (TIA) that converts the current signal generated by the photodetector 704 into a voltage signal and amplifies the voltage signal to a level suitable for further processing. The output of the amplifier 706 is an analog signal. The analog signal is provided to the ADC 708, which is configured to convert the analog signal into a digital signal that approximates the input analog signal. The output of the ADC 708 is ADC data representing the detected return signal. In some examples, the ADC 708 converts the analog signal into a digital signal in two stages: sampling and quantization. In the sampling stage, the analog signal is measured (or sampled) at specific time intervals, and the obtained values ​​are stored as digital samples. In the quantization stage, the amplitude of each sample is rounded to the nearest digital value based on the number of bits used for representation.

[0105] After the ADC 708 converts the voltage signal from an analog signal to a digital signal, the digital signal is sent as ADC data to the control circuit 350. As described above, the control circuit 350 may include one or more processors (e.g., processor 712) and memory devices (e.g., memory 714) for processing the ADC data to determine, for example, the timing of the return light pulses for distance calculation and point cloud construction.

[0106] Dynamic calibration of the photodetector 704 may be performed by the control circuit 350 and / or other computing systems using one or more of the DAC 702, the amplifier 706, and the ADC 708. The following description of the dynamic calibration method uses the APD as an example, but it should be understood that the same or similar methods may be applied to other types of photodetectors that need to be calibrated relative to the operating temperature. During dynamic calibration of the photodetector 704 (e.g., APD), the bias voltage is swept from a low value to a target value. In some examples, the target value is a default value. In some examples, a large range bias voltage calibration is performed, particularly when the LiDAR system is started. During the large range calibration, the bias voltage may be swept from a low value (e.g., 20% of the target value) to the target value. In some examples, a small range bias voltage calibration is performed, particularly when the LiDAR system is in use. During the small range calibration, the bias voltage may be swept within a small range of the target value (e.g., from 95% to 100% of the target value). The small range calibration is effective and may not affect the performance of the LiDAR system, particularly when the LiDAR system is in use. Multi-point calibration of the APD uses multiple points within the bias voltage sweep range to calibrate the APD.

[0107] Figure 8 8 is a diagram illustrating a relationship between a reference intensity and a bias voltage of an APD according to one embodiment of the present invention. Schematic diagram 800 illustrates a relationship 810 between a reference intensity and a bias voltage of an APD at a temperature T. In some examples, temperature T is an operating temperature of a LiDAR system. Figure 8 , the horizontal axis represents the bias voltage value within the bias voltage scan range. In some examples, the bias voltage scan range is based on the breakdown voltage of the APD. The breakdown voltage of the APD is the voltage at which the avalanche multiplication process begins to occur. In some examples, the bias voltage scan range can be selected based on the maximum bias voltage of the APD. Figure 8 In the figure, the vertical axis represents the inverse of the reference intensity (1 / Io). The reference intensity is the ADC from the LiDAR system (e.g., Figure 7 The output intensity (e.g., voltage amplitude) of a signal at a reference point of the ADC 708 in the LiDAR system. The output intensity is the output signal intensity of the ADC of the LiDAR system. The terms "reference intensity" and "output intensity" may be used interchangeably in this disclosure. Figure 8As shown, when the bias voltage of the APD is scanned, a plurality of data points 850 are obtained based on a plurality of bias voltage values ​​840 within the bias voltage scanning range at temperature T. Each data point in the plurality of data points 850 represents the inverse (1 / I0) of the reference intensity at the corresponding bias voltage of the plurality of bias voltage values ​​840. In some embodiments, the multi-point calibration obtains at least seven data points at each of the plurality of different bias voltage values. In some embodiments, the multi-point calibration may be a seven-point calibration. For example, Figure 8 As shown, multiple bias voltage values ​​840 include seven bias voltage values. In some embodiments, the seven bias voltage values ​​are evenly distributed within the bias voltage scanning range. Multiple data points 850 include seven data points corresponding to each of the seven bias voltage values. The linear fit 810 at temperature T is determined using the data points 850. The linear fit 810 represents the relationship between the inverse (1 / Io) of the output intensity of the LiDAR system at temperature T and the bias voltage within the bias voltage scanning range. The bias voltage 830 at temperature T is determined by the intersection of the linear fit 810 and the horizontal axis.

[0108] Although the above multi-point calibration uses seven points as an example, it should be understood that any other number of points may be selected for calibration. The number of points may be selected based on the requirements for calibration speed and / or calibration accuracy. For example, a small number of points may be used for faster calibration, but the calibration results may not be accurate (e.g., a linear fit using a small number of points may not be good). On the other hand, a large number of points may be used for more accurate calibration, but the calibration may need to be performed over a longer period of time. It should also be understood that although Figure 8 The use of a linear fit is shown, but other types of fits may be used.

[0109] like Figure 8 As shown, the linear fit 820 represents the relationship between the inverse (1 / Io) of the output intensity of the LiDAR system at temperature T0 and the bias voltage within the bias voltage scanning range. The bias voltage 840 at temperature T0 is determined by the intersection of the linear fit 820 and the horizontal axis. For example, T0 can be a default temperature value. The default temperature value can be predetermined, or it can be a temperature used for calibration at the time of manufacture. In one embodiment, when the LiDAR system including the APD optical sensor is powered on on a vehicle, a multi-point calibration procedure is automatically initiated. Multiple data points for calibration are obtained at different bias voltages at the default temperature _xD835__xDC47_O of the APD. And the bias voltage 840 of the APD is determined by the intersection of the linear fit 820 and the horizontal axis. Therefore, the default bias voltage of the LiDAR system, which is usually set at the time of manufacture, can be updated. The final bias voltage is defined as the output voltage of the calibration or multi-point calibration process.

[0110] As described above, the bias voltage at any temperature can be determined based on scanning the bias voltage and performing a linear fit of the data points. In some embodiments, the fit quality parameter R square (or R2) is calculated based on multiple data points and the linear fit curve to monitor the fit quality. If the R square is greater than a threshold value (e.g., 0.99), which means that the fit is successful, the multi-point calibration procedure updates the bias voltage of the APD, and the bias voltage 840 will be the final bias voltage. If the R square is less than the threshold value, which means that the fit fails, the multi-point calibration procedure does not update the bias voltage of the APD, and the default value of the bias voltage of the APD will still be used as the final bias voltage. As described above, the default value of the bias voltage can be obtained by calibration during manufacturing.

[0111] In some embodiments, Figure 8 As shown, T0 can be a first temperature, and T can be a second temperature or a current temperature. The bias voltage determined at T0 can be a first bias voltage. The bias voltage determined at T using multi-point calibration can be a second bias voltage. Once the second bias voltage is determined, the first bias voltage can be updated based on the second bias voltage. In some embodiments, the determination of whether to update the first bias voltage based on the second bias voltage depends on a fit quality parameter. When the fit quality parameter meets a predetermined threshold, it can be determined that the first bias voltage is to be updated. For example, in order to determine whether to update the first bias voltage, a fit quality parameter R square is calculated based on a linear fit. R square can be a coefficient of determination defined in statistics. R square ranges from 0 to 1. A higher R square indicates a better linear fit. For example, if R square is greater than 0.99, this means that the fit is successful, and the multi-point calibration procedure updates the first bias voltage of the APD using the second bias voltage. If R square is less than 0.99, this means that the fit fails, and the multi-point calibration procedure does not update the first bias voltage of the APD.

[0112] Fig. 9 A graphical representation 900 of the results of a multi-point calibration of an APD operating at 34° C. is shown in accordance with one embodiment of the present invention. Fig. 9 In FIG. 9 , the horizontal axis of the schematic diagrams 902-916 represents the bias voltage sweep range proportional to the target bias voltage. Fig. 9 As shown, schematics 902-916 include a small scanning range from 94% to 100% of the maximum bias voltage (or target bias voltage). The vertical axis of the left schematics (i.e., schematics 902, 906, 910, and 914) represents the intensity ratio of a specific scanning channel. The LiDAR system can have multiple scanning channels, each of the multiple scanning channels including a receiver and a detector. The intensity ratio is the ratio of the output signal intensity of the LiDAR system at a specific bias voltage to the output signal intensity at the maximum bias voltage (i.e., at the target bias voltage). Reference Fig. 9, the vertical axis of the right schematic diagrams (i.e., schematic diagrams 904, 908, 912, and 916) represents the final bias voltage. The final bias voltage is defined as the output voltage of the multi-point calibration. As described above, the final bias voltage can be determined based on the intersection of the linear fit with the horizontal axis, such as Figure 8 shown.

[0113] like Fig. 9 As shown, for example, data point 918 in schematic diagram 902 shows that at a temperature of 34°C, the intensity ratio is about 45% at a bias voltage scan range of about 95% and about 80% at a scan range of about 99%. That is, when the bias voltage of the APD changes from about 95% to 99%, the intensity ratio of the output signal can change from 45% to 80%. And data point 920 in schematic diagram 904 shows that the corresponding final bias voltage changes only within about 0.1V. Therefore, because the final bias voltage does not change significantly, the bias voltage scan range may not need to change within a wide range, but can be narrowed to a smaller range. Narrowing the scan range can maintain the consistency of the final bias voltage while minimizing the degradation of detection performance. This is because the LiDAR system may not work properly when the APD is calibrated by changing the bias voltage. During calibration, the performance of the LiDAR system may degrade. Fig. 9 It is shown that in a larger scan range or a smaller scan range, the final bias voltage may not change much. Similarly, as shown in schematic diagrams 906-916, in other scan channels (e.g., channels B, C, and D), in a larger scan range or a smaller scan range, the final bias voltage does not change much.

[0114] The same conclusion applies to relatively high temperatures of 107°C ( Fig.10 ) and relatively low temperatures of -7℃( Fig.11 ). Fig.10 Illustrated is a graphical representation 1000 of the results of a multi-point APD calibration of an APD operating at 107° C., according to one embodiment of the present invention. Fig.10 In the diagram, the horizontal axis of the schematic diagrams 1002-1016 represents the bias voltage sweep range proportional to the target bias voltage. The vertical axis of the left schematic diagrams 1002, 1006, 1010 and 1014 represents the intensity ratio. The vertical axis of the right schematic diagrams 1004, 1008, 1012 and 1016 represents the final bias voltage. Similar to Fig. 9 Those shown, such as Fig.10 As shown, the final bias voltage does not change much in a larger scanning range or a smaller scanning range.

[0115] Fig.11 A graphical representation 1100 of the results of a multi-point calibration of an APD operating at -7°C is illustrated in accordance with one embodiment of the present invention. Fig.11 In the diagram, the horizontal axis of the schematic diagrams 1102-1116 represents the bias voltage sweep range proportional to the target bias voltage. The vertical axis of the left schematic diagrams 1102, 1106, 1110 and 1114 represents the intensity ratio. The vertical axis of the right schematic diagrams 1104, 1108, 1112 and 1116 represents the final bias voltage. Similar to Fig. 9 Those shown, such as Fig.11 As shown, the final bias voltage does not change much in a larger scanning range or a smaller scanning range.

[0116] A challenge with multi-point calibration is that within a short time window (e.g., a few seconds or roughly the time it takes to perform calibration), the bias voltage of the APD may drop to a value less than or equal to 95% of the scan range maximum, which may cause a degradation in the detection performance of the LiDAR system, as measured by the intensity ratio. In some embodiments, a dynamic calibration method maintains consistent calibration results over a narrow voltage scan range, thereby mitigating this degradation in the detection performance of the LiDAR system.

[0117] Fig.12 1 is a flow chart illustrating the use of an exemplary method 1200 to dynamically calibrate an avalanche photodiode (APD) of a LiDAR system. In some embodiments, the method 1200 may be performed by Figure 3 and Figure 7 The method 1200 may be performed by the LiDAR system 300 or 700 shown in the figure. The method 1200 may also be performed by other devices (e.g., a remote server, a vehicle-mounted computer of a vehicle, etc.). The method 1200 includes steps 1202-1210, which may be performed by Figure 3 and Figure 7 The method 1200 is performed by the optical receiver and light detector 330 and / or the control circuit 350. The following description uses the control circuit 350 as an example of performing the method 1200.

[0118] At step 1202, the control circuit 350 obtains a usage indication. The indication may be obtained in order to determine whether to perform calibration on a photodetector (e.g., APD) operating at a first bias voltage. For example, the first bias voltage may be a default bias voltage, or a bias voltage previously calibrated at a specific temperature. In some embodiments, the control circuit 350 obtains an indication to see whether to calibrate the first bias voltage. The indication may be generated and / or obtained based on one or more criteria. For example, one or more criteria may be based on temperature changes in the operating environment of the photodetector. In one case, if the temperature change is greater than a threshold, an indication that calibration is required may be generated. In some embodiments, one or more criteria may be based on vehicle conditions. The vehicle condition may include one or more of the following: stationary condition, movement, speed, LiDAR usage state, and LiDAR usage condition. As an example, when a vehicle is first started, calibration of the photodetector may be triggered. Calibration may also be performed when the vehicle is in a stationary condition so that the LiDAR performance degradation caused by calibration does not affect the vehicle operation. Calibration may be performed when the vehicle is at a low speed to reduce or minimize the impact of the LiDAR performance degradation caused by calibration. Calibration can also be triggered by the LiDAR usage status or usage conditions. For example, if the LiDAR is not in use, calibration can be triggered to reduce or minimize the impact of LiDAR performance degradation caused by calibration. If the LiDAR has been used for a long period of time but has not been calibrated, that is, the calibration is delayed, calibration can also be triggered. There may be some other vehicle conditions that can trigger calibration. In some embodiments, the vehicle condition is detected or reported to the control circuit 350. And an indication can be generated and obtained based on a standard associated with the vehicle condition. The indication can be used in the next step to determine whether calibration should be performed.

[0119] In some embodiments, one or more criteria for obtaining an indication may be based on a calibration strategy. For example, a calibration strategy may include one or more priority rules associated with one or more criteria. Priority rules may define certain criteria as having a higher or lower priority than other criteria. In one example, priority rules may include rules manually set by the user of the LiDAR system or the vehicle driver / system. For example, an autonomous driving system (e.g., Figure 2 The system 220 or 280 shown in FIG. 1 ) can set priority rules to delay and / or cancel all instructions to perform calibration within one hour of operation.

[0120] In some embodiments, the indication may be obtained from a vehicle control module. For example, the indication may be based on a command received from a vehicle controller (e.g., Figure 2The vehicle and / or LiDAR system may request to perform calibration when the vehicle detects that the temperature has changed to a certain extent or the LiDAR performance is degrading. The vehicle with light detector function may determine whether to perform calibration or delay calibration. In some embodiments, the indication may be obtained at fixed time intervals. For example, during operation of the LiDAR system, an indication to perform calibration may be obtained every ten minutes.

[0121] At step 1204, the control circuit 350 determines whether to perform calibration on the light detector operating at the first bias voltage based on the indication. In some embodiments, a temperature sensor is used with the light detector to detect temperature changes. When the operating temperature of the light detector changes by a threshold amount (e.g., 10° C.) during the running operation of the LiDAR system, the control circuit 350 can determine that calibration should be performed. In some embodiments, as described above, for example, when the vehicle detects that the temperature has changed to a certain extent or the LiDAR performance is degrading or has degraded, the vehicle controller can request that calibration be performed.

[0122] At step 1206, based on a determination to perform calibration, the control circuit 350 initiates a multi-point calibration of the photodetector within a bias voltage sweep range. In some embodiments, the photodetector is an APD, and the bias voltage sweep range may be selected based on a breakdown voltage of the APD. The breakdown voltage of the APD may be the minimum voltage that causes the APD to become conductive. In some embodiments, the bias voltage sweep range may be selected based on a maximum bias voltage of the photodetector. For example, the bias voltage sweep range may be selected between 95% and 100% of the maximum bias voltage of the APD. In some embodiments, the lower limit of the bias voltage sweep range may be selected based on a current intensity ratio of the photodetector. For example, as described above Fig. 9 As shown, if the minimum intensity ratio is selected to be approximately 45%, the lower limit of the bias voltage scanning range can be correspondingly selected to be approximately 95%.

[0123] At step 1208, the control circuit 350 performs a multi-point calibration. During the multi-point calibration, the control circuit 350 determines a second bias voltage corresponding to the current temperature in the operating environment of the light detector. The process of performing the multi-point calibration is described above. Figure 8 is described in more detail in and is used below Fig.13 1300 is described in more detail.

[0124] At step 1210, the control circuit 350 determines whether to update the first bias voltage based on the second bias voltage based on the multi-point calibration. In some embodiments, the control circuit 350 calculates a fit quality parameter based on the linear fit to determine whether to update the first bias voltage. When the fit quality parameter meets a predetermined threshold, the control circuit 350 determines to update the first bias voltage based on the second bias voltage. In some embodiments, the fit quality parameter may be R square (or R2), which is a coefficient of determination defined in statistics, which is calculated based on a plurality of data points and a linear fit curve to monitor the quality of the fit. If the R square is greater than a threshold value (e.g., 0.99), which means that the fit is successful, the multi-point calibration procedure updates the first bias voltage of the photodetector using the second bias voltage. If the R square is less than the threshold value, which means that the fit fails, the multi-point calibration procedure does not update the first bias voltage. And the first bias voltage (e.g., a default value of the bias voltage or a previously calibrated bias voltage) may still be used as the current bias voltage of the photodetector. More details on performing multi-point calibration are provided in conjunction with Fig.13 They are described together as follows.

[0125] Fig.13 1 is a flow chart illustrating an exemplary method 1300 for using a photodetector (eg, APD) for dynamically calibrating a LiDAR system. In some embodiments, the method 1300 may be performed by Figure 3 and Figure 7 The multi-point calibration is performed by the LiDAR system 300 or 700 including the control circuit 350 shown respectively. At step 1302, the control circuit 350 obtains multiple data points at multiple bias voltage values ​​within the bias voltage scanning range. Each of the multiple data points represents the inverse (1 / I0) of the output intensity at the corresponding bias voltage of the multiple bias voltage values. In some embodiments, the multi-point calibration includes obtaining at least seven data points at multiple different bias voltage values. For example, the multi-point calibration can be a seven-point calibration.

[0126] At step 1304, the control circuit 350 uses the plurality of data points to determine a linear fit of the inverse of the output intensity (1 / Io) of the LiDAR system relative to the bias voltage within the bias voltage sweep range. For example, to determine the linear fit, the control circuit 350 finds a line that best describes the relationship between the plurality of data points. The control circuit 350 may determine the linear fit by a line that minimizes the error or difference between the actual data points and the predicted values ​​at a plurality of different bias voltage values. At step 1306, the control circuit 350 determines a second bias voltage based on the linear fit. For example, the second bias voltage of the photodetector is determined by the intersection of the linear fit and the horizontal axis representing the bias voltage.

[0127] In some embodiments, a dynamic calibration method for a photodetector (e.g., an APD) for a LiDAR system includes obtaining a first usage indication. The method includes first determining whether to perform a first multi-point calibration of the APD based on the first indication. The first indication can be generated based on, for example, a vehicle condition indicating that the vehicle and / or LiDAR system has just been started. Based on a first determination to perform the first multi-point calibration, a multi-point calibration of the APD is performed within a bias voltage scanning range to determine a first bias voltage corresponding to a first temperature. The method also includes obtaining a second usage indication. The second usage indication can be generated based on, for example, an operating temperature change. The method includes secondly determining whether to perform a second multi-point calibration of the APD based on the second indication. Based on a second determination to perform the second multi-point calibration, a second multi-point calibration of the APD is performed within a bias voltage scanning range to determine a second bias voltage corresponding to a second temperature. The method also includes determining whether to update the first bias voltage based on the second bias voltage.

[0128] The foregoing description should be understood to be illustrative and exemplary in all respects, rather than restrictive, and the scope of the invention disclosed herein is not determined by the description, but by the claims as fully interpreted as permitted by patent law. It should be understood that the embodiments shown and described herein are merely illustrative of the principles of the invention, and that various modifications may be made by those skilled in the art without departing from the scope and spirit of the invention. Various other feature combinations may be implemented by those skilled in the art without departing from the scope and spirit of the invention.

Claims

1. A method for dynamically calibrating a light detector of a light detection and ranging (LiDAR) system, performed by a processor, the method comprising: Get instructions for use; determining, based on the indication, whether to perform calibration on the photodetector operating at a first bias voltage; initiating a multi-point calibration of the photodetector over a bias voltage sweep range based on a determination to perform the calibration, wherein the multi-point calibration includes determining a second bias voltage corresponding to a current temperature in an operating environment of the photodetector; as well as Based on the multi-point calibration, it is determined whether to update the first bias voltage based on the second bias voltage.

2. The method according to claim 1, wherein: The multi-point calibration further comprises: Obtaining a plurality of data points at a plurality of different bias voltage values ​​within the bias voltage scanning range; Determining a linear fit of an inverse of an output intensity (1 / Io) of the LiDAR system with respect to a bias voltage within the bias voltage sweep range using the plurality of data points; and The second bias voltage is determined based on the linear fit.

3. The method according to claim 2, wherein: Obtaining the plurality of data points includes obtaining at least seven data points at a plurality of different bias voltage values.

4. The method according to any one of claims 1 to 3, wherein: Determining whether to update the first bias voltage includes: calculating a fit quality parameter based on the linear fit; and When the fit quality parameter satisfies a predetermined threshold, updating the first bias voltage is determined based on the second bias voltage. 5 . The method according to claim 1 , further comprising updating the first bias voltage based on the second bias voltage.

6. The method according to any one of claims 1 to 5, wherein: The photodetector includes an avalanche photodetector (APD), and wherein the bias voltage sweep range is based on a breakdown voltage of the APD.

7. The method according to any one of claims 1 to 6, wherein: The multi-point calibration further includes selecting the bias voltage sweep range based on a maximum bias voltage of the photodetector.

8. The method according to any one of claims 1 to 7, wherein: The multi-point calibration further includes selecting a lower limit of the bias voltage sweep range based on a current intensity ratio of the photodetectors.

9. The method according to any one of claims 1 to 8, wherein: The bias voltage scanning range is between 95% and 100% of the maximum bias voltage of the photodetector.

10. The method according to any one of claims 1 to 9, wherein: The indication is obtained based on one or more criteria.

11. The method according to claim 10, wherein: The one or more criteria are based on temperature changes in the operating environment of the light detector.

12. The method according to claim 11, wherein: Determining whether to perform the calibration includes determining whether the temperature change satisfies a predetermined threshold.

13. The method according to any one of claims 10 to 11, wherein: The one or more criteria are based on vehicle conditions.

14. The method according to claim 13, wherein: The vehicle status includes one or more of the following: stationary status, moving, speed, LiDAR usage status and LiDAR usage status.

15. The method according to any one of claims 10 to 11 and claim 13, wherein The one or more criteria are based on a calibration strategy.

16. The method according to claim 15, wherein: The calibration strategy includes one or more priority rules associated with the one or more standards.

17. The method according to any one of claims 1 to 16, wherein: Obtaining the indication includes obtaining the indication from a vehicle control module.

18. The method according to any one of claims 1 to 17, wherein: Obtaining the indication includes obtaining the indication at fixed time intervals.

19. A light detection and ranging (LiDAR) system comprising an APD, the LiDAR system being configured to perform the method according to any one of claims 1 to 18.

20. A vehicle comprising a Light Detection and Ranging (LiDAR) system, the LiDAR system comprising an APD, the LiDAR system being configured to perform the method of any one of claims 1 to 18.

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