Laser trigger mode for noise reduction and isolation of signal sources

Abstract

A method for operating an optical ranging and detection (LiDAR) system is provided. The method includes emitting transmitted light pulses in two orthogonal directions according to a first timing jitter pattern. The first timing jitter pattern includes predetermined non-random timing jitter that satisfies one or more constraints. The first timing jitter pattern represents a variable time interval between consecutive transmitted light pulses. The method further includes receiving a return light pulse from outside the LiDAR system. The return light pulses include a first return light pulse and one or more adjacent return light pulses of the first return light pulse. The method further includes, for the first return light pulse and the at least one adjacent return light pulse, calculating a plurality of object distances based on the timing jitter pattern to obtain calculated object distances, and rejecting one or more uncorrelated object distances of the calculated object distances based on a filtering criterion.

Description

Cross-reference to related applications

[0001] This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 533,889, filed August 21, 2023, entitled "LASER TRIGGER PATTERNS TO REDUCE NOISE AND ISOLATE SIGNAL ORIGIN". This application relates to U.S. Patent Application Serial No. 16 / 282,163 (U.S. Patent No. 11,391,823), filed February 21, 2019, entitled "LIDAR DETECTION SYSTEMS AND METHODS WITH HIGH REPETITION RATE TO OBSERVE FAR OBJECTS". For all purposes, the contents of both applications are incorporated herein by reference in their entirety. Technical Field

[0002] This disclosure generally relates to optical detection and ranging (LiDAR) systems, and more specifically, to techniques for significantly reducing or eliminating false detections or noise caused by distance ambiguity and / or interference with returned light pulses in LiDAR systems. Background Technology

[0003] 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. Some typical scanning LiDAR systems include a light source, a light emitter, a light steering system, and a photodetector. The light source generates a light beam, which, when emitted from the LiDAR system, is guided in a specific direction by the light steering system. When the emitted beam is scattered or reflected by an object, a portion of the scattered or reflected light returns to the LiDAR system as a returned light pulse. The photodetector detects the returned light pulse. Using the difference between the time it takes to detect the returned light pulse and the time it takes for the corresponding light pulse in the beam to be emitted, the LiDAR system can determine the distance to an object based on the speed of light. This distance determination technique is called Time-of-Flight (ToF). The light steering system can guide the light beam along different paths to allow the LiDAR system to scan the surrounding environment and generate images or point clouds. Typical non-scanning LiDAR systems illuminate the entire field of view (FOV) rather than scanning it. An example of a non-scanning LiDAR system is a flash LiDAR, which can also use ToF technology to measure the distance to objects. The LiDAR system can also use techniques other than time-of-flight and scanning to measure the surrounding environment. Summary of the Invention

[0004] Existing systems enable semi-autonomous or fully autonomous vehicles. Such systems can use one or more ranging, mapping, or object detection technologies to provide sensing input to assist in the control of semi-autonomous or fully autonomous vehicles. Conventional LiDAR systems designed to observe distant objects (e.g., objects 200 meters or more away from the LiDAR system) have a relatively slow laser pulse repetition rate. This relatively slow repetition rate results in relatively low scan resolution but does not lead to many false detections (e.g., false positives). However, to improve resolution, conventional LiDAR systems may increase their laser pulse repetition rate. But increasing the laser pulse repetition rate can also lead to false detections. That is, if the correspondence between the emitted laser pulse and the received return light pulse is incorrect, a relatively distant object may be detected as an object relatively close to the LiDAR system. False detections are sometimes referred to as “distance ambiguity” or “aliasing.” False detections can also result from interfering return light signals from another interfering LiDAR system. Such false detections (e.g., false positives) can adversely affect the operation of vehicles equipped with conventional LiDAR systems.

[0005] Techniques for reducing distance ambiguity or aliasing have been developed. Such techniques introduce purely random time intervals between two consecutive transmitted laser pulses, making the time interval between laser pulses variable. While introducing purely random time intervals may help reduce distance ambiguity, problems may arise. For example, for a given transmitted laser pulse, it could have eight adjacent transmitted laser pulses (in both the vertical and horizontal scanning directions, such as...) Figure 9B As shown, and described in more detail below. Randomly introduced time intervals or time delays relative to a particular transmitted laser pulse can cause its temporal location to be very close to one of its eight neighbors. Therefore, randomly introduced time intervals or time delays may not always help identify the correspondence between a transmitted laser pulse and a particular received return pulse. False detections, false positives, or even skipped data points may still occur. Thus, purely random time intervals cannot be used to significantly reduce or eliminate distance ambiguity or aliasing. Furthermore, purely random time intervals can introduce cumulative differences in point density in the point cloud generated by the LiDAR system. This can result in one scan line in the point cloud having a higher data point density than another. Variations in data point density can cause data point shifts and / or missing data points in the point cloud data. This, in turn, can cause perception algorithms to miss objects when the physical objects actually exist at their locations within the FOV. Therefore, this could be particularly dangerous if LiDAR systems are used in autonomous vehicles.

[0006] The techniques described in this disclosure use timing jitter patterns to derive variable time intervals. A timing jitter pattern may have only non-random timing jitter, or it may have non-random timing jitter superimposed with random timing jitter. As an example of a timing jitter pattern with only non-random timing jitter, it can be configured such that all timing jitter in the pattern is limited to a jitter range, and any two adjacent timing jitters in the pattern satisfy a minimum jitter difference. Such a timing jitter pattern can significantly reduce or completely eliminate the possibility of transmitted light pulses being emitted too close to another transmitted light pulse. For timing jitter patterns that have both non-random and random timing jitter, random timing jitter is introduced in the timing jitter pattern to further reduce interference between LiDAR systems. For example, if two LiDAR systems use timing jitter patterns with the same non-random timing jitter, the two LiDAR systems may interfere with each other because the timing positions of their transmitted light pulses may be undesirably synchronized. To address this problem, random timing jitter can be superimposed on non-random timing jitter to form a unique timing jitter pattern for each interfering LiDAR system. Because the timing jitter pattern has a unique random timing jitter, interfering LiDAR systems become desynchronized, even if they are subjected to the same non-random timing jitter. Therefore, random timing jitter can be used to further improve the timing jitter pattern to eliminate potential interference between two LiDAR systems. Typically, random timing jitter is smaller compared to non-random timing jitter. Therefore, the timing jitter pattern described in this disclosure can be used in LiDAR systems to accurately detect objects while significantly reducing or eliminating distance ambiguity and interference.

[0007] In one example, a method for operating an optical ranging and detection (LiDAR) system is provided. The method includes transmitting a plurality of transmitted light pulses in two orthogonal directions according to a first timing jitter pattern. The first timing jitter pattern includes predetermined non-random timing jitter satisfying one or more constraints. The first timing jitter pattern represents a variable time interval between consecutive transmitted light pulses of the plurality of transmitted light pulses. The method further includes receiving a plurality of returned light pulses from outside the LiDAR system. The plurality of returned light pulses includes a first returned light pulse and one or more adjacent returned light pulses of the first returned light pulse. The plurality of returned light pulses are formed based at least on the plurality of transmitted light pulses. The method further includes calculating a plurality of object distances based on the timing jitter pattern for the first returned light pulse and at least one adjacent returned light pulse of the plurality of returned light pulses to obtain calculated object distances. The method further includes rejecting one or more irrelevant object distances among the calculated object distances based on a filtering criterion. The irrelevant object distances correspond to one or more transmitted light pulses that are not related to any of the returned light pulses of the plurality of returned light pulses caused by at least one of the distance ambiguity or interference returned light pulses. The method further includes providing residual object distances of at least two calculated object distances for generating point cloud data. Attached Figure Description

[0008] This application can be best understood by referring to the embodiments described below in conjunction with the accompanying drawings, in which the same parts are indicated by the same reference numerals.

[0009] Figure 1 The illustration shows one or more exemplary LiDAR systems that are set up or included in a motor vehicle.

[0010] Figure 2 This is a block diagram illustrating the interaction between an exemplary LiDAR system and several other systems, including a vehicle perception and planning system.

[0011] Figure 3 This is a block diagram illustrating an exemplary LiDAR system.

[0012] Figure 4A This is a block diagram illustrating an exemplary fiber-optic-based laser source.

[0013] Figure 4B This is a block diagram illustrating an exemplary semiconductor-based laser source.

[0014] Figures 5A to 5C The illustration shows an exemplary LiDAR system that uses pulse signals to measure the distance to an object positioned in the field of view (FOV).

[0015] Figure 6 This is a block diagram illustrating exemplary apparatus for implementing systems, devices, and methods in various embodiments.

[0016] Figure 7 The illustration depicts an illustrative scenario where a conventional LiDAR system can detect objects but cannot distinguish the distance differences between multiple objects.

[0017] Figure 8 An illustrative timing diagram based on the prior art is shown to illustrate the distance blurring or aliasing problem associated with multiple transmitted light pulses relative to the return light pulse.

[0018] Figure 9A This is a block diagram illustrating an exemplary LiDAR system according to some embodiments, which is configured to significantly reduce or eliminate distance blur using the timing jitter pattern described herein.

[0019] Figure 9B The illustration shows exemplary timing diagrams of transmitted light pulses with and without timing jitter, and possible return light pulse timing positions, according to some embodiments.

[0020] Figure 9C An example of the pattern of transmitted light pulses in a two-dimensional scan according to some embodiments is illustrated.

[0021] Figure 10A This is an exemplary timing jitter pattern with predetermined non-random timing jitter according to some embodiments.

[0022] Figure 10B The illustration shows a model based on some embodiments. Figure 10A The timing jitter pattern shown is an example of an accumulated timing jitter pattern derived from the timing jitter pattern.

[0023] Figures 10C to 10D This is another example of a timing jitter pattern that has only non-random timing jitter according to some embodiments.

[0024] Figure 11 This is a block diagram of the control circuitry of a LiDAR system, which is configured to perform object distance calculation and filtering using a timing jitter mode with non-random timing jitter and optional random timing jitter.

[0025] Figure 12A This is a timing diagram illustrating, according to some embodiments, the calculation of distances between multiple transmitted light pulses and one or more returned light pulses.

[0026] Figure 12B An exemplary formula for calculating object distance is illustrated according to some embodiments.

[0027] Figure 12C and Figure 12DThis is an exemplary method, according to some embodiments, for applying filtering criteria to reject irrelevant object distances.

[0028] Figure 13A This is a flowchart illustrating an exemplary method for operating a LiDAR system to eliminate or significantly reduce distance blur and / or interference using a timing jitter mode, according to some embodiments.

[0029] Figure 13B and Figure 13C This is a flowchart illustrating an exemplary method for calculating object distance and filtering out irrelevant object distances according to some embodiments. Detailed Implementation

[0030] To provide a more thorough understanding of the various embodiments of the present invention, numerous specific details, such as specific configurations, parameters, and examples, are set forth in the following description. However, it should be understood that this description is not intended to limit the scope of the invention, but rather to provide a better description of exemplary embodiments.

[0031] Throughout the specification and claims, unless the context clearly indicates otherwise, the following terms shall have the meaning explicitly associated herein: 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, various embodiments of the invention can be readily combined without departing from the scope or spirit of this disclosure.

[0032] As used herein, the term “or” is an inclusive “or” operator and is equivalent to the term “and / or”, unless the context clearly indicates otherwise.

[0033] The term "based on" is not exclusive and allows for the use of additional factors not described unless explicitly stated in the context.

[0034] As used herein, unless the context otherwise requires, the term "coupled to" is intended to include both direct coupling (where two coupled elements are in contact with each other) and indirect coupling (where at least one additional element is located between the two elements). 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 capable of exchanging data, the terms "coupled to" and "coupled with" are also used to indicate possible "communicable coupling" with via one or more intermediate devices. Components or devices can be optical, mechanical, and / or electrical.

[0035] 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 used only to distinguish one element from another. For example, without departing from the scope of the examples in the various descriptions, a first returning light pulse may be referred to as a second returning light pulse, and similarly, a second returning light pulse may be referred to as a first returning light pulse. Both the first and second returning light pulses can be returning light pulses, and in some cases, they can be separate and different returning light pulses.

[0036] Furthermore, throughout the specification, the meanings of “an,” “a,” and “the” include the plural, and the meaning of “in” can include both “in” and “on”.

[0037] While some 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 the 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. Furthermore, the transitional term "comprising" means having a component or element, or those components or elements. As used herein, the transitional term "comprising" is inclusive or open-ended and does not exclude additional, unlisted elements or method steps.

[0038] As used in the description herein and throughout the claims thereafter, when a system, engine, server, device, module or other computing element is described as being configured to perform or execute functions on data in memory, the meaning of “configured to” or “programmed to” is defined as one or more processors or cores of the computing element being programmed with a set of software instructions stored in the memory of the computing element to perform that set of functions on target data or data objects stored in memory.

[0039] 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, individually or in combination. It should be understood that computing devices include processors configured to execute software instructions stored on tangible, non-transitory computer-readable storage media, such as hard disk drives, FPGAs, PLAs, solid-state drives, RAM, flash memory, ROM, or any other volatile or non-volatile storage devices. These software instructions configure or program the computing device to provide roles, responsibilities, or other functions, as discussed below with respect to the disclosed apparatus. Furthermore, the disclosed technology can be embodied as a computer program product including a non-transitory computer-readable medium storing software instructions that cause a processor to perform the disclosed steps associated with the implementation of computer-based algorithms, processes, methods, or other instructions. In some embodiments, various servers, systems, databases, or interfaces may exchange data using standardized protocols or algorithms based on HTTP, HTTPS, AES, public-key-private-key exchange, web service APIs, known financial transaction protocols, or other electronic information exchange methods. Data exchange between devices can be carried out through the following: packet-switched networks, the Internet, LAN, WAN, VPN or other types of packet-switched networks; circuit-switched networks; cell-switched networks; or other types of networks.

[0040] Existing systems enable semi-autonomous or fully autonomous vehicles. Such systems can use one or more ranging, mapping, or object detection technologies to provide sensing input to assist in the control of semi-autonomous or fully autonomous vehicles. Conventional LiDAR systems designed to observe distant objects (e.g., objects 200 meters or more away from the LiDAR system) have a relatively slow laser pulse repetition rate. This relatively slow repetition rate results in relatively low scan resolution but does not lead to many false detections (e.g., false positives). However, to improve resolution, conventional LiDAR systems may increase their laser pulse repetition rate. But increasing the laser pulse repetition rate can also lead to false detections. That is, if the correspondence between the emitted laser pulse and the received return light pulse is incorrect, a relatively distant object may be detected as an object relatively close to the LiDAR system. False detections are sometimes referred to as “distance ambiguity” or “aliasing.” False detections can also result from interfering return light signals from another interfering LiDAR system. Such false detections (e.g., false positives) can adversely affect the operation of vehicles equipped with conventional LiDAR systems.

[0041] Techniques for reducing distance ambiguity or aliasing have been developed. Such techniques introduce purely random time intervals between two consecutive transmitted laser pulses, making the time interval between laser pulses variable. While introducing purely random time intervals may help reduce distance ambiguity, problems may arise. For example, for a given transmitted laser pulse, it could have eight adjacent transmitted laser pulses (in both the vertical and horizontal scanning directions, such as...) Figure 9B As shown, and described in more detail below. Randomly introduced time intervals or time delays relative to a particular transmitted laser pulse can cause its temporal location to be very close to one of its eight neighbors. Therefore, randomly introduced time intervals or time delays may not always help identify the correspondence between a transmitted laser pulse and a particular received return pulse. False detections, false positives, or even skipped data points may still occur. Thus, purely random time intervals cannot be used to significantly reduce or eliminate distance ambiguity or aliasing. Furthermore, purely random time intervals can introduce cumulative differences in point density in the point cloud generated by the LiDAR system. This can result in one scan line in the point cloud having a higher data point density than another. Variations in data point density can cause data point shifts and / or missing data points in the point cloud data. This, in turn, can cause perception algorithms to miss objects when the physical objects actually exist at their locations within the FOV. Therefore, this could be particularly dangerous if LiDAR systems are used in autonomous vehicles.

[0042] The techniques described in this disclosure use timing jitter patterns to derive variable time intervals. A timing jitter pattern may have only non-random timing jitter, or it may have non-random timing jitter superimposed with random timing jitter. As an example of a timing jitter pattern with only non-random timing jitter, it can be configured such that all timing jitter in the pattern is limited to a jitter range, and any two adjacent timing jitters in the pattern satisfy a minimum jitter difference. Such a timing jitter pattern can significantly reduce or completely eliminate the possibility of transmitted light pulses being emitted too close to another transmitted light pulse. For timing jitter patterns that have both non-random and random timing jitter, random timing jitter is introduced in the timing jitter pattern to further reduce interference between LiDAR systems. For example, if two LiDAR systems use timing jitter patterns with the same non-random timing jitter, the two LiDAR systems may interfere with each other because the timing positions of their transmitted light pulses may be undesirably synchronized. To address this problem, random timing jitter can be superimposed on non-random timing jitter to form a unique timing jitter pattern for each interfering LiDAR system. Because the timing jitter pattern has a unique random timing jitter, interfering LiDAR systems become desynchronized, even if they are subjected to the same non-random timing jitter. Therefore, random timing jitter can be used to further improve the timing jitter pattern to eliminate potential interference between two LiDAR systems. Typically, random timing jitter is smaller compared to non-random timing jitter. Therefore, the timing jitter pattern described in this disclosure can be used in LiDAR systems to accurately detect objects while significantly reducing or eliminating distance ambiguity and interference.

[0043] In one example, a method for operating an optical ranging and detection (LiDAR) system is provided. The method includes transmitting a plurality of transmitted light pulses in two orthogonal directions according to a first timing jitter pattern. The first timing jitter pattern includes predetermined non-random timing jitter satisfying one or more constraints. The first timing jitter pattern represents a variable time interval between consecutive transmitted light pulses of the plurality of transmitted light pulses. The method further includes receiving a plurality of returned light pulses from outside the LiDAR system. The plurality of returned light pulses includes a first returned light pulse and one or more adjacent returned light pulses of the first returned light pulse. The plurality of returned light pulses are formed based at least on the plurality of transmitted light pulses. The method further includes calculating a plurality of object distances based on the timing jitter pattern for the first returned light pulse and at least one adjacent returned light pulse of the plurality of returned light pulses to obtain calculated object distances. The method further includes rejecting one or more irrelevant object distances among the calculated object distances based on a filtering criterion. The irrelevant object distances correspond to one or more transmitted light pulses that are not related to any of the returned light pulses of the plurality of returned light pulses caused by at least one of the distance ambiguity or interference returned light pulses. The method further includes providing residual object distances of at least two calculated object distances for generating point cloud data.

[0044] Figure 1The illustration shows one or more exemplary LiDAR systems 110 and 120A-120I set up or included in a motor vehicle 100. The vehicle 100 can be a car, SUV, truck, train, van, bicycle, motorcycle, tricycle, bus, motorized scooter, tram, ship, boat, underwater vehicle, airplane, helicopter, unmanned aerial vehicle (UAV), spacecraft, etc. The motor vehicle 100 can be a vehicle with any level of automation. For example, the motor vehicle 100 can be a partially automated vehicle, a highly automated vehicle, a fully automated vehicle, or a driverless vehicle. A partially automated vehicle can perform some driving functions without human driver intervention. For example, a partially automated vehicle can perform blind spot monitoring, lane keeping and / or lane changing operations, automatic emergency braking, intelligent cruise control and / or traffic following, etc. Some 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 can generally perform all the operations of a partially automated vehicle, but with fewer limitations. Highly automated vehicles can also detect their own limits while operating the vehicle and, if necessary, request the driver to take over control. Fully automated vehicles can perform all vehicle operations without driver intervention, but can also detect their own limits and, if necessary, request driver intervention. Driverless vehicles can operate autonomously without any driver intervention.

[0045] 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., a flash LiDAR). A scanning-based LiDAR system scans one or more beams in one or more directions (e.g., horizontal and vertical) to detect objects in the field of view (FOV). A non-scanning-based LiDAR system emits a laser 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 a laser, illuminating the FOV simultaneously using a single light pulse or a beam of light.

[0046] LiDAR systems are commonly used sensors in at least partially automated vehicles. In one embodiment, such as... Figure 1As shown, a motor vehicle 100 may include a single LiDAR system 110 (e.g., without LiDAR systems 120A-120I) positioned at the highest point of the vehicle (e.g., on the top of the vehicle). Positioning the LiDAR system 110 on the 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 systems 110 and / or 120A-120I. Figure 1 As shown, in one embodiment, multiple LiDAR systems 110 and / or 120A-120I are attached to vehicle 100 at different locations on the vehicle. For example, LiDAR system 120A is attached to vehicle 100 at the right front corner; LiDAR system 120B is attached to vehicle 100 at the front center position; LiDAR system 120C is attached to vehicle 100 at the left front corner; LiDAR system 120D is attached to vehicle 100 at the right rearview mirror; LiDAR system 120E is attached to vehicle 100 at vehicle 100 at the left rearview mirror; LiDAR system 120F is attached to vehicle 100 at vehicle 100 at the rear center position; LiDAR system 120G is attached to vehicle 100 at the right rear corner; LiDAR system 120H is attached to vehicle 100 at vehicle 100 at the left rear corner; and / or LiDAR system 120I is attached to vehicle 100 at the center towards the rear end (e.g., the rear end of the top of the vehicle). It should be understood that one or more LiDAR systems can be distributed and attached to vehicles 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-pillar of vehicle 100 instead of the rearview mirror. As another example, LiDAR system 120B may be attached to the windshield of vehicle 100 instead of the front bumper.

[0047] In some embodiments, LiDAR systems 110 and 120A-120I are independent LiDAR systems, each with its own laser source, control electronics, transmitter, receiver, and / or steering mechanism. In other embodiments, some of LiDAR systems 110 and 120A-120I may share one or more components, thereby forming 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 located at the center of vehicle 100 or anywhere else) includes a light source, transmitter, and photodetector, but no steering mechanism. System 110 may distribute transmitted light to each of systems 120A-120I. The transmitted light may be distributed via optical fiber. Optical connectors may be used to couple optical fiber to each of systems 110 and 120A-120I. In some examples, one or more of systems 120A-120I include a steering mechanism, but no light source, transmitter, or photodetector. 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. Embodiments of the light source, emitter, steering mechanism, and photodetector will be described in more detail below. Via the steering mechanism, one or more systems in systems 120A-120I scan light into one or more corresponding fields of view (FOVs) and receive the corresponding return light. The return light is formed by the scattering or reflection of transmitted light by one or more objects in the FOV. Systems 120A-120I may also include collecting lenses and / or other optics to focus and / or guide the return light into an optical fiber, which delivers the received return light to system 110. System 110 includes one or more photodetectors for detecting the received return light. In some examples, system 110 is located inside a vehicle, thus placing it in a temperature-controlled environment, while one or more systems 120A-120I may be at least partially exposed to the external environment.

[0048] Figure 2 This is a block diagram 200 illustrating the interaction between an onboard LiDAR system 210 and several other systems, including a vehicle perception and planning system 220. The LiDAR system 210 can be mounted on or integrated into a vehicle. The LiDAR system 210 includes sensors that scan the surrounding environment with laser light to measure the distance, angle, and / or velocity of objects. Based on the scattered light returning to the LiDAR system 210, it can generate sensor data (e.g., image data or 3D point cloud data) representing the perceived external environment.

[0049] LiDAR system 210 may include one or more of short-range LiDAR sensors, mid-range LiDAR sensors, and long-range LiDAR sensors. Short-range LiDAR sensors measure objects up to approximately 20-50 meters away. They can be used, for example, to monitor nearby moving objects (e.g., pedestrians crossing the street in a school zone), parking assistance applications, etc. Mid-range LiDAR sensors measure objects up to approximately 70-200 meters away. They can be used, for example, to monitor road intersections, assist merging or exiting highways, etc. Long-range LiDAR sensors measure objects located at 200 meters and above. Long-range LiDAR sensors are typically used when vehicles are traveling at high speeds (e.g., on highways), allowing the vehicle's control system only a few seconds (e.g., 6-8 seconds) to respond to any event detected by the LiDAR sensor. Figure 2 As shown, in one embodiment, LiDAR sensor data can be provided to the vehicle perception and planning system 220 via communication path 213 for further processing and control of vehicle operation.

[0050] Communication path 213 can be any wired or wireless communication link capable of transmitting data.

[0051] Still referencing Figure 2 In some embodiments, other vehicle sensors 230 are configured to provide additional sensor data, either alone or in conjunction with the LiDAR system 210. These other vehicle 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. Cameras 232 may capture images and / or video of the vehicle's external environment. Cameras 232 may capture, for example, high-definition (HD) video with millions of pixels per frame. Cameras include image sensors that facilitate the generation of monochrome or color images and videos. Color information may be important in 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. Cameras 232 may include one or more of narrow-focal-length cameras, wide-focal-length cameras, side-view cameras, infrared cameras, fisheye cameras, etc. Image and / or video data generated by cameras 232 may also be provided to the vehicle perception and planning system 220 via communication path 233 for further processing and control of vehicle operation. The communication path 233 can be any wired or wireless communication link capable of transmitting data. The camera 232 can be mounted or integrated into any location on the vehicle (e.g., rearview mirror, pillar, front grille, and / or rear bumper, etc.).

[0052] Other vehicle-mounted sensors 230 may also include a radar sensor 234. The radar sensor 234 uses radio waves to determine the distance, angle, and speed of an object. The radar sensor 234 generates electromagnetic waves in the radio or microwave spectrum. These electromagnetic waves are reflected by the object, and some of the reflected waves return to the radar sensor, providing information about the object's position and speed. The radar sensor 234 may include one or more of short-range, medium-range, and long-range radars. Short-range radar measures objects at a distance of approximately 0.1–30 meters from the radar. Short-range radar is useful for detecting objects located near vehicles (such as other vehicles, buildings, walls, pedestrians, cyclists, etc.). Short-range radar can be used for blind spot detection, lane change assistance, providing rear-end collision warnings, parking assistance, and emergency braking. Medium-range radar measures objects at a distance of approximately 30–80 meters from the radar. Long-range radar measures objects located at approximately 80–200 meters. Medium-range and / or long-range radar can be used for, for example, traffic tracking, adaptive cruise control, and / or automatic braking on highways. Sensor data generated by radar sensor 234 can 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 can be installed or integrated into any location on the vehicle (e.g., rearview mirror, pillar, front grille and / or rear bumper, etc.).

[0053] Other onboard sensors 230 may also include ultrasonic sensors 236. Ultrasonic sensors 236 use sound waves or pulses to measure objects located outside the vehicle. Sound waves generated by ultrasonic sensors 236 are emitted into the surrounding environment. At least some of the emitted waves are reflected by objects and return to ultrasonic sensors 236. Based on the returned signals, the distance to the object can be calculated. Ultrasonic sensors 236 can be used, for example, to check blind spots, identify parking spaces, and provide lane change assistance in traffic. Sensor data generated by ultrasonic sensors 236 can also be provided via communication path 233 to the vehicle perception and planning system 220 for further processing and control of vehicle operation. Ultrasonic sensors 236 can be mounted or integrated into any location on the vehicle (e.g., rearview mirrors, pillars, front grille, and / or rear bumper, etc.).

[0054] 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 the other sensors 238 may also be provided to the vehicle perception and planning system 220 via communication path 233 for further processing and control of vehicle operation. It should be understood that communication path 233 may include one or more communication links for transmitting data between the various sensors 230 and the vehicle perception and planning system 220.

[0055] In some embodiments, such as Figure 2 As shown, sensor data from other vehicle-mounted sensors 230 can be provided to the vehicle-mounted LiDAR system 210 via communication path 231. The LiDAR system 210 can process the sensor data from the other vehicle-mounted sensors 230. For example, sensor data from camera 232, radar sensor 234, ultrasonic sensor 236, and / or other sensors 238 can be correlated or fused with the sensor data from the 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 the processing results can then be transmitted back to the vehicle perception and planning system 220 and / or the LiDAR system 210).

[0056] Still referencing Figure 2 In some embodiments, sensors on other vehicles 250 are used individually or in conjunction with the LiDAR system 210 to provide additional sensor data. 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 referred to as V2V (vehicle-to-vehicle) communication. For example, as... Figure 2 As shown, sensor data generated by other vehicles 250 can be transmitted to the vehicle perception and planning system 220 and / or the 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.

[0057] Sharing sensor data facilitates better perception of the external environment of a vehicle. For example, the first vehicle may not detect a pedestrian approaching it from behind a second vehicle. The second vehicle can share sensor data related to the pedestrian with the first vehicle, allowing the first vehicle additional reaction time to avoid a collision. In some embodiments, data generated by sensors on other vehicles 250, similar to data generated by sensor 230, 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 vehicle perception and planning system 220.

[0058] In some embodiments, the intelligent infrastructure system 240 is used to provide sensor data, either alone or in conjunction with the LiDAR system 210. Certain infrastructure can be configured to communicate with vehicles to relay information, and vice versa. Communication between vehicles and infrastructure is generally referred to as V2I (vehicle-to-infrastructure) communication. For example, the intelligent infrastructure system 240 may include intelligent traffic lights that can communicate their status to approaching vehicles with messages such as "turns yellow in 5 seconds." The intelligent infrastructure system 240 may also include its own LiDAR system installed near an intersection, enabling it to transmit traffic monitoring information to vehicles. 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 from the opposite direction. In this case, the sensors of the intelligent infrastructure system 240 can provide useful data to the left-turning vehicle. This data may include, for example, traffic conditions, information about objects in the direction the vehicle is turning, traffic light status, and predictions. The sensor data generated by the intelligent 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 can include any wired or wireless communication links capable of transmitting data. For example, sensor data from the intelligent infrastructure system 240 can be transmitted to the LiDAR system 210 and correlated or fused with the 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-described 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.

[0059] Still referencing Figure 2The vehicle perception and planning system 220 receives sensor data from one or more of the LiDAR system 210, other onboard sensors 230, other vehicles 250, and / or intelligent infrastructure systems 240 via various communication paths. In some embodiments, different types of sensor data are correlated and / or fused by a 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 located at different locations on the vehicle. The sensor fusion subsystem 222 obtains sensor data from different types of sensors and uses the combined data to perceive the environment more accurately. 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 significantly affected, 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 perform better than camera 232 or LiDAR system 210. Accordingly, sensor fusion subsystem 222 can use sensor data provided by radar sensor 234 to compensate for sensor data provided by camera 232 or LiDAR system 210.

[0060] In other examples, sensor data generated by other onboard sensors 230 may have lower resolution (e.g., radar sensor data) and therefore may need to be correlated and verified by a LiDAR system 210, which typically has higher resolution. For example, radar sensor 234 may detect a manhole cover (also known as a maintenance hatch cover) as an object approaching a vehicle. Due to the low resolution of radar sensor 234, vehicle perception and planning system 220 may not be able to determine whether the object is an obstacle that the vehicle needs to avoid. Therefore, high-resolution sensor data generated by LiDAR system 210 can be used to correlate and verify that the object is a manhole cover and will not cause damage to the vehicle.

[0061] The vehicle perception and planning system 220 further includes an object classifier 223. Using raw sensor data and / or related / fused data provided by the sensor fusion subsystem 222, the object classifier 223 can use any computer vision technique to detect and classify objects and estimate their positions. 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 algorithms such as region-based convolutional neural networks (R-CNN), fast R-CNN, faster R-CNN, oriented gradient histogram (HOG), region-based fully convolutional networks (R-FCN), single-shot detectors (SSD), spatial pyramid pooling (SPP-net), and / or You Only Look Once (Yolo).

[0062] 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 raw or fused sensor data provided by radar sensor 234, camera 232, and / or LiDAR system 210, the road detection subsystem 224 can construct a 3D model of the road based on machine learning techniques (e.g., pattern recognition algorithms for lane identification). Using the 3D model of the road, the road detection subsystem 224 can identify objects (e.g., obstacles or debris) and / or markings (e.g., lane lines, turning signs, pedestrian crossing signs, etc.) on the road.

[0063] The vehicle perception and planning system 220 further includes a localization and vehicle attitude subsystem 225. Based on raw or fused sensor data, the localization and vehicle attitude subsystem 225 can determine the vehicle's position and attitude. For example, using sensor data from LiDAR system 210, camera 232, and / or GPS data, the localization and vehicle attitude subsystem 225 can determine the vehicle's precise location on the road and its six degrees of freedom (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 localization. The HD map can provide a very detailed three-dimensional computer map that accurately locates the vehicle's position. For example, using an HD map, the localization and vehicle attitude subsystem 225 can accurately determine the vehicle's current position (e.g., which lane the vehicle is currently in on the road, and how close it is to the curb or sidewalk) and predict the vehicle's future position.

[0064] The vehicle perception and planning system 220 further includes an obstacle predictor 226. Objects identified by the object classifier 223 can be stationary (e.g., lampposts, road signs) or dynamic (e.g., moving pedestrians, bicycles, another vehicle). For moving objects, predicting their movement paths or future positions is important for collision avoidance. The obstacle predictor 226 can predict obstacle trajectories and / or warn the driver or vehicle planning subsystem 228 of potential collisions. For example, if there is a high probability that the obstacle's trajectory will intersect with the vehicle's current movement path, 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, Kalman filter-based and extended Kalman filter-based models, recurrent neural network (RNN)-based models, long short-term memory (LSTM) neural network-based models, encoder-decoder RNN models, etc.

[0065] Still referencing 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 vehicle's route based on the vehicle's current location data, target location data, traffic information, etc. The driving behavior planner uses obstacle prediction results provided by obstacle predictor 226 to adjust the timing and planned movement based on how other objects might move. The motion planner determines the specific actions the vehicle needs to follow. The planning results are then transmitted to the vehicle control system 280 via vehicle interface 270. Communication can be performed via communication paths 227 and 271, which include any wired or wireless communication links capable of transmitting data.

[0066] The vehicle control system 280 controls the vehicle's steering mechanism, throttle, brakes, etc., to operate the vehicle according to a 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 a user (e.g., a driver) to, for example, overtake 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 acquire and display raw or fused sensor data, identified objects, the vehicle's position / attitude, etc. This displayed data can 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 capable of transmitting data. It should be understood that... Figure 2 The various systems, sensors, communication links, and interfaces within can be configured in any desired manner, and are not limited to... Figure 2 The configuration shown.

[0067] Figure 3 This is a block diagram illustrating an exemplary LiDAR system 300. The LiDAR system 300 can be used to implement... Figure 1 and Figure 2 The LiDAR systems 110, 120A-120I, and / or 210 are shown. In one embodiment, LiDAR system 300 includes a light source 310, a transmitter 320, an optical receiver and photodetector 330, a steering system 340, and a control circuitry system 350. These components are coupled together using communication paths 312, 314, 322, 332, 342, 352, 362, and 372. These communication paths include communication links (wired or wireless, bidirectional or unidirectional) between various LiDAR system components, but do not necessarily have to be the physical components themselves. While communication paths can be implemented by one or more wires, buses, or optical fibers, they can also be wireless channels or free-space optical paths, thus eliminating the need for a physical communication medium. For example, in one embodiment of LiDAR system 300, communication path 314 between light source 310 and 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. Furthermore, communication paths 312, 322, 342, and 362 can be implemented using one or more wires carrying electrical signals. The communication paths may also include one or more of the communication media of the types described above (for example, they may include optical fibers and free-space optical components, or include one or more optical fibers and one or more wires).

[0068] In some embodiments, LiDAR system 300 can be a coherent LiDAR system. Frequency-modulated continuous wave (FMCW) LiDAR is one example. Coherent LiDAR detects objects by mixing the reflected light from the object with light from a coherent laser emitter. Therefore, as... Figure 3 As shown, if LiDAR system 300 is a coherent LiDAR, it may include a route 372 that provides a portion of the transmitted light from transmitter 320 to optical receiver and photodetector 330. Route 372 may include one or more optical components (e.g., optical fibers, lenses, mirrors, etc.) for providing light from transmitter 320 to optical receiver and photodetector 330. The transmitted light provided by transmitter 320 may be modulated light and may be split into two parts. One part is emitted to the field of view (FOV), while the second part is sent to the optical receiver and photodetector 330 of LiDAR system 300. The second part is also referred to as light retained locally (LO) in LiDAR system 300. The transmitted light is scattered or reflected by various objects in the FOV, and at least a portion of it forms returned light. The returned light is then detected and interferes with and reconstitutes with the second part of the locally retained transmitted light. Coherent LiDAR provides a mechanism for optically sensing the range of objects and their relative velocity along the line of sight (LOS).

[0069] The LiDAR system 300 may also include Figure 3 Other components not shown include power buses, power supplies, LED indicators, and switches. Additionally, other communication connections between components may exist, such as a direct connection between the light source 310 and the optical receiver and photodetector 330, to provide a reference signal that allows for accurate measurement of the time from the emission of a light pulse to detection and the return of the light pulse.

[0070] Light source 310 outputs laser light to illuminate objects within the field of view (FOV). The laser light can be infrared light with wavelengths ranging from 700 nm to 1 mm. Light source 310 can be, for example, a semiconductor-based laser (e.g., a diode laser) and / or a fiber-based laser. Semiconductor-based lasers can be, for example, edge-emitting lasers (EELs), vertical-cavity surface-emitting lasers (VCSELs), external-cavity diode lasers, vertical-external-cavity surface-emitting lasers, distributed feedback (DFB) lasers, distributed Bragg reflector (DBR) lasers, interband cascade lasers, quantum cascade lasers, quantum well lasers, dual heterostructure lasers, etc. Fiber-based lasers are lasers 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 fiber laser is based on double-clad fiber, wherein the gain medium forms the core of the fiber surrounded by two cladding layers. Double-clad fiber allows the fiber core to be pumped with a high-power beam, thus enabling the laser source to become a high-power fiber laser source.

[0071] 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 an 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 in direct contact with the end mirror of the laser resonator. Microchip lasers are typically pumped by laser diodes (directly or using fiber) to obtain the desired output power. The microchip laser 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 may have multiple amplification stages to achieve high power gain, enabling the laser output to have high power and thus allowing the LiDAR system to have a long scan range. In some examples, the power amplifier of the light source 310 can be controlled, allowing the power gain to be varied to achieve any desired laser output power.

[0072] Figure 4A This is a block diagram illustrating an exemplary fiber-optic-based laser source 400, which includes a seed laser and one or more pumps (e.g., laser diodes) for pumping a desired output power. The fiber-optic-based laser source 400 is... Figure 3An example of light source 310 is shown. In some embodiments, the fiber-based laser source 400 includes a seed laser 402 configured to generate initial optical pulses of one or more wavelengths (e.g., infrared wavelengths such as 1550 nm), which are provided to a wavelength division multiplexer (WDM) 404 via fiber 403. The fiber-based laser source 400 further includes a pump 406 for providing laser power (e.g., different wavelengths, such as 980 nm) to the WDM 404 via fiber 405. The WDM 404 multiplexes the optical pulses provided by the seed laser 402 and the laser power provided by the pump 406 onto a single fiber 407. The output of the WDM 404 can then be provided to one or more preamplifiers 408 via fiber 407. The preamplifier 408 may be an optical amplifier that amplifies the optical signal (e.g., with a gain of about 10-30 dB). In some embodiments, the preamplifier 408 is a low-noise amplifier. The preamplifier 408 outputs to an optical combiner 410 via fiber 409. Combiner 410 combines the output laser from preamplifier 408 with laser power supplied by pump 412 via fiber optic 411. Combiner 410 can combine optical signals with the same or different wavelengths. An example of a combiner is a WDM. Combiner 410 provides the combined optical signal to boost amplifier 414, which generates an output optical pulse via fiber optic 415. Boost amplifier 414 provides further amplification of the optical signal (e.g., another 20-40 dB). The output optical pulse can then be transmitted to transmitter 320 and / or steering mechanism 340 (e.g., ...). Figure 3 (As shown). It should be understood that, Figure 4A The illustration shows an exemplary configuration of a fiber-optic laser source 400. The laser source 400 may have the following characteristics: Figure 4A One or more components shown and / or Figure 4A Many other configurations of different combinations of other components not shown (e.g., power supplies, lenses, filters, beam splitters, combiners, etc.).

[0073] In some variations, the fiber-based laser source 400 can be controlled (e.g., via control circuitry 350) to generate pulses of different amplitudes based on the fiber gain distribution of the fiber used in the fiber-based laser source 400. Communication path 312 couples the fiber-based laser source 400 to the control circuitry 350 (e.g., via control circuitry 350). Figure 3As shown, components of the fiber-based laser source 400 can be controlled by or otherwise communicate with the control circuitry system 350. Alternatively, the fiber-based laser source 400 may include its own dedicated controller. Instead of the control circuitry system 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 and controls the components of the fiber-based laser source 400 and / or communicates with them. The fiber-based laser source 400 may also include other components not shown, such as one or more power connectors, power supplies, and / or transmission lines.

[0074] Figure 4B This is a block diagram illustrating an exemplary semiconductor-based laser source 440. The semiconductor-based laser source 440 is... Figure 3 An example of light source 310 is shown. In Figure 4B In the example shown, laser source 440 is a vertical-cavity surface-emitting laser (VCSEL), which is a type of semiconductor laser diode with a unique structure that allows it to emit light vertically from the surface of the chip, rather than emitting light through the edge of the chip like an edge-emitting laser (EEL) diode. VCSELs have the advantages of high-speed operation and ease of integration into semiconductor devices. Figure 4BA cross-sectional view of an exemplary VCSEL 440 is shown. In this example, the VCSEL 440 includes a metal contact layer 442, an upper Bragg reflector 444, an active region 446, a lower Bragg reflector 448, a substrate 450, and another metal contact layer 452. In the VCSEL 440, the metal contact layers 442 and 452 are used to form electrical contacts, thereby allowing current and / or voltage to be supplied to the VCSEL 440 to generate laser light. The substrate layer 450 is a semiconductor substrate, which may be, for example, a gallium arsenide (GaAs) substrate. The VCSEL 440 uses a laser resonator that includes two distributed Bragg reflectors (DBRs) (i.e., the upper Bragg reflector 444 and the lower Bragg reflector 448), with the active region 446 sandwiched between the DBR reflectors. The active region 446 includes, for example, one or more quantum wells for laser generation. The planar DBR reflector may be a mirror with alternating high and low refractive index layers. Each layer has a thickness equivalent to one-quarter of the laser wavelength in the material, producing an intensity reflectivity of, for example, 99%. High-reflectivity mirrors in the VCSEL balance the short axial length of the gain region. In one example of the VCSEL 440, the upper DBR reflector 444 and the lower DBR reflector 448 can be doped with p-type and n-type materials, respectively, to form a diode junction. In another example, the p-type and n-type regions can be embedded between the reflectors, requiring more complex semiconductor processes to fabricate electrical contacts with the active region, but eliminating power losses in the DBR structure. The active region 446 is sandwiched between the DBR reflectors 444 and 448 of the VCSEL 440. The active region is where the laser light is generated. The active region 446 typically has a quantum well or quantum dot structure containing the gain medium responsible for optical amplification. When current is applied to the active region 446, it generates photons through stimulated emission. The distance between the upper DBR reflector 444 and the lower DBR reflector 448 defines the cavity length of the VCSEL 440. The cavity length, in turn, determines the wavelength of the emitted light and affects the laser's performance characteristics. When current is applied to the VCSEL 440, it generates light that bounces between the DBR reflectors 444 and 448 and exits the VCSEL 440 through, for example, the lower DBR reflector 448, thereby producing a highly coherent and vertically emitted laser beam 454. The VCSEL 440 can provide improved beam quality, low threshold current, and the ability to produce single-mode or multimode output.

[0075] In some variations, VCSEL 440 can be controlled (e.g., via control circuitry 350) to generate pulses of varying amplitudes. Communication path 312 couples VCSEL 440 to control circuitry 350 (e.g., via control circuitry 350). Figure 3As shown, components of the VCSEL 440 can be controlled by or otherwise communicate with the control circuitry system 350. Alternatively, the VCSEL 440 may include its own dedicated controller. Instead of the control circuitry system 350 communicating directly with the components of the VCSEL 400, the dedicated controller of the VCSEL 440 communicates with and controls the components of the VCSEL 440 and / or communicates with it. The VCSEL 440 may also include other components not shown, such as one or more power connectors, power supplies, and / or power lines.

[0076] The VCSEL 440 can be used to generate laser pulses or continuous wave (CW) lasers. To generate laser pulses, the control circuitry 350 modulates the current supplied to the VCSEL 440. Laser pulses can be generated by rapidly switching the power supply current on and off. The pulse duration, repetition rate, and shape can be controlled by adjusting the modulation parameters. As another example, the VCSEL 440 can also be a mode-locked VCSEL, which uses a combination of current modulation and optical feedback to obtain ultrashort pulses. Mode-locked VCSELs can also be controlled to synchronize the phase of the laser mode to produce very short and high-intensity pulses. As another example, the VCSEL 440 can use Q-switching technology, which includes an optical switch in the laser cavity that temporarily blocks laser action and allows energy to accumulate in the cavity. When the switch is open, a high-intensity pulse is emitted. As another example, the VCSEL 440 can also have external modulation performed by an external modulator (not shown), such as an electro-optic or acousto-optic modulator. External modulation can be used in conjunction with the VCSEL itself to produce pulse output. The external modulator can be used to control the pulse duration and repetition rate. The type of VCSEL used as at least a part of the light source 310 depends on the application and the required pulse characteristics, such as pulse duration, repetition rate and peak power.

[0077] refer to Figure 3Typical operating wavelengths of the light source 310 include, for example, approximately 850 nm, approximately 905 nm, approximately 940 nm, approximately 1064 nm, and approximately 1550 nm. For laser safety, the maximum usable laser power is capped by regulations set by the U.S. Food and Drug Administration (FDA). The optical power limit at 1550 nm is significantly higher than the power limits at the other wavelengths mentioned above. Furthermore, at 1550 nm, optical power loss in the fiber is very low. These characteristics of the 1550 nm wavelength make it more advantageous for long-range 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. Pulse widths can range from nanoseconds to picoseconds. Average power is the product of pulse energy and pulse repetition rate (PRR). As described in more detail below, PRR represents the frequency of the pulsed laser. Generally, the smaller the time interval between pulses, the higher the PRR. PRR typically corresponds to the maximum range that a 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 range. Wall insertion efficiency (WPE) is another factor for evaluating total power consumption and can be a useful metric for assessing laser efficiency. For example, as... Figure 1 As shown, multiple LiDAR systems can be attached to vehicles, which can be electric vehicles or vehicles with limited fuel or battery power. Therefore, high WPE and intelligent methods of using laser power are often important considerations when selecting and configuring the light source 310 and / or designing laser delivery systems for vehicle-mounted LiDAR applications.

[0078] It should be understood that the above description provides a non-limiting example of light source 310. Light source 310 can be configured to include many other types of light sources (e.g., laser diodes, short-cavity fiber lasers, solid-state lasers, and / or external-cavity tunable diode lasers) configured to generate one or more optical signals of various wavelengths. In some examples, light source 310 includes amplifiers (e.g., preamplifiers and / or boost amplifiers), which can be doped fiber amplifiers, solid-state amplifiers, and / or semiconductor optical amplifiers. The amplifiers are configured to receive and amplify the optical signals at a desired gain.

[0079] Return to reference Figure 3The LiDAR system 300 further includes a transmitter 320. A light source 310 supplies laser light (e.g., in the form of a laser beam) to the transmitter 320. The laser light supplied by the light source 310 may 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 transmits it to a steering mechanism 340 with low divergence. In some embodiments, the transmitter 320 may include, for example, optical components (e.g., lenses, optical fibers, mirrors, etc.) for transmitting one or more laser beams directly or via the steering mechanism 340 to the field of view (FOV). 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.

[0080] The laser beam supplied by light source 310 may diverge as it propagates to emitter 320. Therefore, emitter 320 typically includes a collimating lens or lens group configured to collect the diverging laser beam and produce a more parallel beam with reduced or minimal divergence. The collimated beam can then be further guided through various optics, such as mirrors and lenses. The collimating lens can be, for example, a single plano-convex lens or a lens group. 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 known as the M² factor), is used to measure the quality of the laser beam. In many LiDAR applications, good laser beam quality is important in the generated emitted laser beam. The M² factor represents the degree of variation of the beam relative to an ideal Gaussian beam. Therefore, the M² factor reflects how well a collimated laser beam can be focused on a small point, or how well a diverging laser beam can be collimated. Therefore, the light source 310 and / or emitter 320 can be configured to meet, for example, scanning resolution requirements while maintaining the desired M2 factor.

[0081] One or more beams of light provided by transmitter 320 are scanned onto the field of view (FOV) by steering mechanism 340. Steering mechanism 340 scans the beams in multiple dimensions (e.g., horizontal and vertical) to allow LiDAR system 300 to map the environment by generating a 3D point cloud. The horizontal dimension may be 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). Steering mechanism 340 will be described in more detail below. The laser light scanned onto the FOV may be scattered or reflected by objects within the FOV. At least a portion of the scattered or reflected light forms a return beam that returns to LiDAR system 300. Figure 3Further illustration shows an optical receiver and photodetector 330 configured to receive returned light. The optical receiver and photodetector 330 include an optical receiver configured to collect returned light from the field of view (FOV). The optical receiver may include optics (e.g., lenses, optical fibers, mirrors, etc.) for receiving, redirecting, focusing, amplifying, and / or filtering the returned light from the FOV. For example, the optical receiver typically includes a collecting lens (e.g., a single plano-convex lens or a group of lenses) to collect the returned light and / or focus the collected returned light onto the photodetector.

[0082] A photodetector detects the returned light focused by an optical receiver and generates a current and / or voltage signal proportional to the incident intensity of the returned light. Based on such current and / or voltage signals, depth information of the object within the field of view (FOV) can be derived. An exemplary method for deriving this depth information is based on direct time-of-flight (TOF), which will be described in more detail below. A photodetector can be characterized by its detection sensitivity, quantum efficiency, detector bandwidth, linearity, signal-to-noise ratio (SNR), overload immunity, interference immunity, etc. Depending on the application, a photodetector can be configured or customized to have any desired characteristics. For example, the optical receiver and photodetector 330 can be configured such that the photodetector has a large dynamic range while maintaining good linearity. Photodetector linearity indicates the detector's ability 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 over a large dynamic range of input optical signals.

[0083] To achieve the desired detector characteristics, the structure and / or material system of the photodetector can be configured or customized. Various detector structures can be used for photodetectors. For example, a photodetector structure can be a PIN-based structure with an undoped intrinsic semiconductor region (i.e., the "I" region) between the p-type and 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 photodetector 330.

[0084] Photodetectors (e.g., APD-based detectors) can have internal gain, amplifying the input signal when an output signal is generated. However, noise can also be amplified due to the photodetector's internal gain. Common noise types 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 for a low-noise amplifier (LNA). In some embodiments, the preamplifier may also include a transimpedance amplifier (TIA) that converts a current signal into a voltage signal. For linear detector systems, input equivalent noise or noise equivalent power (NEP) measures the photodetector's sensitivity to weak signals. Therefore, they can be used as indicators of overall system performance. For example, the photodetector's NEP specifies the power of the weakest signal that can be detected, and thus it 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, coherent detection can be used in photodetectors in addition to or instead of direct detection using a returned signal (e.g., by using Time-of-Flight). Coherent detection allows the detection of the amplitude and phase information of received light by interfering the received light with a local oscillator. Coherent detection can improve detection sensitivity and noise immunity.

[0085] Figure 3 Further illustration shows the LiDAR system 300 including a steering mechanism 340. As described above, the steering mechanism 340 guides the beam from the transmitter 320 to scan the field of view (FOV) in multiple dimensions. The steering mechanism is also referred to as a grating mechanism, a scanning mechanism, or simply a light scanner. Scanning the beam in multiple directions (e.g., horizontal and vertical) facilitates the LiDAR system in mapping the environment by generating images or 3D point clouds. The steering mechanism can be based on mechanical scanning and / or solid-state scanning. Mechanical scanning uses rotating mirrors to steer the laser beam or physically rotates the LiDAR transmitter and receiver (collectively referred to as transceivers) to scan the laser beam. Solid-state scanning guides the laser beam to various locations within the FOV without mechanically moving any macroscopic components, such as transceivers. Solid-state scanning mechanisms include, for example, steering based on optical phased arrays and steering based on flash LiDAR. In some embodiments, steering performed by a solid-state scanning mechanism can be referred to as effective steering because the solid-state scanning mechanism does not physically move macroscopic components. LiDAR systems using solid-state scanning can also be referred to as non-mechanical scanning or simple non-scanning LiDAR systems (flash LiDAR systems are exemplary non-scanning LiDAR systems).

[0086] The steering mechanism 340 can be used with transceivers (e.g., transmitter 320 and optical receiver and photodetector 330) to scan the field of view (FOV) for generating images or 3D point clouds. As an example, to implement the steering mechanism 340, a 2D mechanical scanner can be used with a single-point or several single-point transceivers. The single-point transceivers transmit a single beam or a small number of beams (e.g., 2-8 beams) to the steering mechanism. 2D mechanical steering mechanisms include, for example, polygonal mirrors, oscillating mirrors, rotating prisms, rotating tilting mirrors, single-plane or multi-plane mirrors, or combinations thereof. In some embodiments, the steering mechanism 340 can include a non-mechanical steering mechanism, such as a solid-state steering mechanism. For example, the steering mechanism 340 can be based on the tuned wavelength of a laser incorporating refractive effects, and / or on a reconfigurable grating / phase array. In some embodiments, the steering mechanism 340 can implement 2D scanning using a single scanning device or by using a combination of multiple scanning devices.

[0087] As another example, to implement steering mechanism 340, a one-dimensional mechanical scanner can be used in conjunction 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. One-dimensional mechanical scanners include polygonal mirrors, oscillating mirrors, rotating prisms, rotating tilting mirrors, or combinations thereof, for obtaining a forward-looking horizontal field of view. Steering mechanisms using mechanical scanners can provide robustness and reliability in mass production for automotive applications.

[0088] As another example, to implement the steering mechanism 340, a two-dimensional transceiver can be used to directly generate scanned images or 3D point clouds. In some embodiments, stitching or micro-displacement methods can be used to improve the resolution of the scanned image or the scanned field of view. For example, using a two-dimensional transceiver, signals generated in one direction (e.g., horizontal) and signals generated in another direction (e.g., vertical) can be integrated, interleaved, and / or matched to generate a higher or full-resolution image or 3D point cloud representing the scanned FOV.

[0089] Some implementations of the redirection mechanism 340 include one or more optical redirection elements (e.g., mirrors or lenses) that redirect the returning optical signal along the receiving path (e.g., by rotation, vibration, or guidance) to direct the returning optical signal to the optical receiver and photodetector 330. The optical redirection elements that guide the optical signal along the transmission and receiving paths can be identical components (e.g., shared), separate components (e.g., dedicated), and / or a combination of shared and separate components. This means that in some cases, the transmission and receiving paths are different, although they may partially overlap (or in some cases, substantially overlap or completely overlap).

[0090] Still referencing Figure 3 The LiDAR system 300 further includes a control circuitry system 350. The control circuitry system 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 circuitry system 350 can be configured and / or programmed to perform one or more control operations, including, for example, controlling the light source 310 to obtain 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 field of view (FOV) and maintain pixel registration and / or alignment; controlling the optical receiver and photodetector 330 (e.g., controlling sensitivity, noise reduction, filtering, and / or other parameters) to optimize their operation; and monitoring the overall system health / functional safety status (e.g., monitoring the laser output power and / or the safety of the steering mechanism's operating status).

[0091] The control circuit system 350 can also be configured and / or programmed to perform signal processing on the raw data generated by the optical receiver and photodetector 330 to obtain distance and reflectivity information, and to perform data packaging and communication with the vehicle perception and planning system 220 (such as...). Figure 2 The communication (as shown) involves, for example, the control circuitry 350 determining the time taken from the transmission of a light pulse to the receipt of a corresponding return light pulse; determining when a return light pulse is not received for the transmitted light pulse; determining the direction of the transmitted / return light pulse (e.g., horizontal and / or vertical information); determining an estimated range in a specific direction; deriving the reflectivity of objects in the field of view (FOV); and / or determining any other types of data relevant to the LiDAR system 300.

[0092] The LiDAR system 300 can be incorporated into a vehicle that operates in a variety of environments, including hot or cold weather, rough road conditions that may cause severe 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 optics, optical receivers, and photodetectors 330 in the transmitter 320, and the steering mechanism 340) are positioned and / or configured to maintain long-term mechanical and optical stability. For example, components in the LiDAR system 300 can be secured and sealed so that they can operate under all conditions the vehicle may encounter. As an example, a moisture-proof coating and / or an airtight seal can be applied to the optics, optical receivers, and photodetectors 330 of the transmitter 320, and the steering mechanism 340 (as well as other components susceptible to moisture). As another example, housings, enclosures, fairings, and / or windows can be used in the LiDAR system 300 to provide desired properties such as hardness, foreign object protection rating (IP), self-cleaning capability, chemical resistance, and impact resistance. In addition, the efficient and economical method for assembling the LiDAR system 300 can be used to meet the operational requirements of LiDAR while maintaining low cost.

[0093] Those skilled in the art should understand that Figure 3 The above description is for illustrative purposes only, and a LiDAR system may include other functional units, blocks, or segments, and may include variations or combinations of these functional units, blocks, or segments. For example, LiDAR system 300 may also include Figure 3 Other components not shown include power buses, power supplies, LED indicators, and switches. Additionally, other connections between components may exist, such as direct connections between the light source 310 and the optical receiver and photodetector 330, allowing the photodetector 330 to accurately measure the time from the emission of a light pulse by the light source 310 to the detection of the returning light pulse by the photodetector 330.

[0094] Figure 3The components shown are coupled together using communication paths 312, 314, 322, 332, 342, 352, 362, and 372. These communication paths represent communication (bidirectional or unidirectional) between various LiDAR system components, but do not necessarily have to be the physical components themselves. While communication paths can be implemented by one or more wires, buses, or optical fibers, they can also be wireless channels or open-air optical paths, thus eliminating the need for a physical communication medium. For example, in an 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 carrying electrical signals. Communication paths can also include more than one of the communication media of the types described above (e.g., they can include optical fibers and optical paths, or one or more optical fibers and one or more wires).

[0095] As mentioned above, some LiDAR systems use the time-of-flight (ToF) of an optical signal (e.g., a light pulse) to determine the distance to an object in the optical path. For example, reference... Figure 5A An exemplary LiDAR system 500 includes a laser source (e.g., a fiber laser), a steering mechanism (e.g., a system with one or more moving mirrors), and a photodetector (e.g., a photodetector with one or more optics). 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 defined by the steering mechanism of the LiDAR system 500. In the depicted example, the light pulses 502 generated by the laser source are short pulses of laser light. Furthermore, the signal manipulation mechanism of the LiDAR system 500 is a pulse signal steering mechanism. However, it should be understood that LiDAR systems 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 wave (i.e., "FMCW"). It should also be understood that any 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.

[0096] Return to reference Figure 5A(For example, a time-of-flight LiDAR system using light pulses is illustrated.) When light pulse 502 reaches object 506, it is scattered or reflected to form a returning light pulse 508. The returning light pulse 508 can return to system 500 along optical path 510. The time from when the emitted light pulse 502 leaves LiDAR system 500 to when the returning light pulse 508 returns to LiDAR system 500 can be measured (e.g., via a processor or other electronic device within the LiDAR system, such as control circuitry system 350). This time-of-flight, combined with knowledge of the speed of light, can be used to determine the distance / range from LiDAR system 500 to the portion of object 506 where the light pulse 502 is scattered or reflected.

[0097] By guiding many light pulses, such as Figure 5B As depicted, the LiDAR system 500 scans the external environment (e.g., by guiding optical pulses 502, 522, 526, and 530 along optical paths 504, 524, 528, and 532, respectively). Figure 5C As depicted, the LiDAR system 500 receives returned light pulses 508, 542, and 548 (corresponding to emitted light pulses 502, 522, and 530, respectively). The returned light pulses 508, 542, and 548 are formed by scattering or reflecting the emitted light pulses by one of objects 506 and 514. The returned light pulses 508, 542, and 548 can return to the LiDAR system 500 along optical paths 510, 544, and 546, respectively. Based on the direction of the emitted light pulses (as determined by the LiDAR system 500) and the calculated distance from the LiDAR system 500 to the portion of the object scattering or reflecting the light pulses (e.g., portions of objects 506 and 514), the external environment within the detectable range (e.g., the field of view between paths 504 and 532, included) can be precisely mapped or plotted (e.g., by generating a 3D point cloud or image).

[0098] If no corresponding light pulse is received for a specific emitted light pulse, the LiDAR system 500 can determine that there is no object within its detectable range (e.g., the object is beyond the maximum scanning distance of the LiDAR system 500). For example, in Figure 5B In the middle, optical pulse 526 may not have a corresponding return optical pulse (e.g. Figure 5C As illustrated, the light pulse 526 may not generate a scattering event along its transmission path 528 within a predetermined detection range. The LiDAR system 500 or an external system (e.g., a cloud system or service) communicating with the LiDAR system 500 may interpret the lack of a returning light pulse as the absence of an object positioned along the light path 528 within the detectable range of the LiDAR system 500.

[0099] exist Figure 5B In this process, optical pulses 502, 522, 526, and 530 can be emitted in any order, serially, in parallel, or based on other timing relative to each other. Additionally, although... Figure 5B The emitted light pulse can be depicted as being guided in one dimension or one plane (e.g., the plane of paper), but the LiDAR system 500 can also guide the emitted light pulse along other dimensions or planes. For example, the LiDAR system 500 can also guide the emitted light pulse perpendicular to... Figure 5B The emitted light pulse is guided in the dimension or plane shown, thereby forming a 2D transmission of the light pulse. This 2D transmission of the light pulse can be point-by-point, line-by-line, one-time, or otherwise. That is, the LiDAR system 500 can be configured to perform point scans, line scans, single scans without scanning, or combinations thereof. Point clouds or images (e.g., a single horizontal line) from 1D transmission of the light pulse can generate 2D data (e.g., (1) data from the horizontal transmission direction and (2) the range or distance to the object). Similarly, point clouds or images from 2D transmission of the light pulse can generate 3D 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, the transmission of the light pulse is performed by... n Generation of LiDAR systems using 3D transmission ( n +1) Dimensional data. This is because LiDAR systems can measure the depth of objects or the distance to objects, providing an additional dimension of data. Therefore, 2D scans performed by a LiDAR system can generate 3D point clouds for mapping the external environment of the LiDAR system.

[0100] Point cloud density refers to the number of measurements (data points) performed by a LiDAR system for each region. Point cloud density is related to the LiDAR scan resolution. Generally, at least for the region of interest (ROI), a higher 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 can be fixed. Therefore, to increase the density of points generated by a set of transmit-receive optics (or transceiver optics), a LiDAR system may need to generate pulses more frequently. In other words, the light source in a LiDAR system can have a higher pulse repetition rate (PRR). On the other hand, by generating and transmitting pulses more frequently, the maximum 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 than the corresponding transmitted signal, resulting in ambiguity if the system cannot correctly correlate the return signal with the transmitted signal.

[0101] To illustrate, consider an exemplary LiDAR system capable of emitting laser pulses with repetition rates between 500 kHz and 1 MHz. Based on the time it takes for the pulse to return to the LiDAR system, and to avoid confusion between return pulses from continuous pulses in a typical LiDAR design, the maximum detection range of the LiDAR system could be 300 meters for 500 kHz and 150 meters for 1 MHz. The point density of a LiDAR system with a repetition rate of 500 kHz is half that of a 1 MHz system. Therefore, this example shows that increasing the repetition rate from 500 kHz to 1 MHz (and thus increasing the point density) may reduce the system's detection range if the system cannot properly correlate out-of-order arriving return signals. Various techniques are used to mitigate the trade-off between a 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 correlate the emitted and returned optical signals.

[0102] The various systems, apparatuses, and methods described herein can be implemented using digital circuit systems or using one or more computers that utilize 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 disks, internal hard disks and removable disks, magneto-optical disks, optical disks, etc.

[0103] The various systems, apparatuses, and methods described herein can be implemented using computers operating in a client-server relationship. Typically, in such a system, the client computer is located remotely from the 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, laptops, cellular smartphones, tablets, or other types of computing devices.

[0104] The various systems, apparatuses, and methods described herein can be implemented using a computer program product tangibly contained in an information carrier, such as a non-transitory machine-readable storage device, for execution by a programmable processor; and the methods, processes, and steps described herein (including...) Figures 1 to 1At least one or more steps in section 3) 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 standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0105] Figure 6 A simplified block diagram of an exemplary apparatus that can be used to implement the systems, devices, and methods described herein is illustrated. Apparatus 600 includes a processor 610 operatively coupled to persistent storage device 620 and 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 circuitry system 350 (… Figure 3 As shown), the vehicle perception and planning system 220 ( Figure 2 (as shown) and vehicle control system 280 ( Figure 2 (As shown). Therefore, Figures 1 to 1 At least some of the method steps in section 3 can be defined by computer program instructions stored in main memory device 630 and / or persistent storage device 620, and controlled by processor 610 executing the computer program instructions. For example, the computer program instructions can be implemented as computer-executable code programmed by those skilled in the art to perform the actions described herein. Figures 1 to 1 The algorithm is defined by at least some of the method steps discussed in Figure 3. Accordingly, by executing computer program instructions, processor 610 executes the algorithm defined by the method steps in the foregoing figures. Device 600 also includes one or more network interfaces 680 for communicating with other devices via a network. Device 600 may also include one or more input / output devices 690 that enable a user to interact with device 600 (e.g., a display, keyboard, mouse, speaker, buttons, etc.).

[0106] Processor 610 may include both general-purpose microprocessors and special-purpose microprocessors, and may be the sole processor of device 600 or one of multiple processors. Processor 610 may include one or more central processing units (CPUs) and one or more graphics processing units (GPUs), the GPUs of which may, for example, operate independently of one or more CPUs and / or perform multitasking with one or more CPUs to accelerate processing, for example, for the various image processing applications described herein. Processor 610, persistent storage device 620, and / or 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 be supplemented by one or more ASICs and / or one or more FPGAs, or incorporated into one or more ASICs and / or one or more FPGAs.

[0107] Persistent storage device 620 and main memory device 630 each include a tangible, non-transitory computer-readable storage medium. Persistent storage device 620 and main memory device 630 may each include high-speed random access memory, such as dynamic random access memory (DRAM), static random access memory (SRAM), double-data-rate synchronous dynamic random access memory (DDR RAM), or other random access solid-state memory devices, and may include non-volatile memory, such as one or more 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)), optical disc read-only memory (CD-ROM), digital universal optical disc read-only memory (DVD-ROM), or other non-volatile solid-state storage devices.

[0108] Input / output device 690 may include peripheral devices such as printers, scanners, displays, etc. For example, input / output device 690 may include display devices for displaying information to a user (such as cathode ray tube (CRT), plasma or liquid crystal display (LCD) monitors, keyboards) and pointing devices (such as mice or trackballs) that a user can use to provide input to device 600.

[0109] Any or all of the functions of the systems and apparatuses discussed herein may be executed by processor 610 and / or incorporated into an apparatus or system such as LiDAR system 300. Furthermore, LiDAR system 300 and / or apparatus 600 may utilize one or more neural networks or other deep learning techniques executed by processor 610 or other systems or apparatuses discussed herein.

[0110] Those skilled in the art will recognize that actual computer or computer system implementations may have other structures and may include other components, and Figure 6 This is a brief representation of some of the components of this computer for illustrative purposes.

[0111] Figure 7 The illustration depicts an illustrative scenario where a conventional LiDAR system can detect objects but cannot distinguish distance differences between multiple objects. Specifically, Figure 7 A LiDAR system 700 is shown, which may include a transmitter 704 and a receiver 706. The LiDAR system 700 is configured to detect a nearby object 730 and a distant object 740. The distant object 740 is farther from the LiDAR system 700 than the nearby object 730. During operation, the transmitter 704 first emits a transmitted light pulse T1, followed by another transmitted light pulse T2. When the transmitted light pulses T1 and T2 reach objects 730 and 740, reflected or scattered light pulses are formed as reflected light pulses R1 and R2. Specifically, as... Figure 7 As shown, a return light pulse R1 can be formed in response to a transmitted light pulse T1, and a return light pulse R2 can be formed in response to a transmitted light pulse T2. Receiver 706 can receive the return light pulse R2 before receiving the return light pulse R1, even if the transmitted light pulse T1 is emitted before the transmitted light pulse T2. This is because the return light pulse R2 is formed by a nearby object 730, which may be closer to the LiDAR system 700 than a distant object 740.

[0112] Receiving a return light pulse R2 before the return light pulse R1 can cause the system to confuse the correct correspondence between the emitted and received light pulses. For example, the LiDAR system 700 might mistake the return light pulse R2 for an earlier emitted light pulse T1, and thus incorrectly calculate the distance. This effect is called distance ambiguity or aliasing. Conventionally, to prevent or reduce distance ambiguity, the repetition rate of the transmitted light pulses is reduced so that the time interval between transmitted light pulses T1 and T2 is large enough that the return light pulse R1 always returns before the LiDAR system 700 sends the next transmitted light pulse T2. For example, this time interval is set to be no less than the round-trip time of light propagation at the maximum detection range. However, a relatively slow repetition rate results in relatively low resolution because the LiDAR system 700 does not provide a large number of data points per second. To improve resolution, a system with a high repetition rate is often required. However, increasing the repetition rate effectively limits the accuracy at which the LiDAR system 700 can detect the distance to objects. The distance to the detected object can be calculated as 0.5... c / M, where c represents the speed of light and M represents the repetition rate of the transmitted light pulse.

[0113] Figure 8 An illustrative timing diagram is shown to further illustrate distance ambiguity or aliasing problems. For example... Figure 8 As shown, the transmitted light pulse T1 at time t T1 Emission; Transmitted light pulse T2 at time t T2 Emit; and return light pulse R1 at time t R1 Reception. Conventional LiDAR systems have a fixed repetition rate, denoted by M. Therefore, the time interval between two adjacent transmitted light pulses can be calculated as 1 / M. For example, if the repetition rate is 1 MHz, the time interval is 1 µs. Based on the distance between the LiDAR system and the object, it can be calculated as 0.5. The formula for c / M indicates that a 1 MHz transmitted light pulse repetition rate produces an object distance of approximately 150 meters. If the object is located at a distance greater than 150 meters (such as 180 meters), the returned light pulse R1 is received over a time interval exceeding 1 µs (e.g., time t). T1 After approximately 1.2 µs, and at time t T2 Then approximately 0.2 µs). Thus, Figure 8 The diagram shows that two transmitted light pulses, T1 and T2, exist before the system receives the returned light pulse R1. Therefore, the LiDAR system may not be able to distinguish whether the returned light pulse R1 corresponds to the transmitted light pulse T1 or T2. Conventional systems typically assume that the returned light pulse R1 corresponds to the transmitted light pulse T2 (e.g., because relatively distant objects usually have weak returned signals). If the LiDAR system determines that the returned light pulse R1 corresponds to T2, the system can determine that the object is located approximately 30 meters from the system (i.e., (1.2–1.0 µs)). c / 2 is approximately 30 meters. In this respect, the returned light pulse R1 is considered a "ghosting" pulse or an alias pulse because an object that is actually far away is perceived as near. Recording an object as being at 30 meters instead of 180 meters can lead to unintended actions (e.g., erroneous braking by an automated car system). As defined herein, a "ghosting" object is associated with incorrect distance calculations. Referring to the example above, the returned light pulse R1 is actually at 180 meters, but the system records R1 as being at 30 meters.

[0114] Refer back Figure 7A. Distance ambiguity is one problem. Another problem relates to interference. For example, while LiDAR system 700 is transmitting, interfering LiDAR system 710 may transmit a light pulse (e.g., Ti) in the same or similar direction. Therefore, when the light pulse Ti reaches the same object 740, it forms a return light pulse Ri. The return light pulse Ri is directed at interfering LiDAR system 710, but it can also be received by the receiver of LiDAR system 700. Regarding LiDAR system 700, the return light pulse Ri is an interfering return light pulse. Figure 8 It was also shown that the interference return light pulse Ri can be in t T2 and t T3 The object distance is received at any time position between the time positions or at any other time position. However, the interfering returned light pulse Ri does not correspond to any of the transmitted light pulses T1, T2, or T3. Therefore, calculating the object distance using the returned light pulse Ri will result in false detection or noise.

[0115] As mentioned above, techniques have been developed to reduce distance blurring, aliasing, and interference with returned optical signals. Such techniques can be applied to any two consecutive transmitted light pulses (e.g., Figure 8 A purely random time interval is introduced between T1 and T2 (as shown), making the time interval between laser pulses variable. While introducing a purely random time interval may help reduce range ambiguity, it can also present problems. For example, reference... Figure 9B For a given transmitted light pulse, it has eight adjacent transmitted light pulses (in both the vertical and horizontal scanning directions). Randomly introduced time intervals or time delays in a particular transmitted light pulse can cause its temporal position to be very close to one of its eight neighbors. Therefore, randomly introduced time intervals or time delays may not always help identify the correct transmitted light pulse for a given received return light pulse, or may still lead to false detections. Thus, purely random time intervals cannot be used to significantly reduce or eliminate distance ambiguity or aliasing. Furthermore, purely random time intervals can introduce cumulative differences in point density in the point cloud generated by the LiDAR system. This can result in one scan line in the point cloud having a higher data point density than another. Variations in data point density can cause data point offsets and / or missing data points in the point cloud data. This, in turn, can cause perception algorithms to miss objects when the objects are actually located at their positions within the field of view (FOV). Therefore, this could be particularly dangerous if LiDAR systems are used in autonomous vehicles.

[0116] The systems and methods described in this disclosure use timing jitter modes, including non-random timing jitter and optional random timing jitter, to correctly and accurately reject erroneous distance calculations caused by distance ambiguity and / or interference. Figure 9AAn illustrative LiDAR system 900 according to some embodiments is shown, which is configured to significantly reduce or eliminate distance blur and interference using the timing jitter patterns described herein. System 900 may include a transmitter 910, an optical receiver and photodetector 952, a steering mechanism 945, a control circuitry system 960, and an imaging system 980. Components of LiDAR system 900 may be substantially the same as or similar to those of LiDAR system 300 described above. For illustrative and simplifying purposes, other components and systems may be present as part of LiDAR system 900, but not described herein. Figure 9A As shown in [the image]. Figure 9A In this system, transmitter 910 may include clock 912, repetition rate / time interval adjustment circuitry system 920 (hereinafter referred to as time interval adjustment circuitry system 920), control circuitry system 930, and light source 940. Clock 912 may be a system clock used as a reference clock for one or more components in system 900. The reference clock may be necessary for synchronization between transmitter 910, control circuitry system 960, optical receiver and photodetector 952, and other components of LiDAR system 900.

[0117] Still referencing Figure 9A The light source 940 emits transmitted light pulses 914, which are provided to the steering mechanism 945 for scanning the field of view (FOV) of the LiDAR system 900. The time interval adjustment circuitry 920 can control and / or adjust the repetition rate / time interval of the transmitted light pulses 914 provided by the light source 940 according to a timing jitter pattern 922. In one example, the timing jitter pattern 922 described herein is provided to or generated by the time interval adjustment circuitry 920. The timing jitter pattern can be a first timing jitter pattern including predetermined non-random timing jitter. The first timing jitter pattern is determined based on conditions such as the operational requirements of the LiDAR system. The timing jitter in the first timing jitter pattern is shaped such that it is within a jitter range and has a minimum jitter difference between any two adjacent timing jitters. In other examples, the timing jitter pattern 922 provided to or generated by the time interval adjustment circuitry 920 can be a second timing jitter pattern. The second timing jitter pattern can be a first timing jitter pattern superimposed with random timing jitter. The first timing jitter mode and the second timing jitter mode are described in more detail below.

[0118] The time interval adjustment circuit system 920 can change the repetition rate / time interval of adjacent transmitted light pulses, so that consecutive transmitted light pulses have a varying repetition rate / time interval according to the timing jitter mode 922. The time interval between consecutive transmitted light pulses emitted based on the timing jitter mode 922 is varied, but not purely random. Therefore, the timing jitter in the timing jitter mode 922 is not purely random. As mentioned above, a purely random time interval may cause transmitted light pulses to be emitted too close to adjacent light pulses, which in turn leads to false detection or misdetection.

[0119] Still referencing Figure 9A As described above, the timing jitter mode 922 is used to control the timing position of the transmitted light pulse 914. Specifically, the control circuit system 960 (e.g., with...) Figure 3 The control circuit system 350 is essentially the same as or similar to the control circuit system 920, which can adjust the timing jitter pattern 922 for each transmitted light pulse received by the time interval adjustment circuit system 920. The control circuit system 960 can cause the light source 940 to emit laser pulses according to the timing jitter pattern 922. Figure 9B The illustration shows exemplary timing diagrams of transmitted light pulses with and without timing jitter, as well as possible return light pulses and their timing positions. The timing positions of the transmitted light pulses can correspond to the laser triggering timing positions with no difference or minimal difference. (Reference) Figure 9B Timing diagram 915 illustrates the timing positions of transmitted light pulses C1, C2, C3, and C4 without timing jitter. That is, if no timing jitter is applied to the laser trigger, transmitted light pulses C1, C2, C3, and C4 are emitted at fixed time intervals. In other words, the time interval between any two consecutive transmitted light pulses is the same (using...). t express).

[0120] Figure 9B Timing diagram 925 illustrates the time position of the transmitted light pulse emitted according to the timing jitter mode described herein. As shown, for the first transmitted light pulse T1, timing jitter j1 is applied. Therefore, it is not at the nominal time position t V1 It is launched at position t, not at time position t. T1 The first transmitted light pulse T1 is emitted at point t, where t T1 =t V1 +j1. In this example, the timing jitter j1 is -21 ns. Therefore, the time position of the first transmitted light pulse T1 is t. T1 =(t V1 -21) ns. In other words, the first transmitted light pulse T1 is at the nominal time position t V1(When no timing jitter is applied, it can be emitted 21 ns before the time position of optical pulse C1 in timing diagram 915.) Next, by applying timing jitter j2, at time position t T2 The second transmitted light pulse T2 is emitted at the location. For example... Figure 9B As shown, if no timing jitter is applied, the nominal time position t used to emit the second transmitted light pulse T2 is... V2 Therefore, it should be t. v2 = (t) T1 +t), where t represents the length of the excitation period without timing jitter. The excitation period is defined by the time interval between the time positions of two consecutive transmitted light pulses or between two consecutive laser triggers. Therefore, when there is no timing jitter, as shown in timing diagram 915, the length of the excitation period is always t. The length of the excitation period is the reciprocal of the repetition rate, laser trigger rate, or pulse repetition rate. In timing diagram 925, the time position of the second transmitted light pulse T2 is therefore t. T2 = (t) V2 +j2), where t V2 This indicates the nominal time position if no timing jitter is applied during the excitation cycle. In one example, j2 is -3 ns, and therefore the time position of the second transmitted light pulse T2 is t. T2 = (t) V2 -3)ns.

[0121] Similarly, Figure 9B The third transmitted light pulse T3 is shown to be equal to (t V3 +j3) Time position t T3 The light is emitted at point t. If there is no timing jitter, then time position tV3 is the nominal time position of the third transmitted light pulse. That is, t... V3 equal to (t) T2 +t). In this example, the timing jitter j3 can be, for example, -15 ns. Therefore, the time position t T3 It is t V3 -15 ns. Similarly, in the case of (t) V4 +j4) Time position t T4 The fourth transmitted light pulse T4 is emitted at point t, where t V4 This is the nominal time position of the fourth transmitted light pulse T4 without timing jitter, and j4 is the timing jitter applied to the fourth transmitted light pulse T4. In one example, the timing jitter j4 is -9 ns. Therefore, at time position (t... V4 The fourth transmitted light pulse T4 is emitted at -9 ns.

[0122] As can be seen from timing diagram 925, the time interval between adjacent transmitted light pulses varies and is not always equal to the excitation period.t The time intervals are defined as follows. For example, the first time interval between the first transmitted light pulse T1 and the second transmitted light pulse T2 is equal to (t+j2); the second time interval between the second and third light pulses is equal to (t+j3); and the third time interval between the third and fourth light pulses is equal to (t+j4), and so on. In the above example, the first, second, and third time intervals are therefore (t-21), (t-3), and (t-15), respectively. The variable time intervals controlled by the timing jitter mode described herein can be used to reject erroneous distance calculations and thus resolve the distance ambiguity and interference described below.

[0123] Return to reference Figure 9A The LiDAR system 900's receiver may include an optical receiver and photosensor 952, a control circuitry system 360, and an imaging system 380. The optical receiver and photosensor 952 can detect returned light pulses originating from transmitted light pulses reflected or scattered from one or more objects. It can be used with... Figure 3 The optical receiver and photodetector 330 in the model are essentially the same or similar. Figure 9B Timing diagram 935 is shown, which has timing diagrams at time positions t. R1 t R2 and t R3 The three exemplary return optical pulses R1, R2, and R3 received at the location. Figure 9B It can be seen that the first returning light pulse R1 is at time position t R1 The first returned optical pulse is received at time position t. T1 and t T2 There are two preceding transmitted light pulses, T1 and T2. Time position t T1 and t T2 Corresponding to time position t R1 The light pulses emitted in the two immediately preceding excitation cycles before the first returned light pulse R1 are received. Therefore, for the first returned light pulse R1, T2 is the last transmitted light pulse, and T1 is the penultimate transmitted light pulse.

[0124] Figure 9B It also shows the time position t R2 The second returned optical pulse is received at time position t. T1 t T2 and t T3 There are three preceding transmitted light pulses T1, T2, and T3. At the three time positions, time position t... T2 and t T3 Corresponding to time position t R2The light pulses emitted in the two immediately preceding excitation cycles before the second return light pulse R2 are received. Therefore, for the second return light pulse R2, T3 is the last transmitted light pulse, T2 is the penultimate transmitted light pulse, and T1 is the third-to-last transmitted light pulse. Similarly, the third return light pulse R3 is at time position t R3 The third return optical pulse is received at the location, and can have four preceding transmitted optical pulses T1-T4. Time position t T3 and t T4 This corresponds to time position t. R3 The light pulses emitted in the two immediately preceding excitation cycles before the third return light pulse R3 are received.

[0125] Refer back Figure 9A The control circuit system 960 can receive the timing jitter pattern 922 from the time interval adjustment circuit system 920. Based on the timing jitter pattern 922, the control circuit system 960 can determine the absolute time position at which the light source 940 should excite the laser pulse, as described above. Figure 9B As described, the control circuitry system 960 can use timing jitter mode 922 to analyze the correspondence between any given returned light pulse and one or more transmitted light pulses; and reject erroneous distance calculations to provide filtered results. (Reference) Figure 9B In some examples, for time position t R1 t R2 or t R3 For any specific returned optical pulse R1, R2, or R3 received, the control circuitry 960 uses two time positions corresponding to optical pulses emitted in the two immediately preceding excitation cycles before receiving the specific returned optical pulse to calculate the object distance. This is referred to as a two-cycle calculation. In other examples, for any specific returned optical pulse, the control circuitry uses three time positions corresponding to optical pulses emitted in the two immediately preceding excitation cycles before receiving the specific returned optical pulse to calculate the object distance. This is referred to as a three-cycle calculation.

[0126] Typically, object distance calculations can use the time positions of two, three, or more transmitted light pulses (corresponding to two, three, or more excitation cycles) of a specific returned light pulse. For example, for time position t... R1 The received return optical pulse R1, time position t T1 and t T2 It can be used for object distance calculation. For time position t... R2 The received return optical pulse R2, time position t T2 and t T3It can be used for object distance calculation; and so on. If three excitation cycles are used, the time positions of the transmitted light pulses T1, T2, and T3 for the return light pulse R1 are used for object distance calculation; and the time positions of the transmitted light pulses T2, T3, and T4 for the return light pulse R2 are used for object distance calculation. The timing jitter mode described herein can provide an accurate determination of the correspondence between the return light pulse and the transmitted light pulse within at least two or three excitation cycles, and filter out any noise or erroneous object distance calculations. The timing jitter mode and the filtering details for distance calculation are described in more detail below. Return to Reference Figure 9A The control circuit system 960 can provide the filtering results to the imaging system 980. The imaging system 980 can construct 2D or 3D images or point clouds of the environment scanned by the LiDAR system 900.

[0127] Figure 9B Only a timing diagram of a one-dimensional scan is illustrated. For example, timing diagram 925 only shows a specific transmitted light pulse having two adjacent pulses in one scan dimension (e.g., the horizontal scan direction). For example, at time position t T2 The transmitted light pulse T2 at time position t T1 and t T3 There are two adjacent transmitted light pulses T1 and T3. In other words, transmitted light pulses T1 and T2 are consecutive pulses. Similarly, pulses T2 and T3 are consecutive pulses. As described above, for two-dimensional scanning (e.g., horizontal and vertical scanning), the transmitted light pulses can have eight adjacent pulses. Figure 9C An illustrative laser pulse pattern that can be provided by a LiDAR system 900 according to an embodiment is shown. The LiDAR system 900 can emit multiple transmitted light pulses in an array 990, enabling the system 900 to scan a field of view (FOV) (e.g., two-dimensional or three-dimensional space). This light pulse array 990 can be transmitted during an image capture cycle (also known as a data collection cycle) (such as a frame). Objects within the FOV can generate return light pulses, which are guided back to the system 900 that receives the return light pulses. Based on the return light pulses, the LiDAR system 900 can generate data points to construct an image or point cloud of the objects within the FOV.

[0128] This paper describes the methods and algorithms used for emitting optical pulse array 990 and how to interpret the data. The LiDAR system 900 can be designed to scan the field of view (FOV) in two dimensions (e.g., horizontal and vertical) or any two orthogonal directions. The following description uses the horizontal and vertical dimensions of the FOV as an example. Figure 9CThe diagram illustrates the vertical scanning angles of the FOV, denoted as P0, P1, P2 to PN, and the horizontal scanning angles of the FOV, represented by a sequence of dashed lines corresponding to each angle in the vertical dimension. For example, at the vertical scanning angle P0, a laser pulse is emitted at each dashed line, corresponding to pulses T10, T20, T30, T40 to TN0. Subscript 0 corresponds to angle P0. Similarly, at the vertical scanning angle P1, a laser pulse is emitted at each dashed line, corresponding to pulses T11, T21, T31, T41 to TN1. Subscript 1 corresponds to angle P1. For all vertical and horizontal scanning angles, a laser pulse is emitted at each of the indicated time positions (dashed lines). An image capture cycle can comprise a complete scan of all transmitted light pulses for all horizontal and vertical angles. For example, an image capture cycle can begin at time position T10 and be emitted at time position TN0. N The time position ends at that point. The data acquired in one image capture cycle is called a frame of data.

[0129] Similar to Figure 9B As described, the time interval between adjacent transmitted light pulses can vary in both the horizontal and vertical dimensions depending on the timing jitter pattern. Therefore, for example, in the horizontal direction, the time interval between the time positions of pulses T10 and T20 may differ from the time interval between the time positions of pulses T20 and T30; and the time interval between the time positions of pulses T20 and T30 may differ from the time interval between the time positions of pulses T30 and T40; and so on. In the vertical dimension, the time intervals may be the same or may vary between adjacent vertical angles. For example, the time interval between the time positions of pulses T10 and T20 (both at vertical angle P0) may be the same as or different from the time interval between the time positions of pulses T11 and T21 (both at vertical angle P1); and the time interval between the time positions of pulses T20 and T30 may be the same as or different from the time interval between the time positions of pulses T21 and T31; and so on. Furthermore, the time interval between the time positions of pulses T11 and T21 (both at vertical angle P1) may be the same as or different from the time interval between the time positions of pulses T12 and T22 (both at vertical angle P2). Figure 9A As shown, the control circuit system 960 receives the timing jitter mode 922, and therefore can control the time interval between adjacent transmitted light pulses according to the timing jitter mode 922.

[0130] Figure 10AThis is an exemplary timing jitter pattern 1000 according to some embodiments, which has a predetermined non-random timing jitter. The timing jitter in timing jitter pattern 1000 forms an array having multiple rows 1002, 1004, 1006, 1008, etc. Each of the multiple rows includes multiple timing jitters for applying to a corresponding transmitted light pulse emitted in the horizontal scanning direction. Furthermore, different rows include timing jitters for applying to corresponding transmitted light pulses emitted at different vertical angles in the vertical scanning direction. For example, timing jitter pattern 1000 can be used by a control circuit system 960 to emit an array 990 of light pulses in two orthogonal directions (e.g., horizontal and vertical). Timing jitter pattern 1000 has a predetermined non-random timing jitter. Each timing jitter in the timing jitter of pattern 1000 can be applied to a corresponding time position of the transmitted light pulse, in a manner similar to that described above using... Figure 9B The method described in timing diagram 925 is implemented here in a two-dimensional form. For example, refer to... Figure 9C and Figure 10A The first row 1002 of the timing jitter mode 1000 includes timing jitter that can be applied to a row of time positions in the horizontal scanning direction at a first vertical angle P0. The second row 1004 of the timing jitter mode 1000 includes timing jitter that can be applied to a row of time positions in the vertical scanning direction at a second vertical angle P1; the third row 1006 of the timing jitter mode 1000 includes timing jitter that can be applied to a row of time positions in the vertical scanning direction at a third vertical angle P2; and so on. At each vertical angle, timing jitter can be applied as described above. Figure 9B The timing jitter rows described in timing diagram 925 are the same or similar. Therefore, for example, for row 1002, a first timing jitter (i.e., -21 ns) is applied to adjust the timing position of pulse T10, which emits the first transmitted light pulse; a second timing jitter (i.e., -3 ns) is applied to adjust the timing position of pulse T20, which emits the second transmitted light pulse; and so on. For row 1004, a first timing jitter (i.e., 21 ns) is applied to adjust the timing position of pulse T11, which emits the first transmitted light pulse; a second timing jitter (i.e., 3 ns) is applied to adjust the timing position of pulse T21, which emits the second transmitted light pulse; and so on. Therefore, timing jitter mode 1000 can be applied to adjust all the timing positions used for emitting the light pulse array 990.

[0131] Because timing jitter is applied at each time position to adjust the timing of the emitted light pulse, the time interval between consecutive transmitted light pulses is variable. As described above, the time interval is therefore variable in one or both scanning directions. Therefore, the control circuit system 960 controls the emission of each light pulse at a time position determined based on a fixed pulse repetition rate (e.g., 2µs) and a corresponding timing jitter of the transmitted light pulse (e.g., -21 ns, -3 ns, -15 ns, etc.). The fixed pulse repetition rate corresponds to a fixed time interval (e.g., determined by a fixed time position between the nominal time positions for emitting consecutive light pulses)... Figure 9B The t shown represents (as used above). Figure 9B As shown in timing diagram 925, timing jitter in mode 1000 is applied to each light pulse in the light pulse at the top of a fixed time period.

[0132] Timing jitter patterns similar to pattern 1000 can be generated, such that they meet certain conditions or constraints. The time interval adjustment circuit system 920 or another circuit system can generate such timing jitter patterns. Figure 6 The device 600 shown in the diagram contains one or more components (e.g., a processor). In some examples, this can be achieved by using, for example... Figure 6 The computing device described herein uses a computing device to pre-generate a timing jitter pattern and provides it to the time interval adjustment circuit system 920.

[0133] Using timing jitter mode 1000 as an example, it can be configured to limit the timing jitter in timing jitter mode 1000 to a jitter range. For example... Figure 10A As shown, in this example, the timing jitter in mode 1000 is limited to the range of -21 ns to +21 ns. As another example of the constraints for generating timing jitter mode 1000, any two adjacent timing jitters in timing jitter mode 1000 satisfy a minimum jitter difference. For example, as... Figure 10AAs shown, the timing jitter subarray 1010 comprises nine adjacent timing jitters. Among these nine adjacent timing jitters, any two have a minimum jitter difference of at least 6 ns. For example, in row 1004 of subarray 1010, a 3 ns timing jitter has eight adjacent timing jitters, including -21 ns, -3 ns, and -15 ns at row 1002; 21 ns and 15 ns at row 1004; and -21 ns, -3 ns, and -15 ns at row 1006. The timing jitter difference between the 3 ns timing jitter at row 1004 and its eight adjacent rows is at least 6 ns. Specifically, for a timing jitter of 3 ns at line 1004, the timing jitter differences between it and the three adjacent timing jitters at line 1002 are 24 ns, 6 ns, and 18 ns; the timing jitter differences between it and the two adjacent timing jitters at line 1004 are 18 ns and 12 ns; and the timing jitter differences between it and the three adjacent timing jitters at line 1006 are 24 ns, 6 ns, and 18 ns. That is, the minimum timing jitter difference is 6 ns. The minimum timing jitter difference and limiting the timing jitter to a predetermined range can prevent or significantly reduce the possibility of a transmitted light pulse being emitted too close to one of its adjacent transmitted light pulses. Furthermore, this prevents or significantly reduces the possibility of distance ambiguity caused by the control circuit system 960 being unable to determine the correspondence between the returned light pulse and the transmitted light pulse in the first two or three excitation cycles. Therefore, for a specific returned light pulse (e.g., Figure 9B In R1), the timing jitter mode 1000 can be used to mitigate or eliminate the jitter caused by either of the two transmitted light pulses (e.g., pulses T1 and T2) in the preceding two excitation cycles or by nine adjacent transmitted light pulses (e.g., Figure 9C Distance ambiguity caused by any of T12, T22, T32, T11, T21, T31, T10, T20 and T30 shown in the figure.

[0134] The timing jitter mode can be configured based on additional constraints, enabling the control circuit system 960 to resolve distance ambiguity for more excitation cycles (e.g., three excitation cycles in the same scanning direction). Figure 10B An exemplary cumulative timing jitter pattern 1001 derived from timing jitter pattern 1000 is illustrated. According to some embodiments, pattern 1001 is a two-cycle cumulative timing jitter pattern derived from pattern 1000. Pattern 1001 is formed by accumulating timing jitter in timing jitter pattern 1000 over any two consecutive excitation cycles. As described above, the excitation cycle is defined by the time interval between the time positions of two consecutive transmitted light pulses emitted. Figure 10BAs shown, for example, in row 1012 of pattern 1001, the first timing jitter is -24 ns, which is the sum of the first and second timing jitters in row 1002 of pattern 1000 (i.e., -21–3 ns); the second timing jitter is -18 ns, which is the sum of the second and third timing jitters in row 1002 of pattern 1000 (i.e., -3–15 ns); and so on. Therefore, pattern 1001 is a two-cycle cumulative timing jitter pattern derived from pattern 1000. In pattern 1001, the timing jitter accumulates for only two cycles.

[0135] In some examples, the timing jitter in timing jitter mode 1000 is formed based on an additional constraint that any two adjacent cumulative timing jitters in the two-cycle cumulative timing jitter mode satisfy a minimum jitter difference. (Reference) Figure 10B Using subarray 1020 of mode 1001 as an example, the difference between a specific two-cycle cumulative timing jitter and any one of the eight adjacent timing jitters also satisfies the minimum difference. Using the 18 ns timing jitter at row 1014 as an example, its eight adjacent timing jitters in subarray 1020 are -24 ns, -18 ns, and -24 ns at row 1012; 24 ns and 24 ns at row 1014; and -24 ns, -18 ns, and -24 ns at row 1016. The timing jitter difference between the 18 ns timing jitter at row 1014 and its eight adjacent timing jitters is 42 ns, 36 ns, and 42 ns for row 1012; -6 ns and -6 ns for row 1014; and 42 ns, 36 ns, and 42 ns for row 1016. Therefore, the minimum timing jitter difference (absolute value) of the two-cycle cumulative timing jitter mode 1001 is also 6 ns. Correspondingly, timing jitter mode 1000 satisfies this additional constraint, namely, its two-cycle cumulative timing jitter mode satisfies the minimum timing jitter difference requirement. This is important for the control circuit system to resolve distance ambiguity over three excitation cycles. For example, since timing jitter mode 1000 satisfies this additional constraint, applying timing jitter mode 1000 to control the emission of optical pulses prevents or significantly reduces the possibility of distance ambiguity caused by the control circuit system 960's inability to determine the correspondence between the returned optical pulse and the transmitted optical pulse in the first three excitation cycles. Therefore, for a specific returned optical pulse (e.g., ...), Figure 9B R2 in the timing jitter mode 1000 can be used to mitigate or eliminate the jitter caused by any one of the three transmitted light pulses (e.g., pulses T1, T2, and T3) in the preceding two excitation cycles, or by sixteen adjacent transmitted light pulses (e.g., Figure 9CDistance blurring caused by any of T12, T22, T32, T42, T11, T21, T31, T41, T10, T20, T30, and T40 shown herein. In the examples disclosed herein, the transmitted light pulse has eight adjacent pulses. The actual number of adjacent pulses may depend on the scanning pattern. For example, if each scan line is aligned with the other scan lines, the transmitted light pulse may have eight adjacent pulses. However, if each odd-numbered scan line is offset from the even-numbered scan line by half an excitation cycle, the transmitted light pulse may have only six adjacent pulses. It should be understood that although this disclosure uses eight adjacent pulses for illustration, the methods and techniques described herein can also be applied when the number of adjacent pulses is smaller.

[0136] In some embodiments, other constraints may exist for constructing the timing jitter pattern. For example, one such constraint could be that, for each of the multiple rows in the timing jitter pattern 1000, the cumulative timing jitter over all timing jitters (e.g., all excitation cycles) in that row is approximately zero. In practice, each row of the timing jitter pattern may have hundreds or thousands of timing jitters. Furthermore, the sum of all timing jitters can be forced to be close to zero. In this way, horizontal scans between two different vertical angles will not differ too much in timing, and scan lines at different vertical angles will not deviate too much.

[0137] The timing jitter pattern can be arranged such that each row in the multiple rows of timing jitter pattern 1000 includes multiple positive timing jitters and multiple negative timing jitters. As described above, the positive and negative timing jitters are alternated in the rows, and the cumulative timing jitter is forced to be close to zero. For example, Figure 10A Row 1002 is shown to have negative timing jitter, and it may also include positive timing jitter (not shown) which has the same absolute value as the negative timing jitter but with the opposite sign. In this way, the cumulative timing jitter of all timing jitter in row 1002 can be forced to be close to zero.

[0138] In some examples, such as Figure 10A As shown, for mode 1000, corresponding timing jitter in two adjacent rows of multiple rows in the array has the same value but opposite signs. For example, row 1002 of mode 1000 includes timing jitter with a negative sign; while row 1004 of mode 1000 includes timing jitter with a positive sign. However, the corresponding timing jitter in rows 1002 and 1004 has the same absolute value. However, it should be understood that there are different ways to configure timing jitter in timing jitter modes. For example, corresponding timing jitter in two adjacent rows of multiple rows in the array can have different values ​​and opposite signs. Such a mode 1003... Figure 10CThe diagram shows that in mode 1003, the first row has all negative timing jitter, while the second row has all positive timing jitter; and the absolute values ​​of the timing jitter in the two rows are different.

[0139] Figure 10D Another exemplary timing jitter pattern 1005 is illustrated, where each row includes both negative and positive timing jitter, and the absolute values ​​of the timing jitter differ between rows. However, timing jitter patterns 1003 and 1005 also satisfy the constraints imposed on timing jitter pattern 1000. For example, for patterns 1003 and 1005, all timing jitter is configured within a jitter range such that any timing jitter cannot be too large to cause the transmitted light pulse to skip. Furthermore, the timing jitter difference between any two adjacent timing jitters in patterns 1003 and 1005 satisfies a minimum jitter difference, ensuring that two transmitted light pulses are not emitted too close to each other, resulting in further distance blurring.

[0140] The timing jitter patterns described herein can be determined based on one or more constraints such as jitter range and minimum jitter difference. In some embodiments, the jitter range and minimum jitter difference are determined based on at least one of the following: the maximum detection range of the LiDAR system, the pulse repetition rate of the transmitted light pulses, or the photodetector resolution. Typically, the minimum jitter difference requirement of a LiDAR system depends on the distance resolution of the photodetector (e.g., the minimum distance between two return light pulses that the photodetector can separate). Generally, the higher the distance resolution of the detector, the smaller the minimum jitter difference. In some scenarios, other factors (e.g., measurement errors) may need to be considered when determining the minimum jitter difference to provide sufficient margin. The maximum detection range may affect the number of excitation cycles, and the number of excitation cycles should be considered when determining the minimum jitter difference and / or jitter range. The maximum detection range is typically related to the pulse repetition rate (sometimes also called the laser trigger rate). For example, the maximum detection range of a LiDAR system may be 150 meters, 300 meters, 600 meters, or other figures, corresponding to pulse repetition rates of 1000 kHz, 500 kHz, 250 kHz, etc. If a LiDAR system is configured to detect signals at a maximum range of 500 meters with a pulse repetition rate of 250 kHz, then only one excitation cycle may need to be considered to determine the minimum jitter difference and / or jitter range. With higher pulse repetition rates (meaning a reduced maximum detection range), a larger number of excitation cycles may need to be considered to determine the minimum jitter difference and / or jitter range. For example, if the LiDAR system has a pulse repetition rate of 500 kHz or 1000 kHz, then two or three excitation cycles with variable time delays may need to be considered, respectively.

[0141] By using the aforementioned timing jitter patterns (e.g., patterns 1000, 1001, 1003, or 1005), distance ambiguity can be significantly reduced or eliminated. These timing jitter patterns can also be used to filter out noise or interfering return light pulses generated by other LiDAR systems. For example, if other LiDAR systems do not use the same timing jitter pattern, the current LiDAR system applying the timing jitter pattern can distinguish between the desired return light pulse and interfering return light pulses, and identify the correct correspondence between the transmitted light signal and the return light signal. However, in some scenarios, other LiDAR systems may use the same or similar timing jitter patterns as the current LiDAR system. That is, two or more LiDAR systems (e.g., Figure 7 Systems 700 and 710 in the diagram can use the same or similar timing jitter patterns. Therefore, interfering return pulses formed by reflecting transmitted light pulses from one LiDAR system may not be easily distinguishable from expected return pulses from another LiDAR system. To reject these types of interfering return pulses, the aforementioned timing jitter pattern can be superimposed with random timing jitter, resulting in a timing jitter pattern that uniquely identifies the LiDAR system.

[0142] Specifically, to better reject interfering return light pulses, a LiDAR system (e.g., system 300 or 900) can transmit multiple transmitted light pulses according to a second timing jitter mode. The second timing jitter mode includes a first timing jitter mode superimposed with random timing jitter. The first timing jitter mode is a mode with predetermined non-random timing jitter (e.g., modes 1001, 1003, 1005, and 1007). As described above, the first timing jitter mode is predetermined to reduce or eliminate distance ambiguity, such that the returned light pulse is correlated with the transmitted light pulse in one of two or more consecutive transmitted light pulses emitted immediately preceding the reception of the returned light pulse. However, if another LiDAR system uses the same or similar timing jitter mode, the first timing jitter mode may be insufficient to reject some interfering return light pulses from that other LiDAR system. In this case, the first timing jitter mode can be superimposed with random timing jitter. The random timing jitter is randomly selected and can have a small value (e.g., not greater than 3 ns). When the random timing jitter is small, it may not affect the constraints of the first timing jitter mode. For example, random timing jitter should not affect or significantly affect the minimum timing jitter difference between adjacent timing jitters, or the entire timing jitter range. As described below, when random timing jitter is included, one or more transmitted light pulses are uncorrelated with interfering return light pulses. Thus, one or more calculated object distances are uncorrelated. This, in turn, helps to reject interfering return light pulses received from one or more other LiDAR systems. In particular, random timing jitter can reduce the chance of interference when two LiDAR systems are using the same mode.

[0143] Return to reference Figure 9A After the steering mechanism 945 scans the transmitted light pulse at a time position based on a timing jitter pattern, the optical receiver and photodetector 952 receive multiple returned light pulses from outside the LiDAR system 900. The returned light pulses include, for example, a first returned light pulse and one or more adjacent returned light pulses. Figure 9B As shown, at time position t R1 t R2 and t R3 Three exemplary return light pulses, R1, R2, and R3, are received. The return light pulses are formed based at least on multiple transmitted light pulses emitted by the LiDAR system 900. In some cases, the return light pulses may include interfering return light pulses (e.g., from another LiDAR system).

[0144] Given a timing jitter pattern (a first timing jitter pattern without random timing jitter or a second timing jitter pattern with random timing jitter), the control circuitry 960 can calculate the object distance of the returned light pulses, including both the desired returned light pulse and the interfering returned light pulse. The control circuitry 960 can reject one or more irrelevant object distances from the calculated object distances based on filtering criteria. The irrelevant object distance corresponds at least to one or more transmitted light pulses that are unrelated to any one of the multiple returned light pulses caused by distance ambiguity. As mentioned above, if interfering returned light pulses are present, the irrelevant object distance can also correspond to one or more transmitted light pulses that are unrelated to the interfering returned light pulses. After filtering, the control circuitry 960 can provide the remaining object distances for generating point cloud data.

[0145] Figure 11 This is a block diagram of a control circuitry 1100 for a LiDAR system, configured to perform object distance calculations using a timing jitter mode (with or without random timing jitter). The control circuitry 1100 may include an object distance calculator and a filter 1106, which may include hardware circuitry (e.g., implemented by a processor, GPU, or FPGA) and / or software programs. The control circuitry 1100 may be the aforementioned circuitry 960 or 350, and when the LiDAR system 300 or 900 operates at a much higher repetition rate than conventional LiDAR systems, this control circuitry can be used to correctly identify objects at specific distances while eliminating or reducing distance ambiguity and interference. The object distance calculator and filter 1106 may include an object distance calculator 1110, an object filter 1120, and filtered distant objects 1130. The object distance calculator and filter 1106 can accurately determine the distance to an object by taking into account the variable time interval between transmitted light pulses determined by the timing jitter pattern and by applying one or more filters to reject distance calculations that do not meet the appropriate criteria (i.e., irrelevant distances).

[0146] The distance calculator and filter 1106 of the control circuit system 1100 can receive timing jitter mode 1104 from the time interval adjustment circuit system 1102. Timing jitter mode 1104 can be any of modes 1000, 1001, 1003, and 1005, or these modes superimposed with random timing jitter. (As described above...) Figure 9B The variable time interval between consecutive transmitted light pulses can be determined based on the timing jitter mode. For example, as... Figure 9BAs shown, the time interval between consecutive transmitted light pulses can be obtained based on timing jitter j1, j2, j3, and j4. Timing jitter mode 1104 can be a mode with non-random timing jitter or a mode with non-random timing jitter superimposed with random timing jitter. Based on the variable time interval derived from timing jitter mode 1104, the temporal position of the transmitted light pulse can be determined. For example, in... Figure 9B In the middle, the transmitted light pulses T1, T2, T3 and T4 at time position t T1 t T2 t T3 and t T4 It was launched from there.

[0147] refer to Figure 11 and Figure 12A The object distance (OD) calculator 1110 can calculate several different distances, each corresponding to a different time position of a transmitted light pulse. That is, for any specific returned light pulse received by the LiDAR system, the OD calculator 1110 can calculate a first object distance based on the time position of the specific returned light pulse and the time position of the last transmitted light pulse emitted before receiving the specific returned light pulse; and a second object distance based on the time position of the specific returned light pulse and the time position of the penultimate transmitted light pulse emitted before receiving the specific returned light pulse. In other words, the OD calculator 1110 can be configured to calculate the object distance between any specific returned light pulse with respect to two, three, or even more transmitted light pulses emitted before the specific returned light pulse is received. In some examples, the time position of the transmitted light pulse corresponds to the light source 940 (…). Figure 9A (As shown) is the time indicated for emitting transmitted light pulses. Since the light source 940 emits transmitted light pulses based on a variable time interval, for at least two consecutive returned light pulses, the OD calculator 1110 can perform object distance calculations for multiple time positions of the transmitted light pulses using a moving window. Figure 12A and Figure 12B The illustration shows the details.

[0148] Figure 12A The illustration shows an illustrative timing diagram illustrating the transmitted light pulses and their corresponding time positions, adjacent returning light pulses and their corresponding time positions with respect to the two excitation cycles. The first excitation cycle (i.e., cycle 1) is at t T1 It can begin at point t, or when the laser source is triggered to emit the first optical pulse T1, which may occur at t T1 Not long ago, two transmitted light pulses were shown as T1 and T2, and their respective time positions were t. T1 and t T2 The transmitted light pulses T1 and T2 are located at time positions t, respectively. T1 and t T2The first return light pulse R1 is emitted at time position t. R1 The signal is received at point t. The second excitation cycle (i.e., cycle 2) begins at t. T2 This occurs either at the point where the laser source is triggered to emit the second optical pulse T2. The two transmitted optical pulses are shown as T2 and T3, and at time position t... R2 The second returned optical pulse R2 is received at point t. The third excitation period is at t T3 Starting at, or at the light source at t T3 It begins when triggered shortly before, and so on. It should be understood that other transmitted and / or returned light pulses can also be emitted and / or received in multiple excitation cycles, and are not shown. For example, one or more interfering returned light pulses (not shown) from other LiDAR systems can also be received, and can have different time positions than the returned light pulses R1 and R2. The following description uses R1 and R2 as examples for calculating object distance, but similar calculations and filtering can be applied to interfering returned light pulses. As mentioned above, to reject interfering returned light pulses, a timing jitter pattern with both non-random timing jitter and optionally random timing jitter can be applied.

[0149] exist Figure 12A In some embodiments, the first return optical pulse R1 and the second return optical pulse R2 can be adjacent return optical pulses received consecutively during a horizontal scan. Therefore, Figure 12A The pulses R1 and R2 in the equation can correspond to, for example... Figure 9B The timing diagram 935 shown includes pulses R1 and R2. In other examples, in Figure 12A In this context, the second returning optical pulse R2 is one of eight adjacent returning optical pulses of the first returning optical pulse R1 in the horizontal, vertical, or diagonal direction. Figure 9C A timing diagram is described, showing the temporal positions of transmitted light pulses in two dimensions (e.g., horizontal and vertical dimensions), and thus, for any given transmitted light pulse, it has eight adjacent transmitted light pulses. Similarly, returned light pulses can correspond to two-dimensional temporal positions, generated by the horizontal and vertical scans of the LiDAR system. Therefore, for any given returned light pulse, it can also have eight adjacent returned light pulses in the horizontal, vertical, and diagonal directions, similar to... Figure 9C The transmitted light pulses shown are as follows. Therefore, the following description regarding the distance calculation of the return light pulses R1 and R2 can be applied not only to the case where R1 and R2 are consecutive return pulses received during a scan in the horizontal direction, but also to the case where R1 and R2 are eight adjacent return light pulses in the horizontal, vertical, or diagonal directions.

[0150] exist Figure 12AIn the diagram, the time positions of the transmitted light pulses T1, T2, and T3 are shown as t T1 t T2 and t T3 The time positions of the returned light pulses R1 and R2 are shown as t, respectively. R1 and t R2 The time intervals between consecutive transmitted light pulses T1, T2, and T3 are shown as M1 and M2, where M1 = (t... T2 -t T1 ) and M2=(t T3 -t T2 Because of time position t T1 t T2 and t T3 Based on the timing jitter pattern described above, the time interval between consecutive transmitted light pulses T1, T2, and T3 is variable (e.g., M1 ≠ M2). The variable time interval represents the variable repetition rate of the emitted consecutive transmitted light pulses. Figure 12A The transmitted light pulses T1, T2, and T3 shown can correspond to specific angles (e.g., Figure 9C The transmitted light pulse corresponding to one of the angles P0, P1, P2 to PN.

[0151] Figure 12B The calculation of the object distance (OD) for the first returned light pulse R1 and the second returned light pulse R2 is shown. When receiving the returned light pulses R1 and R2, the LiDAR system does not know whether the first returned light pulse R1 corresponds to the transmitted light pulse T2 or T1 (or whether pulse R1 corresponds to any transmitted light pulse). However, the LiDAR system determines that both transmitted light pulses T1 and T2 were emitted before the received returned light pulse R1. The OD calculator 1110 can calculate two different distances: one relative to the transmitted light pulse T2 (shown as OD1). (R1) The other is relative to the transmitted light pulse T1 (shown as OD2). (R1) Pulse T2 is the last transmitted light pulse emitted before receiving the first returned light pulse R1; and pulse T1 is the penultimate transmitted light pulse emitted before receiving the returned light pulse R1. Similarly, since both transmitted light pulses T2 and T3 are emitted before receiving the returned light pulse R2, the OD calculator 1110 can calculate two different distances: one relative to the transmitted light pulse T3 (shown as OD1). (R2) Another relative to the transmitted light pulse T2 (shown as OD2) (R2)Pulse T3 is the last transmitted light pulse emitted before receiving the second return light pulse R2; and pulse T2 is the penultimate transmitted light pulse emitted before receiving the return light pulse R2. In some examples, the OD calculator 1110 can use the third-to-last transmitted light pulse for calculation. Therefore, for the second return light pulse R2, the OD calculator 1110 can also calculate the distance relative to the transmitted light pulse T1. If the OD calculator 1110 calculates the distance relative to two last transmitted light pulses, it is sometimes referred to as a two-cycle calculation; and if it calculates the distance relative to three last transmitted light pulses, it is referred to as a three-cycle calculation. Regardless of whether it is a two-cycle or three-cycle calculation, the timing jitter pattern described above can be used to significantly reduce or eliminate distance ambiguity and interference with the return light signal.

[0152] As described above, given a timing jitter pattern, the temporal position of the transmitted light pulse can be determined. Furthermore, a variable time interval (e.g., M1 or M2) can be determined. Using this information and Figure 12B The formula shown can be used to calculate distance. As an example, assume the time and location are t. R1 If OD1 is 1.2 µs and M1 is 1 µs, then OD1 (R1) It is calculated to be approximately 30 meters, and OD2 (R1) It was calculated to be approximately 180 meters. Similarly, since the LiDAR system does not know whether the second returning light pulse R2 corresponds to its last transmitted light pulse T3 or to the penultimate transmitted light pulse T2, the OD calculator 1110 can calculate two different distances: one relative to the last transmitted light pulse T3 (shown as OD1). (R2) Another one relative to the penultimate transmitted light pulse T2 (shown as OD2) (R2) Following the example above, and assuming t R2 If M1 is 2.2 µs and M2 is 1.2 µs, then OD1 (R2) It is calculated to be approximately 13.5 meters, and OD2 (R2) The distance was calculated to be approximately 180 meters. The two distances calculated for the first returned light pulse R1 and the two distances calculated for the second returned light pulse R2 cannot both be true object distances (or if R1 and / or R2 are interfering returned light pulses, then neither may be true). In other words, at least some of the calculated distances are irrelevant, meaning that a particular returned light pulse is unrelated to a particular transmitted light pulse. Therefore, one or more of these calculated object distances are irrelevant and need to be filtered out or rejected.

[0153] Return to reference Figure 11After the OD calculator 1110 calculates multiple object distances, the object filter 1120 can apply filtering criteria to reject one or more irrelevant object distances. An irrelevant object distance corresponds to at least one of the following: one or more transmitted light pulses that are unrelated to any one of the multiple returned light pulses caused by distance ambiguity, or one or more transmitted light pulses that are unrelated to interfering returned light pulses. Continuing the example above, based on one or more filtering criteria, the object filter 1120 determines whether to reject one or both of the distant objects OD1 and OD2.

[0154] Figure 12C and Figure 12D The illustration shows some exemplary filtering criteria used for rejection. In some examples, the filtering criteria are based on the assumption that the physical object (e.g., pedestrians, vehicles, trees, buildings, etc.) is continuous in the field of view of the LiDAR system, and that the angle between the line connecting the continuous scan points falling on the object's surface and the laser beam emitted to scan the object's surface is greater than a threshold. This second assumption is generally valid because if the angle is too small, the scattered light (forming a return pulse) is typically very weak due to the large angle of incidence. In other words, if the LiDAR system receives a return pulse with good signal strength, the second assumption is likely valid. Therefore, if the LiDAR system detects a positive return pulse from an object at a given angle, at least one of its adjacent angles should also produce a positive return pulse. For example, again using... Figure 9C To explain, Figure 9C The positions shown are considered as return pulse positions (not transmission pulse positions). If an object is detected at position T21 but not at its adjacent positions T11, T31, or T20, the same object should be detected at T22 (or a diagonal position like T12). Furthermore, assuming no object can move faster than a specific speed (e.g., 5 km / s) and adjacent scan points are measured at very short time intervals (e.g., 1 µs for a lateral scan or 1 ms for a vertical scan), the distance calculated for these two adjacent positive return pulses should be less than a specific threshold distance (e.g., 5 meters). If only one specific positive return pulse is obtained, and none of its eight adjacent positions have a corresponding return pulse, the object filter 1120 may consider that specific positive return pulse as noise or a false return. For example, briefly refer again... Figure 9CTo clarify, if a returned light pulse is detected at time position T21, but nothing is detected within a reasonable distance (e.g., 5 meters) at time positions T11, T31, T20, or T22, the object filter 1120 may reject the returned light pulse at time position T21 as noise or erroneous returned light pulses that may be caused by distance ambiguity, aliasing, or interference.

[0155] Now for reference Figure 11 and Figure 12C Based on the two-cycle distance calculation, the OD filter 1120 calculates the first object distance (e.g., OD1). (R1) ) and the third object distance (e.g., OD1) (R2) The first object distance and the third object distance are both relative to the last transmitted light pulse of the corresponding returned light pulse (e.g., first returned light pulse R1 and second returned light pulse R2). The OD filter 1120 also calculates the second object distance (e.g., OD2). (R1) ) and the fourth object distance (e.g., OD2) (R2) The second difference between the first and fourth object distances is calculated. Both the second and fourth distances are the penultimate transmitted light pulse relative to the corresponding returned light pulses (e.g., the first returned light pulse R1 and the second returned light pulse R2). The OD filter 1120 then compares the first difference with a distance threshold and the second difference with a distance threshold. Based on the comparison results, the OD filter 1120 rejects one or more of the first, second, third, and fourth object distances.

[0156] For example, based on two-cycle calculations, the OD filter 1120 can determine whether the first difference is not greater than a threshold distance (e.g., 0.5-5 meters). If the first difference is greater than the threshold distance, it means that the first and third object distances do not represent a continuous object surface. Therefore, they may be irrelevant object distances caused by distance ambiguity and / or interference with the returned light signal. Similarly, the OD filter 1120 can determine whether the second difference is not greater than a threshold distance. If the second difference is greater than the threshold distance, it means that the second and fourth object distances do not represent a continuous object surface.

[0157] The following uses Figure 11 , Figure 12C and Figure 12D Describe an example of filtering. In one example, object filter 1120 can be compared using the same vertical scan angle (e.g., Figure 9C The object distance (OD1) is obtained by using the returned light pulse associated with the angles P0, P1, etc. in the middle of the path to apply a time-based filter. Specifically, the object filter 1120 can determine OD1. (R2) With OD1 (R1)The absolute value of the difference between them is less than a threshold (e.g., 5 meters). If this determination is true, the distance associated with OD1 is considered positive and is allowed to pass as a filtered distant object 1130. In other words, OD1 (R2) and OD1 (R1) This is the relevant object distance. If this determination is false, the distance associated with OD1 is negative / non-passing, and this distance is stored in memory. This means OD1 (R2) and OD1 (R1) The object distance may be irrelevant, but further determination may be needed. Negative / non-passing OD1 is marked as NOD1 and stored in memory for subsequent analysis. Object filter 1120 also determines OD2. (R2) With OD2 (R1) The absolute value of the difference between them is less than the same threshold. If this determination is true, the distance associated with OD2 is considered positive and is allowed to pass as a filtered distant object 1130. If this determination is false, the distance associated with OD2 is negative / not allowed, and this distance is stored in memory (and will be referred to as NOD2 below) for further analysis.

[0158] Continuing with the example above, if the threshold is set to 5 meters, the OD1 filter will not pass because 30 - 13.5 equals 16.5 meters, which is greater than 5 meters; however, the OD2 filter will pass because 180 - 180 equals 0, which is less than 5 meters. In reality, depending on the object and the measurement uncertainties and motion of the LiDAR system, this value can be a small non-zero value, such as, for example, one to five centimeters. Based on this filter, OD1 will be rejected and stored in memory, while the OD2 distance of 180 meters will be filtered as a positive distance for the object 1130 to pass.

[0159] Therefore, it is demonstrated that by varying the time interval according to the aforementioned timing jitter pattern, the distance to an object (whether near or far from the LiDAR system) can be accurately calculated and mapped. This is because even if the object (especially a distant object) might cause distance ambiguity (e.g., ghosting or aliasing) when determining which returned light pulse corresponds to which transmitted light pulse, distance calculation based on the repetition rate / time interval of the timing jitter pattern allows irrelevant object distances to be rejected. Based on the timing jitter pattern described herein, the time interval variation between adjacent transmitted light pulses is sufficiently different to allow filtering criteria to be applied to the system with high confidence.

[0160] In some embodiments, as described above, the timing jitter pattern may have repetition (e.g., in line 1002 of pattern 1000, the first few timing jitters are -21, -3, -15, -9, -21, -3, -13; then the timing jitter begins to repeat). Therefore, the time interval between adjacent transmitted light pulses can be repeated as a sequence of predetermined time intervals. For example, this sequence may include a fixed number of time intervals, each with a different length that satisfies a minimum increment requirement between adjacent time intervals to account for various tolerances in LiDAR systems. This sequence can be repeated as needed to trigger transmitted light pulses according to the variable time intervals discussed herein.

[0161] refer to Figure 12D NOD1 and NOD2 can be used for secondary verification to confirm the validity of the returned light pulses associated with distance calculations of NOD1 and NOD2. For example, NOD1 and NOD2 can be applied to a spatially based filter, as described below, to verify the validity of the returned light pulses associated with NOD1 and NOD2. The spatially based filter compares NOD1 and NOD2 with corresponding object distances at different vertical angles to determine if any associated returned light pulses exist at adjacent vertical angles. (Reusing...) Figure 9C As an illustration (and observation) Figure 9C The dashed lines representing the transmitted light pulses (which represent the returned light pulses) correlate the calculated OD1 and OD2 with R1 and R2 (which can be returned light pulses associated with angle P1). Object filter 1120 can correlate OD1 and OD2 with returned light pulses at other adjacent angles (e.g., P0 and P2) and determine whether the calculated object distance is relevant. (Return to Reference) Figure 12D If no associated return light pulse exists for NOD1 and NOD2 at adjacent angles (e.g., if the difference between NOD1 and NOD2 at two different angles P1 and P0 is not less than a threshold distance), then NOD1 and NOD2 can be rejected. If associated return light pulses exist for NOD1 and NOD2 at adjacent angles (e.g., if the difference between NOD1 and NOD2 at two different angles P1 and P0 is less than a threshold distance), then NOD1 and NOD2 can be retained in memory for further analysis, which will be used below. Figure 12D A more detailed description. Vertical angle refers to the angle within the vertical field of view of the LiDAR system.

[0162] exist Figure 11 and Figure 12DIn the example shown, object filter 1120 can apply a space-based filter by comparing object distances between adjacent angles. For example, suppose a time-space filter produces NOD2 based on a first angle (e.g., angle P0) and also produces NOD2 based on adjacent angles (e.g., angle P1). Object filter 1120 can determine NOD2. (P0) and NOD2 (P1) The absolute value of the difference between the two distances is less than the vertical distance threshold. If this determination is true, the distance associated with NOD2 is considered accurate and is allowed to pass as a filtered distant object 1130. Therefore, NOD2 is a relevant object distance. If this determination is false, the distance associated with NOD2 is negative / not passed, and this distance is stored in memory for further analysis. Therefore, NOD2 is an irrelevant object distance.

[0163] In some embodiments, the variable time interval obtained based on the timing jitter pattern described herein can be used to detect truly distant objects. This can be achieved by calculating and filtering the object distance for at least three consecutive transmitted light pulses. This is referred to as a three-cycle distance calculation. It includes the two-cycle distance calculation described above, and additionally performs a calculation with respect to the penultimate transmitted light pulse. Specifically, refer to... Figure 9B Assuming the first returned light pulse is R2 and the second returned light pulse is R3, the OD calculator 1110 calculates, for the first returned light pulse R2, the object distance between the time position of the first returned light pulse R2 and each of the time positions of the first transmitted light pulse T3, the second transmitted light pulse T2, and the penultimate transmitted light pulse T1 emitted before receiving the first returned light pulse R2. Similarly, for the second returned light pulse R3, the OD calculator 1110 calculates the object distance between the time position of the second returned light pulse R3 and each of the time positions of the first transmitted light pulse T4, the second transmitted light pulse T3, and the penultimate transmitted light pulse T2 emitted before receiving the second returned light pulse R3.

[0164] Next, object filter 1120 calculates a first difference between a first object distance and a third object distance (e.g., both the first and third distances are used for the last transmitted light pulse); calculates a second difference between a second object distance and a fourth object distance (e.g., both the second and fourth distances are used for the penultimate transmitted light pulse); calculates a third difference between a fifth object distance and a sixth object distance (e.g., both the fifth and sixth distances are used for the penultimate transmitted light pulse); compares the first difference with a distance threshold; compares the second difference with a distance threshold; compares the third difference with a distance threshold; and rejects one or more of the first, second, third, fourth, fifth, and sixth object distances based on the comparison results. Therefore, similar to the two-cycle distance calculation process, the three-cycle distance calculation allows object filter 1120 to perform comparisons and reject irrelevant object distances to eliminate distance ambiguity and interference with returned light pulses.

[0165] In a specific example, refer to Figure 9B Assuming that the returned light pulse R2 is actually the returned light pulse corresponding to the transmitted light pulse T1, the returned light pulse R3 corresponds to the transmitted light pulse T2, and the returned light pulse R1 does not exist, the LiDAR system can determine whether R2 corresponds to T3, T2, or T1 using the embodiments discussed herein. In this example, the OD calculator 1110 can calculate multiple object distances relative to the returned light pulses R3 and R2 to determine the correct distance for pulse R2. For example, the OD calculator 1110 can calculate the following object distance for the returned light pulse R3: an object distance relative to T4 (OD1... (R3) ), an object distance (OD2) relative to T3 (R3) ), an object distance relative to T2 (OD3) (R3) The OD calculator 1110 can calculate the following object distance for a returned light pulse R2: an object distance relative to T3 (OD1). (R2) ), an object distance relative to T2 (OD2) (R2) ) and an object distance (OD3) relative to T1. (R2) All object distances can be evaluated by one or more filters to determine which object distances should be accepted or rejected. For example, the OD1 filter can calculate (OD1...). (R3) ) and (OD1) (R2) The OD2 filter can calculate (OD2) by comparing the difference between the two values ​​and a threshold. (R3) ) and (OD2) (R2) The difference between (OD3) and the threshold are compared, and the OD3 filter can calculate (OD3) (R3) ) and (OD3) (R2) The difference between (OD3) and the threshold is compared. In this particular example, (OD3) (R3) ) and (OD3) (R2)The difference between R2 and T1 is approximately zero, therefore indicating the correct distance associated with R2 is related to the transmitted light pulse T1. Thus, it should be understood that the variable time interval embodiment can be used to correctly determine the position of an object at any reasonable distance, including, for example, 500 meters or more. Depending on the desired range of distance calculations, the system can calculate the necessary number of distance calculations required to make the determination. The examples above illustrate distance calculations for three consecutive emission pulses (and the examples in Figures 9 and 12 illustrate distance calculations for two consecutive emission pulses). If needed, the system can calculate the distance relative to any number of consecutive emission pulses using the number required to cover the desired range of object detection.

[0166] Figure 13A This is a flowchart illustrating an exemplary method 1300 for operating a LiDAR system to eliminate or significantly reduce distance ambiguity and / or interference using a timing jitter pattern, according to some embodiments. Referring to method 1300, in block 1302, a transmitter (e.g., 910) of a LiDAR system (e.g., system 300 or 900) emits a plurality of transmitted light pulses in two orthogonal directions according to a first timing jitter pattern. The first timing jitter pattern includes a predetermined non-random timing jitter satisfying one or more constraints. The first timing jitter pattern can be any of patterns 1000, 1001, 1003, and 1005. The first timing jitter pattern represents a variable time interval between successive transmitted light pulses of the plurality of transmitted light pulses. In some examples, the transmitter emits each transmitted light pulse at a time position determined based on a fixed pulse repetition rate and the corresponding timing jitter of the transmitted light pulse.

[0167] In some examples, a first timing jitter mode is formed such that the timing jitter in the first timing jitter mode is constrained within a jitter range; and any two adjacent timing jitters in the first timing jitter mode satisfy a minimum jitter difference. Additionally, a two-cycle cumulative timing jitter mode is formed by accumulating the timing jitter in the first timing jitter mode over any two consecutive excitation cycles. The excitation cycle is defined by the time interval between the time positions of two consecutive transmitted light pulses emitted. The timing jitter in the first timing jitter mode is formed based on the additional constraint that any two adjacent two-cycle cumulative timing jitters in the two-cycle cumulative timing jitter mode satisfy a minimum jitter difference.

[0168] In some examples, the jitter range and minimum jitter difference are determined based on at least one of the following: the maximum detection range of the LiDAR system; the pulse repetition rate of the transmitted light pulse; and the resolution of the photodetector.

[0169] In some examples, the timing jitter in the first timing jitter mode forms an array with multiple rows. Each row includes multiple timing jitters for applying to a corresponding transmitted light pulse emitted in the horizontal scanning direction, and different rows correspond to different vertical angles in the vertical scanning direction. For each row in the first timing jitter mode, the cumulative timing jitter of all timing jitters in that row is approximately zero. In some examples, each row in the first timing jitter mode includes multiple positive timing jitters and multiple negative timing jitters, which are alternated in the row. In some examples, corresponding timing jitters in two adjacent rows of the array have the same value but opposite signs. In some examples, corresponding timing jitters in two adjacent rows of the array have different values ​​and opposite signs.

[0170] In some examples, the emission of multiple transmitted light pulses is based on a first timing jitter pattern superimposed with random timing jitter. This first timing jitter pattern, superimposed with random timing jitter, forms a second timing jitter pattern. The random timing jitter is randomly selected, and one or more uncorrelated object distances further correspond to one or more transmitted light pulses uncorrelated with the interfering returned light pulses. Interfering returned light pulses are received from one or more other LiDAR systems.

[0171] In block 1304, the optical receiver and photodetector (e.g., 952) of the LiDAR system receive multiple returned light pulses from outside the LiDAR system. The multiple returned light pulses include a first returned light pulse and one or more adjacent returned light pulses of the first returned light pulse. Figure 9B The illustration shows, for example, a first returning optical pulse R2, and adjacent returning optical pulses R1 and R3. Multiple returning optical pulses are formed based on at least multiple transmitted optical pulses.

[0172] In box 1306, for the first returned light pulse and at least one adjacent returned light pulse among a plurality of returned light pulses, the OD calculator (e.g., 1110) of the LiDAR system calculates multiple object distances based on a timing jitter pattern to obtain a calculated object distance. Such an object distance could be, for example, OD1. (R1) OD2 (R1) OD1 (R2) OD2 (R2) As mentioned above. Specifically, refer to... Figure 13BIn the two-cycle calculation process 1320, for the first returning light pulse, the OD calculator calculates (box 1322) the first object distance based on the time position of the first returning light pulse and the time position of the last transmitted light pulse emitted before receiving the first returning light pulse; and calculates (box 1324) the second object distance based on the time position of the first returning light pulse and the time position of the penultimate transmitted light pulse emitted before receiving the first returning light pulse. The OD calculator further calculates (box 1326) the third object distance based on the time position of the second returning light pulse and the time position of the last transmitted light pulse emitted before receiving the second returning light pulse; and calculates (box 1328) the fourth object distance based on the time position of the second returning light pulse and the time position of the penultimate transmitted light pulse emitted before receiving the second returning light pulse.

[0173] In some embodiments, the first return light pulse and the second return light pulse are adjacent return light pulses received consecutively during a horizontal scan. In some embodiments, the second return light pulse is one of eight adjacent return light pulses of the first return light pulse in the horizontal, vertical, or diagonal direction.

[0174] refer to Figure 13C In the three-cycle calculation process 1350, in addition to performing the above-mentioned... Figure 13B In addition to the same calculation steps described in blocks 1322-1328 of process 1320, the OD calculator further performs the following operations: for the first returning light pulse, calculates (box 1352) the fifth object distance based on the time position of the first returning light pulse and the time position of the penultimate transmitted light pulse emitted before receiving the first returning light pulse; and for the second returning light pulse, calculates (box 1354) the sixth object distance based on the time position of the second returning light pulse and the time position of the penultimate transmitted light pulse emitted before receiving the second returning light pulse.

[0175] Return to reference Figure 13A In box 1308, the object filter (e.g., 1120) rejects one or more irrelevant object distances from the calculated object distances based on filtering criteria. An example of the filtering criteria is... Figure 12D As shown and described above, an irrelevant object distance corresponds to one or more transmitted light pulses that are unrelated to any one of the multiple returned light pulses caused by distance ambiguity or interference. (Continued) Figure 13BIn the above example of the two-cycle calculation process 1320, the filter performs the following steps: calculates (box 1332) the first difference between the first and third object distances; calculates (box 1334) the second difference between the second and fourth object distances; compares the first difference with a distance threshold (box 1336); compares the second difference with a distance threshold (box 1338); and rejects (box 1340) one or more of the first, second, third, and fourth object distances based on the comparison results (e.g., in the two-cycle calculation, if the first difference is greater than 0.5-5 meters, it means that the first and third object distances do not represent a continuous object surface, and therefore, they are incorrect object distances caused by distance ambiguity. Similarly, for interference pulses, the difference can be large).

[0176] For the three-cycle calculation process 1350, the filter performs the following calculations: (box 1362) the first difference between the first and third object distances; (box 1364) the second difference between the second and fourth object distances; (box 1366) the third difference between the fifth and sixth object distances; compares the first difference with a distance threshold; compares the second difference with a distance threshold (box 1368); compares the third difference with a distance threshold; and rejects (box 1370) one or more of the first, second, third, fourth, fifth, and sixth object distances based on the comparison results.

[0177] In some examples, the LiDAR system determines whether at least one adjacent return pulse of the first return pulse has been received; based on the determination that at least one adjacent return pulse of the first return pulse has been received, it obtains the time position of the first return pulse and at least one adjacent return pulse for calculating multiple object distances; and based on the determination that at least one adjacent return pulse of the first return pulse has not been received, it rejects the first return pulse. In other words, if a return pulse has no adjacent return pulses, it is likely noise or interference and should be rejected. This case does not require calculation.

[0178] Return to reference Figure 13A In box 1310, the object filter provides the remaining object distance of at least two calculated object distances for generating point cloud data.

[0179] The foregoing description should be understood as illustrative and exemplary in all respects, not restrictive, and the scope of the invention disclosed herein is not determined by the description, but by the claims as interpreted in the fullest extent 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 various modifications can be made by those skilled in the art without departing from the scope and spirit of the invention. Various other combinations of features can be implemented by those skilled in the art without departing from the scope and spirit of the invention.

Claims

1. A method for operating an optical ranging and detection (LiDAR) system, the method comprising: According to a first timing jitter mode, multiple transmitted light pulses are emitted in two orthogonal directions, wherein the first timing jitter mode includes a predetermined non-random timing jitter that satisfies one or more constraints, and the first timing jitter mode represents a variable time interval between consecutive transmitted light pulses of the multiple transmitted light pulses. Multiple return light pulses are received from outside the LiDAR system, wherein the multiple return light pulses include a first return light pulse and one or more adjacent return light pulses of the first return light pulse, and the multiple return light pulses are formed based at least on the multiple transmitted light pulses; For the first return light pulse and at least one adjacent return light pulse among the plurality of return light pulses, multiple object distances are calculated based on the timing jitter mode to obtain the calculated object distance; Based on the filtering criteria, one or more irrelevant object distances in the calculated object distances are rejected. The irrelevant object distances correspond to one or more transmitted light pulses that are not related to any one of the plurality of returned light pulses caused by at least one of the distance ambiguity or interference returned light pulses. as well as The remaining object distance based on the calculated object distance is provided for generating point cloud data.

2. The method according to claim 1, wherein sending the plurality of transmitted light pulses comprises: Each transmitted light pulse is emitted at a time position determined based on a fixed pulse repetition rate and the corresponding timing jitter of the transmitted light pulse.

3. The method according to any one of claims 1 to 2, wherein the first timing jitter mode is configured such that: The timing jitter in the first timing jitter mode is limited to a jitter range; and Any two adjacent timing jitters in the first timing jitter mode satisfy the minimum jitter difference.

4. The method according to claim 3, wherein: A two-cycle cumulative timing jitter mode is formed by accumulating the timing jitter in the first timing jitter mode over any two consecutive excitation cycles. The excitation cycle is defined by the time between the time positions of the two consecutive transmitted light pulses emitted. and The timing jitter in the first timing jitter mode is formed based on the additional constraint that any two adjacent cumulative timing jitters in the two-cycle cumulative timing jitter mode satisfy the minimum jitter difference.

5. The method of claim 3, wherein the jitter range and the minimum jitter difference are determined based on at least one of the following: The maximum detection range of the LiDAR system; The pulse repetition rate of the transmitted light pulse; and Photodetector resolution.

6. The method according to any one of claims 1 to 2, wherein the timing jitter in the first timing jitter mode forms an array having a plurality of rows, wherein each of the plurality of rows includes a plurality of timing jitters for applying to a corresponding transmitted light pulse emitted in the horizontal scanning direction, and different rows correspond to different vertical angles in the vertical scanning direction.

7. The method of claim 6, wherein for each of the plurality of rows in the first timing jitter mode, the cumulative timing jitter of all the timing jitters in the row is approximately zero.

8. The method of claim 6, wherein each of the plurality of rows in the first timing jitter mode includes a plurality of positive timing jitters and a plurality of negative timing jitters, the positive timing jitters and the negative timing jitters being alternately arranged in the rows.

9. The method of claim 6, wherein corresponding timing jitter in two adjacent rows of the plurality of rows in the array has the same value but opposite signs.

10. The method of claim 6, wherein corresponding timing jitters in two adjacent rows of the plurality of rows in the array have different values ​​and opposite signs.

11. The method according to any one of claims 1 to 2, wherein the emission of the plurality of transmitted light pulses is based on a first timing jitter pattern superimposed with random timing jitter, and the first timing jitter pattern superimposed with random timing jitter forms a second timing jitter pattern.

12. The method of claim 11, wherein the random timing jitter is randomly selected, and wherein the one or more uncorrelated object distances further correspond to one or more transmitted light pulses uncorrelated with interference return light pulses received from one or more other LiDAR systems.

13. The method according to any one of claims 1 to 2, wherein, For the first returned light pulse and at least one adjacent returned light pulse among the plurality of returned light pulses, calculating the plurality of object distances to obtain the calculated object distance includes: For the first returned light pulse: The first object distance is calculated based on the time position of the first returned light pulse and the time position of the last transmitted light pulse emitted before receiving the first returned light pulse; and The second object distance is calculated based on the time position of the first returned light pulse and the time position of the penultimate transmitted light pulse emitted before receiving the first returned light pulse.

14. The method according to claim 13, wherein, For the first returned light pulse and at least one adjacent returned light pulse among the plurality of returned light pulses, calculating the plurality of object distances to obtain the calculated object distance further includes: for a second returned light pulse that is an adjacent returned light pulse of the at least one adjacent returned light pulse: The third object distance is calculated based on the time position of the second returned light pulse and the time position of the last transmitted light pulse emitted before receiving the second returned light pulse; and The fourth object distance is calculated based on the time position of the second returned light pulse and the time position of the penultimate transmitted light pulse emitted before receiving the second returned light pulse.

15. The method of claim 14, wherein the first return optical pulse and the second return optical pulse are adjacent return optical pulses received consecutively during a horizontal scan.

16. The method of claim 14, wherein the second return light pulse is one of eight or fewer adjacent return light pulses of the first return light pulse in a horizontal, vertical or diagonal direction.

17. The method of claim 14, wherein rejecting one or more irrelevant object distances from the calculated object distances based on filtering criteria comprises: Calculate the first difference between the first object distance and the third object distance; Calculate the second difference between the second object distance and the fourth object distance; Compare the first difference with the distance threshold; Compare the second difference with the distance threshold; as well as Based on the comparison results, reject one or more of the first object distance, the second object distance, the third object distance, and the fourth object distance.

18. The method according to claim 14, wherein, For the first returned light pulse and at least one adjacent returned light pulse among the plurality of returned light pulses, calculating the plurality of object distances to obtain the calculated object distance further includes: For the first returned light pulse, the fifth object distance is calculated based on the time position of the first returned light pulse and the time position of the penultimate transmitted light pulse emitted before receiving the first returned light pulse; and For the second returned light pulse, the sixth object distance is calculated based on the time position of the second returned light pulse and the time position of the penultimate transmitted light pulse emitted before receiving the second returned light pulse.

19. The method of claim 18, wherein rejecting one or more irrelevant object distances from the calculated object distances based on filtering criteria comprises: Calculate the first difference between the first object distance and the third object distance; Calculate the second difference between the second object distance and the fourth object distance; Calculate the third difference between the fifth object distance and the sixth object distance; Compare the first difference with the distance threshold; Compare the second difference with the distance threshold; Compare the third difference with the distance threshold; as well as Based on the comparison results, reject one or more of the first object distance, the second object distance, the third object distance, the fourth object distance, the fifth object distance, and the sixth object distance.

20. The method according to any one of claims 1 to 2, the method further comprising: Determine whether at least one adjacent return optical pulse of the first return optical pulse has been received; Based on the determination that at least one adjacent return light pulse has been received by the first return light pulse, the time positions of the first return light pulse and the at least one adjacent return light pulse are obtained, which are used to calculate the plurality of object distances; as well as The first returned optical pulse is rejected if at least one adjacent returned optical pulse is not received.

21. A LiDAR system comprising a transmitter, a receiver, and one or more processors for performing the method according to any one of claims 1 to 2.

22. A means of transport comprising a LiDAR system, the LiDAR system comprising a transmitter, a receiver, and one or more processors for performing the method according to any one of claims 1 to 2.

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