Method for a vehicle, vehicle and storage medium

Abstract

The present invention relates to methods, vehicles, and storage media for vehicles. Among other things, a method includes receiving first LiDAR point cloud information from a first LiDAR device and second LiDAR point cloud information from a second LiDAR device; generating third point cloud information from merging the first LiDAR point cloud information and the second LiDAR point cloud information; and operating a vehicle based on the third LiDAR point cloud information.

Description

[0001] This application is a divisional application of the application entitled Merging Data from Multiple LiDAR Devices, having application number 2020800051882 and filing date January 31, 2020.

[0002] Cross Reference to Related Applications

[0003] This application claims priority to U.S. Provisional Patent Application 62 / 799,391 filed January 31, 2019 and Danish Patent Application PA201970131 filed February 27, 2019, the entire contents of both of which are incorporated herein by reference. TECHNICAL FIELD

[0004] This specification relates to merging data from multiple LiDAR (Light Detection and Ranging) devices. SUMMARY

[0005] Techniques and the like for merging data from multiple LiDAR devices are described, the multiple LiDAR devices having different physical locations and can have different start angles and different frequencies.

[0006] In an embodiment, a system includes: at least two LiDAR devices configured to detect light reflected from objects proximate to the vehicle and generate LiDAR point cloud information based on the detected light, wherein a first LiDAR device is at a first location of the vehicle and is configured at a first start angle and a first frequency, and a second LiDAR device is at a second location of the vehicle and is configured at a second start angle and a second frequency; one or more computer-readable media for storing computer-executable instructions; one or more processors communicatively coupled to the at least two LiDAR devices and configured to execute the computer-executable instructions, the execution performing operations comprising: receiving first LiDAR point cloud information from the first LiDAR device and second LiDAR point cloud information from the second LiDAR device, receiving first timestamp information associated with the first LiDAR point cloud information and second timestamp information associated with the second LiDAR point cloud information, and generating third point cloud information merging the first LiDAR point cloud information and the second LiDAR point cloud information according to the first LiDAR point cloud information and the second LiDAR point cloud information, the first timestamp information and the second timestamp information, the first start angle, the first frequency, the second start angle, and the second frequency; and control circuitry communicatively coupled to the one or more processors, wherein the control circuitry is configured to operate the vehicle based on the third LiDAR point cloud information.

[0007] In an embodiment, a method includes: configuring a first LiDAR device to spin from a first starting angle at a first frequency; configuring a second LiDAR device to spin from a second starting angle different from the first starting angle at a second frequency; receiving, from the first LiDAR device, information representing a first detected light point and a first timestamp representing a time at which the first point was illuminated; receiving, from the second LiDAR device, information representing a second detected light point and a second timestamp representing a time at which the second point was illuminated, wherein a difference between a time of the first timestamp and a time of the second timestamp is less than an inverse of the first frequency; determining that the first detected light point and the second detected light point correspond to a same location relative to a vehicle; and generating, in accordance with the determination that the first detected light point and the second detected light point correspond to the same location, LiDAR point cloud information including the first detected light point and the second detected light point at a same coordinate relative to a fixed origin of the vehicle.

[0008] These and other aspects, features, and implementations can be expressed as methods, apparatus, systems, components, program products, methods or steps for performing functions, and in other ways.

[0009] These and other aspects, features, and implementations will become apparent with reference to the following description of specific embodiments.

[0010] These and other aspects, features, and implementations have one or more of the following advantages. Timestamp information can be used to more accurately combine LiDAR point clouds. Timestamps can be used to coordinate between two or more LiDARs with different starting angles and / or different frequencies. If the LiDARs have intentionally staggered starting angles, the information will still be recent enough to use in most scenarios (e.g., recognizing movement of a pedestrian). If one LiDAR is occluded at a particular point in time, information from another LiDAR can be used. BRIEF DESCRIPTION OF DRAWINGS

[0011] FIG. 1 An example of an autonomous vehicle with autonomous capabilities is shown.

[0012] FIG. 2 An example "cloud" computing environment is illustrated.

[0013] FIG. 3 An example computer system is illustrated.

[0014] FIG. 4 An example architecture of an autonomous vehicle is shown.

[0015] FIG. 5 An example of inputs and outputs that a perception module can use is shown.

[0016] FIG. 6An example of a LiDAR system is shown.

[0017] FIG. 7 A LiDAR system in operation is shown.

[0018] FIG. 8 Additional details of the operation of a LiDAR system are shown.

[0019] FIG. 9 A block diagram showing the relationship between the inputs and outputs of a planning module is shown.

[0020] FIG. 10 A directed graph used in path planning is shown.

[0021] FIG. 11 A block diagram showing the inputs and outputs of a control module is shown.

[0022] FIG. 12 A block diagram showing the inputs, outputs, and components of a controller is shown.

[0023] FIG. 13 An AV with two LiDAR devices is shown.

[0024] FIG. 14-15 A LiDAR device is shown in more detail.

[0025] FIG. 16 Components of a system for generating an integrated point cloud are shown.

[0026] FIG. 17A and 17B A representation of an integrated point cloud in the form of a set of polar voxels is shown.

[0027] FIG. 18-19 is a flowchart representation of a process for operating a vehicle based on point cloud information. DETAILED DESCRIPTION

[0028] In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent, however, that the present application can be practiced without

[0029] In the drawings, specific arrangements or orders of illustrative elements are shown, such as those representing devices, modules, instruction blocks, and data elements. However, those skilled in the art will appreciate that the specific ordering or arrangements of illustrative elements in the drawings does not imply that a particular processing order or sequence, or separation of processing processes, is required. Further, inclusion of an illustrative element in a drawing does not imply that such element is required in all embodiments, nor that the features represented by such element cannot be included in some embodiments or combined with other elements.

[0030] Further, in the drawings, connecting elements, such as lines or arrows or the like, are used to illustrate connections, relationships or associations between two or more other illustrative elements, and the absence of such connecting elements does not imply the absence of a connection, relationship or association. In other words, connections, relationships or associations between some elements are not shown in the drawings to not obscure the invention. Further, multiple connections, relationships or associations between elements are shown using a single connecting element for ease of illustration. For example, if a connecting element represents a communication of signals, data or instructions, those skilled in the art will appreciate that the element represents one or more signal paths (e.g., a bus) through which the communication might occur.

[0031] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description of embodiments, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one skilled in the art that the various described embodiments can be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0032] Several of the described features can be used independently of one another, in any combination, or in various embodiments of the application. However, none of the individual features alone, or in any combination other than those specifically described herein, can solve the problems described above, or any single feature may

[0033] 1. OVERALL SUMMARY

[0034] 2. SYSTEM SUMMARY

[0035] 3. AUTONOMOUS VEHICLE ARCHITECTURE

[0036] 4. AUTONOMOUS VEHICLE INPUT

[0037] 5. AUTONOMOUS VEHICLE PLANNING

[0038] 6. Autonomous vehicle control

[0039] 7. Multiple LiDAR devices

[0040] OVERALL SUMMARY

[0041] As described in more detail below with respect to FIG. 13-19 In more detail, a vehicle, such as an autonomous vehicle, can have multiple LiDAR devices installed at different locations of the vehicle. Data from these LiDAR devices can be combined to take advantage of the redundancy.

[0042] SYSTEM SUMMARY

[0043] FIG. 1 An example of an autonomous vehicle 100 with autonomous capabilities is shown.

[0044] As used herein, the term "autonomous capabilities" refers to a function, feature, or facility that enables a vehicle to operate, in part or in whole, without real-time human intervention, including but not limited to fully autonomous vehicles, highly autonomous vehicles, and conditional autonomous vehicles.

[0045] As used herein, an autonomous vehicle (AV) is a vehicle with autonomous capabilities.

[0046] As used herein, a "vehicle" includes a mode of transportation of goods or people. For example, a car, a bus, a train, an airplane, a drone, a truck, a boat, a ship, a submersible, a spacecraft, etc. A self-driving car is an example of a vehicle.

[0047] As used herein, a "trajectory" refers to a path or route that navigates an AV from a first spatiotemporal location to a second spatiotemporal location. In embodiments, the first spatiotemporal location is referred to as an initial or starting location, and the second spatiotemporal location is referred to as a destination, final location, target, target location, or target location. In some examples, a trajectory is composed of one or more segments (e.g., sections of a road), and each segment is composed of one or more blocks (e.g., a portion of a lane or an intersection). In embodiments, spatiotemporal locations correspond to real-world locations. For example, a spatiotemporal location is a pickup or drop-off location for a person or cargo to board or disembark.

[0048] As used herein, a“sensor(s)” includes one or more hardware components for detecting information related to the sensor’s surrounding environment. Some hardware components can include sensing components (e.g., image sensors, biometric sensors), transmitting and / or receiving components (e.g., laser or radio frequency wave emitters and receivers), electronic components (e.g., analog-to-digital converters), data storage devices (e.g., RAM and / or non-volatile memory), software or firmware components, and data processing components (e.g., ASICs (application specific integrated circuits), microprocessors, and / or microcontrollers).

[0049] As used herein, a“scene description” is a data structure (e.g., a list) or data stream that includes one or more classified or labeled objects detected by one or more sensors on an AV or one or more classified or labeled objects provided by a source external to the AV.

[0050] As used herein, a“roadway” is a physical area that can be traversed by a vehicle, and can correspond to a named thoroughfare (e.g., a city street, an interstate highway, etc.) or can correspond to an unnamed thoroughfare (e.g., a driveway within a house or office building, a section of a parking lot, a section of an empty parking lot, a dirt path in a rural area, etc.). Because some vehicles (e.g., four-wheel drive pickup trucks, sport utility vehicles (SUVs), etc.) are capable of traversing a variety of physical areas that are not specifically suited for vehicle travel, a“roadway” can be any physical area that has not been formally defined as a thoroughfare by a municipality or other governmental or administrative body.

[0051] As used herein, a "lane" is a portion of a roadway that can be traversed by a vehicle. Sometimes a lane is identified based on lane markings. For example, a lane can correspond to most or all of the space between lane markings, or only a portion of the space (e.g., less than 50%) between lane markings. For example, a roadway with lane markings far apart can accommodate two or more vehicles such that one vehicle can pass another without crossing a lane marking, and thus can be interpreted as a lane being narrower than the space between lane markings, or as two lanes between lanes. Lanes can also be interpreted in the absence of lane markings. For example, a lane can be defined based on physical features of the environment (e.g., rocks in a rural area and trees along a boulevard, or natural obstacles that should be avoided such as in underdeveloped areas). Lanes can also be interpreted independent of lane markings or physical features. For example, a lane can be interpreted based on an arbitrary path in an area that lacks obstacles that would otherwise lack features to be interpreted as lane boundaries. In an example scenario, an AV can interpret a lane through an unobstructed portion of a field or open space. In another example scenario, an AV can interpret a lane through a wide (e.g., wide enough for two or more lanes) roadway without lane markings. In this scenario, the AV can communicate information about the lane to other AVs so that the other AVs can use the same lane information to coordinate path planning between AVs.

[0052] The term "over-the-air (OTA) client" includes any AV, or any electronic device (e.g., computer, controller, IoT device, electronic control unit (ECU)) embedded in, coupled to, or in communication with an AV.

[0053] The term "over-the-air (OTA) update" means any update, change, deletion, or addition to software, firmware, data, or configuration settings, or any combination thereof, delivered to an OTA client using proprietary and / or standardized wireless communication technologies, including but not limited to: cellular mobile communication (e.g., 2G, 3G, 4G, 5G), wireless radio local area networks (e.g., WiFi), and / or satellite internet.

[0054] The term "edge node" refers to one or more edge devices coupled to a network that provide a portal for communication with AVs and can communicate with other edge nodes and cloud-based computing platforms to schedule and deliver OTA updates to OTA clients.

[0055] The term "edge device" refers to a device that implements an edge node and provides a physical wireless access point (AP) to a core network of an enterprise or service provider (e.g., VERIZON, AT&T). Examples of edge devices include, but are not limited to, a computer, a controller, a transmitter, a router, a routing switch, an integrated access device (IAD), a multiplexer, a metropolitan area network (MAN) and a wide area network (WAN) access device.

[0056] "one or more" includes a function performed by one element, a function performed by more than one element, for example in a distributed manner, several functions performed by one element, several functions performed by several elements, or any combination of the above.

[0057] It will also be understood that, although the terms "first," "second," etc. can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact without departing from the scope of the various described embodiments. The first contact and the second contact are both contacts, but they are not the same contact.

[0058] The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms "comprises," "comprising," "includes," "including," "has," "having," "has" and / or "having," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0059] As used herein, the term "if' can be construed to mean "when" or "in response to a determination" or "in response to the occurrence of a condition" or "in response to a determination that" or "in response to the occurrence of a condition" depending on the context.

[0060] As used herein, an AV system refers to an AV and real-time generated hardware, software, stored data, and real-time generated data that support operation of the AV. In embodiments, an AV system is incorporated within an AV. In embodiments, an AV system is distributed across multiple locations. For example, some software of an AV system is in a cloud computing environment similar to the cloud computing environment 300 described below in connection with FIG. 3 implemented in a cloud computing environment described below in connection with

[0061] In general, this document describes technology applicable to any vehicle with one or more autonomous capabilities, including fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles, respectively as Level 5, Level 4, and Level 3 vehicles (see SAE International Standard J3016: Classification and Definition of Degrees of Automation for Automated Driving Systems for Motor Vehicles on Roadways, incorporated by reference in its entirety for more detailed information on levels of vehicle autonomy). The technology described in this document is also applicable to partially autonomous vehicles and driver-assisted vehicles, as Level 2 and Level 1 vehicles (see SAE International Standard J3016: Classification and Definition of Degrees of Automation for Automated Driving Systems for Motor Vehicles on Roadways). In embodiments, one or more Level 1, Level 2, Level 3, Level 4, and Level 5 vehicle systems can automatically perform certain vehicle operations (e.g., steering, braking, and use of a map) under certain operating conditions based on processing of sensor inputs. The technology described in this document can benefit vehicles at each level from fully autonomous vehicles to human-operated vehicles.

[0062] With reference to FIG. 1 , the AV system 120 causes the AV 100 to operate along a trajectory 198 through the environment 190 to a destination 199 (sometimes referred to as a final location) while avoiding objects (e.g., natural obstacles 191, vehicles 193, pedestrians 192, cyclists, and other obstacles) and obeying road rules (e.g., operating rules or driving preferences).

[0063] In embodiments, the AV system 120 includes devices 101 for receiving and operating on operating commands from the computer processor 146. In embodiments, the computer processor 146 is similar to the processor 304 described below in connection with FIG. 3 Examples of devices 101 include a steering controller 102, a brake 103, a gear, an accelerator pedal or other acceleration control mechanism, a windshield wiper, a side door lock, a window control, and a turn indicator.

[0064] In embodiments, the AV system 120 includes sensors 121 for measuring or inferring properties of the state or condition of the AV 100, such as the AV’s position, linear and angular velocity and acceleration, and heading (e.g., the direction of the AV’s 100 front end). Examples of sensors 121 are GPS, and inertial measurement units (IMUs) that measure the vehicle’s linear acceleration and angular rate, wheel rate sensors for measuring or estimating wheel slip, wheel brake pressure or brake torque sensors, engine torque or wheel torque sensors, and steering angle and angular rate sensors.

[0065] In embodiments, the sensors 121 also include sensors for sensing or measuring properties of the AV’s environment. For example, monocular or stereo video cameras 122 in the visible, infrared, or thermal (or both) light spectrum, LiDARs 123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, speed sensors, temperature sensors, humidity sensors, and precipitation sensors.

[0066] In embodiments, the AV system 120 includes a data storage unit 142 and a memory 144 for storing machine instructions related to computer processors 146 or data collected by the sensors 121. In embodiments, the data storage unit 142 is similar to the ROM 308 or storage 310 described below. In embodiments, the memory 144 is similar to the main memory 306 described below. In embodiments, the data storage unit 142 and the memory 144 store historical, real-time, and / or predictive information about the environment 190. In embodiments, the stored information includes maps, driving performance, traffic congestion updates, or weather conditions. In embodiments, data related to the environment 190 is transmitted to the AV 100 through a communication channel from a remote database 134. FIG. 3

[0067] ​In embodiments, the AV system 120 includes communication devices 140 for transmitting measured or inferred properties of the state and conditions of other vehicles, such as position, linear and angular velocity, linear and angular acceleration, and linear and angular heading, to the AV 100. These devices include vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication devices, as well as devices for wireless communication through point-to-point or ad hoc networks, or both. In embodiments, the communication devices 140 communicate across the electromagnetic spectrum, including radio and optical communications, or other media (e.g., air and acoustic media). The combination of vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) communication (and, in some embodiments, one or more other types of communication) is sometimes referred to as vehicle-to-everything (V2X) communication. V2X communication is generally in compliance with one or more communication standards for communication with and between autonomous vehicles.

[0068] In embodiments, the communication devices 140 include a communication interface. For example, a wired, wireless, WiMAX, Wi-Fi, Bluetooth, satellite, cellular, optical, near field, infrared, or radio interface. The communication interface transmits data from the remote database 134 to the AV system 120. In embodiments, the remote database 134 is embedded in a cloud computing environment 200, as described in FIG. 2 The communication interface 140 transmits data collected from the sensors 121 or other data related to the operation of the AV 100 to the remote database 134. In embodiments, the communication interface 140 transmits information related to remote operation to the AV 100. In some embodiments, the AV 100 communicates with other remote (e.g., “cloud”) servers 136.

[0069] In embodiments, the remote database 134 also stores and transmits digital data (e.g., data storing road and street locations, etc.). This data is stored in the memory 144 on the AV 100, or transmitted from the remote database 134 to the AV 100 over a communication channel.

[0070] In embodiments, the remote database 134 stores and transmits historical information related to the driving properties (e.g., speed and acceleration rate distributions) of vehicles that have previously traveled along the trajectory 198 at similar times of day. In one implementation, this data can be stored in the memory 144 on the AV 100, or transmitted from the remote database 134 to the AV 100 over a communication channel.

[0071] The computing devices 146 located on the AV 100 generate control actions algorithmically based on real-time sensor data and a priori information, enabling the AV system 120 to perform its autonomous driving capabilities.

[0072] In embodiments, the AV system 120 includes a computer peripheral 132 connected to the computing device 146 for providing information and reminders to a user (e.g., an occupant or a remote user) of the AV 100 and receiving input from the user. In embodiments, the peripheral 132 is similar to the display 312, input device 314, and cursor controller 316 discussed below with reference to FIG. 3 The connection is wireless or wired. Any two or more of the interface devices can be integrated into a single device.

[0073] FIG. 2 An example "cloud" computing environment is illustrated. Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. In typical cloud computing systems, one or more large cloud data centers house the machines used to deliver the services provided by the cloud. Referring now to FIG. 2 , the cloud computing environment 200 includes cloud data centers 204a, 204b, and 204c interconnected by a cloud 202. The data centers 204a, 204b, and 204c provide cloud computing services for computer systems 206a, 206b, 206c, 206d, 206e, and 206f connected to the cloud 202.

[0074] The cloud computing environment 200 includes one or more cloud data centers. Generally, a cloud data center (e.g. FIG. 2 shown in FIG. 4A) refers to the physical arrangement of servers that make up a cloud (e.g. FIG. 2 shown in FIG. 4A or a particular portion of the cloud). For example, the servers are physically arranged in rooms, groups, rows, and racks in the cloud data center. The cloud data center has one or more areas that include one or more server rooms. Each room has one or more rows of servers, and each row includes one or more racks. Each rack includes one or more individual server nodes. In some implementations, the servers in an area, room, rack, and / or row are divided into groups according to the physical infrastructure requirements of the data center facility, including power, energy, heat, heat sources, and / or other requirements. In embodiments, the server nodes are similar to the computer systems described in FIG. 3 FIG. 4B. The data center 204a has many computer systems distributed across multiple racks.

[0075] The cloud 202 includes cloud data centers 204a, 204b, and 204c and networks and network resources (e.g., network devices, nodes, routers, switches, and network cables) used to connect and facilitate access by the computing systems 206a-f to cloud computing services. In embodiments, the network represents any combination of one or more local networks, wide-area networks, or internetworks connected through the use of wireline or wireless links deployed over ground or satellite connections. Data exchanged over the network is transmitted using a variety of network layer protocols, such as Internet Protocol (IP), Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM), Frame Relay, and the like. Moreover, in embodiments where the network represents a combination of multiple sub-networks, different network layer protocols are used on each underlying sub-network. In some embodiments, the network represents one or more internetworks (e.g., the public Internet, etc.).

[0076] The computing systems 206a-f or cloud computing service consumers are connected to the cloud 202 through network links and network adapters. In embodiments, the computing systems 206a-f are implemented as various computing devices, such as servers, desktops, laptops, tablets, smartphones, Internet of Things (IoT) devices, autonomous vehicles (including cars, drones, spacecraft, trains, buses, etc.), and consumer electronics. In embodiments, the computing systems 206a-f are implemented in or as part of other systems.

[0077] FIG. 3 An example computer system 300. In implementations, the computer system 300 is a special-purpose computing device. The special-purpose computing device is hard-wired to perform the techniques, or includes digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or includes one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices can also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. In the embodiments, the special-purpose computing device is a

[0078] In embodiments, the computer system 300 includes a bus 302 or other communication mechanism for communicating information, and a hardware processor 304 coupled with bus 302 for processing information. The hardware processor 304 is, for example, a general-purpose microprocessor. The computer system 300 also includes a main memory 306, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 302 for storing information and instructions to be executed by processor 304. In one implementation, the main memory 306 is used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 304. Such instructions can be stored or implemented in non-transitory storage media accessible to processor 304, such as storage media 310, when such instructions are stored in non-transitory storage media that is accessible to the processor 304, causing the computer system 300 to become a special purpose machine that is specially configured to perform the operations specified in the instructions.

[0079] In embodiments, the computer system 300 also includes a read only memory (ROM) 308 or other static storage device coupled to bus 302 for storing static information and instructions for processor 304. A storage device 310, such as a magnetic disk, optical disk, solid-state drive, or three-dimensional cross-point memory, is provided and coupled to bus 302 for storing information and instructions.

[0080] In embodiments, the computer system 300 is coupled via bus 302 to a display 312, such as a cathode ray tube (CRT), liquid crystal display (LCD), plasma display, light emitting diode (LED) display, or organic light emitting diode (OLED) display, for displaying information to a computer user. An input device 314, including alphanumeric and other keys, is coupled to bus 302 for communicating information and command selections to processor 304. Another type of user input device is cursor control 316, such as a mouse, a trackball, a touch display, or cursor direction keys for communicating direction information and command selections to processor 304 and for

[0081] According to one embodiment, the techniques herein are performed by computer system 300 in response to processor 304 executing one or more sequences of one or more instructions contained in main memory 306. Such instructions can be read into main memory 306 from another storage medium, such as storage device 310. Execution of the sequences of instructions contained in main memory 306 causes processor 304 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry can be used in place of or in combination with software instructions.

[0082] The term "storage media" as used herein refers to any non-transitory media that store data and / or instructions that cause a machine to operate in a specific fashion. Such storage media include non-volatile media and / or volatile media. Non-volatile media include, for example, optical or magnetic disks, such as storage device 310. Volatile media include dynamic memory, such as main memory 306. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, or any other memory chip or cartridge.

[0083] Storage media are distinct from, but can be used in combination with, transmission media. Transmission media participate in transferring information between storage media. For example, transmission media include coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 302. Transmission media can also take the form of acoustic or light waves, such as those generated during radio frequency and infrared data communications.

[0084] In embodiments, various forms of media are involved in carrying one or more sequences of one or more instructions to processor 304 for execution. For example, the instructions can initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer loads the instructions into its dynamic memory and sends the instructions over a telephone line using a modem. A local modem in computer system 300 receives the data on the telephone line and uses an infrared transmitter to convert the data to an infrared signal. An infrared detector receives the data carried in the infrared signal and appropriate circuitry places the data on bus 302. Bus 302 carries the data to main memory 306, from which processor 304 retrieves and executes the instructions. The instructions received by main memory 306 can optionally be stored on storage device 310 either before or after execution by processor 304.

[0085] Computer system 300 also includes a communication interface 318 coupled to bus 302. Communication interface 318 provides a two-way data communication coupling to a network link 320 that is connected to a local network 322. For example, communication interface 318 is a integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 318 is a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links are also implemented in some implementations. In any such implementation, communication interface 318 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0086] Network link 320 typically provides data communication through one or more networks to other data devices. For example, network link 320 provides a connection through local network 322 to a host computer 324 or to cloud data centers or devices operated by an Internet Service Provider (ISP) 326. ISP 326 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the "Internet" 328. Local network 322 and Internet 328 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 320 and through communication interface 318, which carry the digital data to and from computer system 300, are example forms of transmission media for these digital data streams. In embodiments, network 320 includes cloud 202 or a portion of cloud 202 described above.

[0087] Computer system 300 sends messages and receives data, including program code, through the network(s), network link 320 and communication interface 318. In embodiments, computer system 300 receives code for processing. The received code is executed by processor 304 as it is received, and / or stored in storage device 310, or other non-volatile storage for later execution.

[0088] AUTONOMOUS VEHICLE ARCHITECTURE

[0089] FIG. 4 An example architecture 400 is shown for an autonomous vehicle (e.g., AV 100). FIG. 1 Architecture 400 includes a perception module 402 (sometimes referred to as perception circuitry), a planning module 404 (sometimes referred to as planning circuitry), a control module 406 (sometimes referred to as control circuitry), a localization module 408 (sometimes referred to as localization circuitry), and a database module 410 (sometimes referred to as database circuitry). The modules play a role in the operation of AV 100. Collectively, modules 402, 404, 406, 408, and 410 can be part of AV system 120 shown. In some embodiments, any of modules 402, 404, 406, 408, and 410 are a combination of computer software (e.g., executable code stored on a computer-readable medium) and computer hardware (e.g., one or more microprocessors, microcontrollers, application-specific integrated circuits [ASICs], hardware memory devices, other types of integrated circuits, other types of computer hardware, or a combination of any or all of these). FIG. 1 Architecture 400 includes a perception module 402 (sometimes referred to as perception circuitry), a planning module 404 (sometimes referred to as planning circuitry), a control module 406 (sometimes referred to as control circuitry), a localization module 408 (sometimes referred to as localization circuitry), and a database module 410 (sometimes referred to as database circuitry). The modules play a role in the operation of AV 100. Collectively, modules 402, 404, 406, 408, and 410 can be part of AV system 120 shown. In some embodiments, any of modules 402, 404, 406, 408, and 410 are a combination of computer software (e.g., executable code stored on a computer-readable medium) and computer hardware (e.g., one or more microprocessors, microcontrollers, application-specific integrated circuits [ASICs], hardware memory devices, other types of integrated circuits, other types of computer hardware, or a combination of any or all of these).

[0090] In use, the planning module 404 receives data representing the destination 412 and determines data representing the trajectory 414 (sometimes called the route) that the AV100 can travel to reach (e.g., arrive at) the destination 412. In order for the planning module 404 to determine the data representing the trajectory 414, the planning module 404 receives data from the sensing module 402, the positioning module 408, and the database module 410.

[0091] The sensing module 402 is used, for example, as follows FIG. 1 One or more sensors 121 are shown to identify nearby physical objects. The objects are classified (e.g., grouped into types such as pedestrians, bicycles, cars, traffic signs, etc.), and a scene description including the classified objects 416 is provided to the planning module 404.

[0092] The planning module 404 also receives data representing the location 418 of the AV from the positioning module 408. The positioning module 408 determines the location of the AV by using data from the sensor 121 and data (e.g., geographic data) from the database module 410. For example, the positioning module 408 uses data from GNSS (Global Navigation Satellite System) sensors and geographic data to calculate the longitude and latitude of the AV. In embodiments, the data used by the positioning module 408 includes high-precision maps with lane geometry properties, maps describing road network connectivity properties, maps describing lane physical properties (such as traffic speed, traffic volume, number of vehicle and bicycle lanes, lane width, lane traffic direction, or lane marking type and location, or combinations thereof), and maps describing spatial locations of road features (such as intersections, traffic signs, or various types of other traffic signals).

[0093] The control module 406 receives data representing trajectory 414 and data representing AV position 418, and operates the AV's control functions 420a-420c (e.g., steering, throttle, braking, ignition) in a manner that will cause the AV 100 to travel along trajectory 414 to reach destination 412. For example, if trajectory 414 includes a left turn, the control module 406 will operate the control functions 420a-420c in such a way that the steering angle of the steering function will cause the AV 100 to turn left, and the throttle and brake will cause the AV 100 to pause before turning and wait for passing pedestrians or vehicles.

[0094] AUTONOMOUS VEHICLE INPUT

[0095] FIG. 5 The sensing module 402 is shown. FIG. 4 The inputs used are 502a-502d (e.g., FIG. 1The inputs 502a-502d and outputs 504a-504d are examples of the sensors 121) and outputs 504a-504d (e.g., sensor data) shown in FIG. 1. One input 502a is a LiDAR (light detection and ranging) system (e.g., FIG. 1 The LiDAR 123 shown). LiDAR is a technology that uses light (e.g., a beam of light such as infrared light) to obtain data about physical objects in its line of sight. The LiDAR system produces LiDAR data as output 504a. For example, the LiDAR data is a collection of 3D or 2D points (also known as a point cloud) used to construct a representation of the environment 190.

[0096] Another input 502b is a RADAR (Radar) system. RADAR is a technology that uses radio waves to obtain data about physical objects in the vicinity. RADAR can obtain data about objects that are not in the line of sight of the LiDAR system. The RADAR system 502b produces RADAR data as output 504b. For example, the RADAR data is one or more radio frequency electromagnetic signals used to construct a representation of the environment 190.

[0097] Another input 502c is a camera system. The camera system uses one or more cameras (e.g., digital cameras that use a light sensor such as a charge-coupled device [CCD]) to acquire information about physical objects in the vicinity. The camera system produces camera data as output 504c. The camera data is typically in the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). In some examples, the camera system has multiple independent cameras, for example, for the purpose of stereoscopic imagery (stereo vision), which enables the camera system to perceive depth. Although the objects perceived by the camera system are described here as being in the “vicinity,” this is relative to the AV. In use, the camera system can be configured to “see” objects that are far away (e.g., up to 1 kilometer or more in front of the AV). Thus, the camera system can have features such as sensors and lenses that are optimized for perceiving objects that are far away.

[0098] Another input 502d is a traffic light detection (TLD) system. The TLD system uses one or more cameras to obtain information about traffic lights, street signs, and other physical objects that provide visual navigation information. The TLD system produces TLD data as output 504d. The TLD data often takes the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). The TLD system differs from systems that include cameras in that the TLD system uses a camera with a wide field of view (e.g., using a wide-angle lens or a fisheye lens) to obtain information about as many physical objects that provide visual navigation information as possible, so that the AV 100 has access to all relevant navigation information provided by those objects. For example, the TLD system can have a field of view of about 120 degrees or more.

[0099] In some embodiments, the outputs 504a-504d are combined using sensor fusion techniques. Thus, the individual outputs 504a-504d are provided to other systems of the AV 100 (e.g., to the planning module 404 as shown in FIG. 4 The combined output can be provided to other systems in the form of a single combined output or multiple combined outputs that are of the same type (e.g., use the same combination technique or combine the same outputs or both) or different types (e.g., use different respective combination techniques or combine different respective outputs or both). In some embodiments, early fusion techniques are used. Early fusion techniques are characterized by combining the outputs, after which one or more data processing steps are applied to the combined output. In some embodiments, late fusion techniques are used. Late fusion techniques are characterized by combining the outputs after one or more data processing steps are applied to the individual outputs.

[0100] FIG. 6 An example of a LiDAR system 602 (e.g., the input 502a shown in FIG. 5 The LiDAR system 602 emits light 604a-604c from a light emitter 606 (e.g., a light excitation emitter). The light emitted by the LiDAR system is often not in the visible spectrum; for example, infrared light is often used. Some of the emitted light 604b encounters a physical object 608 (e.g., a vehicle) and reflects back to the LiDAR system 602. (Light emitted from the LiDAR system often does not penetrate physical objects, e.g., physical objects in solid form.) The LiDAR system 602 also has one or more light detectors 610 for detecting the reflected light. In embodiments, one or more data processing systems associated with the LiDAR system generate an image 612 representing the field of view 614 of the LiDAR system. The image 612 includes information representing the boundary 616 of the physical object 608. In this way, the image 612 is used to determine the boundary 616 of one or more physical objects in the vicinity of the AV.

[0101] FIG. 7 The LiDAR system 602 in operation is shown. In the context shown in this figure, the AV 100 receives camera system output 504c in the form of an image 702 and LiDAR system output 504a in the form of LiDAR data points 704. In use, the data processing system of the AV 100 compares the image 702 to the data points 704. In particular, a physical object 706 that is identified in the image 702 is also identified in the data points 704. In this way, the AV 100 perceives the boundaries of the physical object based on the contours and density of the data points 704.

[0102] FIG. 8 Additional details of the operation of the LiDAR system 602 are shown. As described above, the AV 100 detects the boundaries of physical objects based on the characteristics of the data points detected by the LiDAR system 602. As shown, a flat object such as the ground 802 will reflect light 804a-804d emitted from the LiDAR system 602 in a consistent manner. In other words, since the LiDAR system 602 emits light using a consistent spacing, the ground 802 will reflect the light back to the LiDAR system 602 at the same consistent spacing. As the AV 100 travels over the ground 802, in the absence of anything blocking the road, the LiDAR system 602 will continue to detect light reflected by the next valid ground point 806. However, if an object 808 blocks the road, the light 804e-804f emitted by the LiDAR system 602 will be reflected from points 810a-810b in a manner that does not conform to the expected consistent manner. From this information, the AV 100 can determine that the object 808 is present. FIG. 8

[0103] PATH PLANNING

[0104] A block diagram 900 showing the relationship between the inputs and outputs of the planning module 404 (e.g., as shown in FIG. 4) is shown. Generally, the output of the planning module 404 is a route 902 from a start point 904 (e.g., a source location or initial location) to an end point 906 (e.g., a destination or final location). The route 902 is generally defined by one or more route segments. For example, a route segment refers to a distance to be traveled over at least a portion of a street, road, highway, lane, or other physical area suitable for automobile travel. In some examples, for example, if the AV 100 is an off-road vehicle such as a four-wheel drive (4WD) or all-wheel drive (AWD) car, SUV, or small truck, the route 902 includes “off-road” route segments such as an unpaved road or open field. FIG. 9 FIG. 4

[0105] ​​In addition to route 902, the planning module also outputs lane-level route planning data 908. Lane-level route planning data 908 is used to navigate segments of route 902 based on conditions at a specific time. For example, if route 902 includes a multi-lane highway, lane-level route planning data 908 includes trajectory planning data 910, which AV 100 can use to select a lane from the multiple lanes, for example, based on factors such as whether an exit is nearby, whether other vehicles are present in more than one of the lanes, or other factors that change over a period of minutes or less. Similarly, in some implementations, lane-level route planning data 908 includes a speed constraint 912 specific to a segment of route 902. For example, if the segment includes pedestrians or unexpected traffic, speed constraint 912 can limit AV 100 to a slower speed than expected, such as a speed limit based on the segment's speed limit data.

[0106] In this embodiment, the input to the planning module 404 includes (e.g., from...) FIG. 4 The database module 410 shown contains database data 914 and current location data 916 (for example, FIG. 4 The AV location 418 shown), destination data 918 (for example, for...) FIG. 4 The destination 412 and object data 920 (as shown) are shown in the figure. FIG. 4 The perception module 402 shown perceives classified objects 416. In some embodiments, database data 914 includes rules used during planning. Rules are specified using a formal language (e.g., Boolean logic). In any given situation encountered by AV 100, at least some of these rules will apply to that situation. A rule applies to a given situation if it has conditions satisfied based on information available to AV 100 (e.g., information related to the surrounding environment). Rules can have priorities. For example, a rule "move to the leftmost lane if the road is a highway" can have a lower priority than "move to the rightmost lane if the exit is within one mile."

[0107] FIG. 10 This is illustrated in path planning (e.g., by planning module 404). FIG. 4 The directed graph used is 1000. Typically, such as... FIG. 10 The directed graph 1000 shown is used to determine any path between a starting point 1002 and an ending point 1004. In the real world, the distance separating the starting point 1002 and the ending point 1004 may be relatively large (e.g., in two different urban areas) or relatively small (e.g., two intersections adjacent to a city block or two lanes of a multi-lane road).

[0108] In embodiments, the directed graph 1000 has nodes 1006a-1006d representing different locations between the start 1002 and end 1004 that the AV 100 can occupy. In some examples, the nodes 1006a-1006d represent road segments, e.g., when the start 1002 and end 1004 represent different metropolitan areas. In some examples, the nodes 1006a-1006d represent different positions on a road, e.g., when the start 1002 and end 1004 represent different locations on the same road. In this way, the directed graph 1000 includes information at different levels of granularity. In embodiments, a directed graph with high granularity is also a subgraph of another directed graph with greater scale. For example, a directed graph whose start 1002 and end 1004 are far apart (e.g., many miles apart) has most of its information at low granularity and is based on stored data, but the directed graph also includes some high granularity information for a portion of the directed graph representing physical locations in the field of view of the AV 100.

[0109] The nodes 1006a-1006d are different from the objects 1008a-1008b, which cannot overlap with the nodes. In embodiments, at low granularity, the objects 1008a-1008b represent areas that a car cannot drive through, e.g., areas without streets or roads. At high granularity, the objects 1008a-1008b represent physical objects in the field of view of the AV 100, e.g., other cars, pedestrians, or other entities with which the AV 100 cannot share physical space. In embodiments, some or all of the objects 1008a-1008b are static objects (e.g., objects that do not change position, such as streetlights or utility poles) or dynamic objects (e.g., objects that can change position, such as pedestrians or other cars).

[0110] Nodes 1006a-1006d are connected by edges 1010a-1010c. If two nodes 1006a- 1006b are connected by an edge 1010a, then an AV 100 can travel between one node 1006a and the other node 1006b, for example, without having to travel to an intermediate node before reaching the other node 1006b. (When referring to an AV 100 traveling between nodes, it means that the AV 100 travels between two physical locations represented by the respective nodes.) Edges 1010a-1010c are typically bidirectional, in the sense that an AV 100 can travel from a first node to a second node, or from the second node to the first node. In embodiments, edges 1010a-1010c are unidirectional, in the sense that an AV 100 can travel from a first node to a second node, but the AV 100 cannot travel from the second node to the first node. Edges 1010a-1010c are unidirectional in cases where they represent, for example, a single lane of a one-way street, a street, a road, or a highway, or other features that can only be traversed in one direction due to legal or physical constraints.

[0111] In embodiments, planning module 404 uses directed graph 1000 to identify a path 1012 consisting of nodes and edges between a start point 1002 and an end point 1004.

[0112] Edges 1010a-1010c have associated costs 1014a-1014b. Costs 1014a-1014b are values that represent resources that will be spent if the AV 100 chooses that edge. A typical resource is time. For example, if one edge 1010a represents a physical distance that is twice that of another edge 1010b, then the associated cost 1014a of the first edge 1010a can be twice that of the associated cost 1014b of the second edge 1010b. Other factors that affect time include expected traffic, number of intersections, speed limits, etc. Another typical resource is fuel economy. Two edges 1010a-1010b can represent the same physical distance, but one edge 1010a requires more fuel than the other edge 1010b, for example, due to road conditions, expected weather, etc.

[0113] When planning module 404 identifies a path 1012 between a start point 1002 and an end point 1004, planning module 404 typically chooses a path that is optimized for cost, e.g., a path that has the minimum total cost when the individual costs of the edges are added together.

[0114] AUTONOMOUS VEHICLE CONTROL

[0115] FIG. 11 is shown (e.g., as FIG. 4A block diagram 1100 of inputs and outputs of the control module 406 (shown) is shown. The control module operates in accordance with a controller 1102, which includes, for example, one or more processors (e.g., one or more computer processors such as microprocessors or microcontrollers or both) similar to the processor 304; short- and / or long-term data storage (e.g., memory random access memory or flash memory or both) similar to the main memory 306, ROM 308, and storage 310; and instructions stored in memory that, when executed (e.g., by the one or more processors), perform the operations of the controller 1102.

[0116] In embodiments, the controller 1102 receives data representing a desired output 1104. The desired output 1104 generally includes a velocity, such as a speed and a heading. The desired output 1104 can be based, for example, on data received from (e.g., as described above with respect to) the planning module 404 (shown). FIG. 4 The controller 1102 generates data that can be used as a throttle input 1106 and a steering input 1108 in accordance with the desired output 1104. The throttle input 1106 represents, for example, an engagement of a throttle (e.g., an acceleration control) of the AV 100 by engaging a steering pedal or engaging another throttle control to achieve a magnitude of the desired output 1104. In some examples, the throttle input 1106 also includes data that can be used to engage a brake (e.g., a deceleration control) of the AV 100. The steering input 1108 represents a steering angle, such as an angle at which a steering control (e.g., a steering wheel, a steering angle actuator, or other functionality for controlling a steering angle) of the AV should be positioned to achieve the desired output 1104.

[0117] In embodiments, the controller 1102 receives feedback used in adjusting the inputs provided to the throttle and steering. For example, if the AV 100 encounters an interference 1110, such as a hill, the measured speed 1112 of the AV 100 drops below the desired output speed. In embodiments, any measured outputs 1114 are provided to the controller 1102 so that adjustments are made, for example, based on a difference 1113 between the measured speed and the desired output. The measured outputs 1114 include a measured position 1116, a measured velocity 1118 (including a speed and a heading), a measured acceleration 1120, and other outputs measurable by sensors of the AV 100.

[0118] In embodiments, information about the disturbance 1110 is detected in advance, e.g., by sensors such as cameras or LiDAR sensors, and the information is provided to the predictive feedback module 1122. The predictive feedback module 1122 then provides information to the controller 1102 that the controller 1102 can use to adjust accordingly. For example, if the sensors of the AV 100 detect (“see”) a hill, the controller 1102 can use that information to prepare to engage the gas at the appropriate time to avoid a significant slowdown.

[0119] FIG. 12 A block diagram 1200 showing inputs, outputs, and components of the controller 1102. The controller 1102 has a speed analyzer 1202 that influences the operation of a throttle / brake controller 1204. For example, the speed analyzer 1202 instructs the throttle / brake controller 1204 to use the throttle / brakes 1206 to accelerate or to decelerate, e.g., based on feedback received by the controller 1102 and processed by the speed analyzer 1202.

[0120] The controller 1102 also has a lateral tracking controller 1208 that influences the operation of a steering wheel controller 1210. For example, the lateral tracking controller 1208 instructs the steering wheel controller 1210 to adjust the position of a steering angle actuator 1212, e.g., based on feedback received by the controller 1102 and processed by the lateral tracking controller 1208.

[0121] The controller 1102 receives a number of inputs that are used to determine how to control the throttle / brakes 1206 and the steering angle actuator 1212. A planning module 404 provides information that the controller 1102 uses, e.g., to select a heading for the AV 100 to start operating and to determine which road segment to drive through when the AV 100 reaches an intersection. A localization module 408 provides information that describes the current location of the AV 100 to the controller 1102, e.g., so that the controller 1102 can determine whether the AV 100 is at a location that is expected based on the way that the throttle / brakes 1206 and the steering angle actuator 1212 are being controlled. In embodiments, the controller 1102 receives information from other inputs 1214, e.g., information received from a database, a computer network, etc.

[0122] MULTIPLE LiDAR DEVICES

[0123] FIG. 13 An AV 1300 is shown with two LiDAR devices 1302, 1304. The AV 1300 is FIG. 1 An example of the AV 100 shown. The LiDAR devices 1302, 1304 are FIG. 5 An example of the LiDAR system 502a shown. As above for the FIG. 5-8In more detail, a LiDAR device emits light (e.g., electromagnetic radiation in the ultraviolet, infrared, or laser spectrum, or any other kind of electromagnetic radiation) that illuminates points on physical objects (e.g., other vehicles, pedestrians, street signs, etc.) near the LiDAR device and is reflected back to the LiDAR device, thereby detecting (e.g., observing) the illuminated points. (Because light travels so fast, the time at which a point is illuminated and the time at which the point is detected are indistinguishable.) For example, as shown in FIG. 13 FIG. 13, each LiDAR device 1302, 1304 emits light 1306, 1308 that is reflected from a nearby vehicle 1310 (e.g., a vehicle on the same road as AV 1300). Although two LiDAR devices 1302, 1304 are shown here, the techniques described herein can also be applied to structures with three or more LiDAR devices.

[0124] Each of the two LiDAR devices 1302, 1304 is located at a different position on AV 1300. In embodiments, one of the devices 1302 is attached (e.g., welded, adhered, or mounted) to one position 1312, and the other device 1304 is attached to another position 1314. Although some attachment techniques (e.g., welding) are semi-permanent and unlikely to change during the lifetime of AV 1300, other attachment techniques (e.g., magnetic attachment) enable the LiDAR devices 1302, 1304 to be removed (e.g., for maintenance or replacement) or moved to different positions at different times.

[0125] Because the two LiDAR devices 1302, 1304 are located at different positions, the data generated by the two devices is integrated for use by AV 1300. As described above for FIG. 5 LiDAR data takes the form of a collection of 3D or 2D points, known as a point cloud. Each point in a point cloud represents an illuminated point on an object (e.g., nearby vehicle 1310) in environment 190 and has a set of coordinates that define its position in the cloud. When an illuminated point is detected, it is added to the point cloud. As described below for FIG. 14More specifically, the LiDAR device assigns the point to a set of coordinates in the point cloud. Furthermore, when multiple LiDAR devices are used simultaneously, each LiDAR device can have its own coordinate system. Therefore, in order to use data from two LiDAR devices, the coordinate systems are integrated. One technique for integrating coordinate systems includes using a common reference point. For example, the coordinates of each point can be defined based on the position of these points relative to a specific location 1320 on AV 1300. Thus, when one LiDAR device 1302 detects point 1316 and another LiDAR device 1304 detects point 1318, the two points 1316 and 1318 will have coordinates defined relative to the same location 1320 on AV 1300.

[0126] If as FIG. 13 As shown, points 1316 and 1318 correspond to approximately the same position relative to AV 1300, meaning they will have approximately the same coordinates. In other words, points 1316 and 1318 will be located at the same position in the point cloud, which includes, for example, the same distance 1324 relative to position 1320 used as a common reference point. Due to the difference in the relative spatial positions of the two LIDAR devices 1302 and 1304, points 1316 and 1318 describe the same position from two different vantage points.

[0127] FIG. 13 The exemplary location 1320 shown as a common reference point is the center of the rear axis 1322, but any location on the AV 1300 can be used. As described in more detail below, coordinate system integration is performed to merge the point clouds generated by the various LiDAR devices 1302, 1304.

[0128] FIG. 14 LiDAR devices 1302 and 1304 are shown in more detail. As described above, each LiDAR device has a different physical position 1312 and 1314 on AV 1300. In an embodiment, the starting angle 1402 of one LiDAR device 1302 is different from the starting angle 1404 of another LiDAR device 1304. In use, the LiDAR device spins rapidly to illuminate objects within the entire field of view of the LiDAR device (e.g., all objects in a 360-degree field of view). As described above for... FIG. 6 The LiDAR device typically has one or more emitters 606. FIG. 14 For the purposes of the examples shown, LiDAR devices 1302 and 1304 will be described as having a single emitter, but the techniques described herein are applicable to LiDAR devices having multiple emitters.

[0129] The start angles 1402, 1404 are defined based on the position of the emitters 1406, 1408 of the LiDAR devices 1302, 1304. In embodiments, the first LiDAR device 1302 is configured with a start angle 1402 of 0 degrees, i.e., when the emitter 1406 is at 0 degrees, the LiDAR device defines the completion of a full rotation and the start of a new full rotation. The second LiDAR device 1304 is configured with a start angle 1404 of 90 degrees, i.e., when the emitter 1408 is at 90 degrees, the LiDAR device defines the completion of a full rotation and the start of a new full rotation. In embodiments, when the power of the respective LiDAR device 1302, 1304 is turned on, the emitters 1406, 1408 return to the respective start angles 1402, 1404.

[0130] In embodiments, one LiDAR device 1302 is configured with a frequency 1410 that is different from the frequency 1412 of the other LiDAR device 1304. The frequency of a LiDAR device corresponds to its rotation rate. For example, if the frequency of a LiDAR device is 10 hertz, then the emitter of the LiDAR device completes ten full rotations per second. In other words, since the period is the inverse of the frequency, the period of the LiDAR device is 1 / 10 of a second.

[0131] Since the LiDAR devices 1302, 1304 can have different start angles 1402, 1404 and / or different frequencies 1410, 1412, the LiDAR devices 1302, 1304 will typically illuminate any particular point at different times. For example, while the first LiDAR device 1302 is rotating (e.g., at its frequency 1410), the first LiDAR device 1302 will illuminate a point at a particular location 1414 at a first time 1416, while the second LiDAR device 1304 will illuminate the point at the location 1414 at a second time 1418 while the second LiDAR device 1304 is rotating (e.g., at its frequency 1412).

[0132] The time at which a point is illuminated depends on the start angle 1402, 1404 and the start frequency 1410, 1412. In embodiments, the multiple LiDAR devices have intentionally staggered start angles. After a point is detected by one LiDAR device, the other LiDAR device(s) will detect another point at the same location before the rotation period of the LiDAR device is completed. In this way, the motion of an object can be observed with better temporal accuracy.

[0133] In an embodiment, if both of the starting frequencies 1410, 1412 are 10 hertz, one starting angle 1402 is 0 degrees, and the other starting angle 1404 is 90 degrees, the first LiDAR device 1302 will illuminate a point at a particular location 1414 1 / 40 of a second before or after the second LiDAR device 1304. This is because the second LiDAR device 1304 starts a full rotation at a quarter of the rotation from the first LiDAR device 1302 (90 divided by 360), and one rotation lasts 1 / 10 of a second. In other words, if the frequencies of the LiDAR devices 1302, 1304 are the same, the difference is calculated by multiplying the period by the angular difference in degrees and dividing by 360. Other angular units (e.g., radians) can also be used.

[0134] In an embodiment, if the frequency 1410 of the first LiDAR device 1302 is 10 hertz, the frequency 1412 of the second LiDAR device 1304 is 20 hertz, and both of the starting angles 1402 and 1404 are the same (e.g., both are 0 degrees), the second LiDAR device 1304 will illuminate the particular location 1414 at a frequency that is equivalent to twice the frequency of the first LiDAR device 1302 because the frequency 1412 of the second LiDAR device 1304 is twice as high as the frequency 1410 of the first LiDAR device 1302. In other words, by the time the first LiDAR device 1302 has completed a full rotation and illuminated a point at the location 1414, the second LiDAR device has illuminated a point at that location 1414 twice.

[0135] Changing the frequencies and / or starting angles is beneficial to the ability of the AV 1300 to perceive its surroundings 190. For example, when multiple starting angles are used, a point at a certain location in the surroundings 190 will be illuminated multiple times during the time period of a rotation, so the multiple LiDAR devices detect the point at that location more frequently than if only one LiDAR device were used. In contrast, if the same starting angle and frequency are used, the multiple LiDAR devices will detect the point at the same location at approximately the same time. The ability to detect the point at the same location more frequently enables the AV 1300 to perceive changes in the surroundings 190 (e.g., movement of objects) more accurately in response to rapid changes in the surroundings 190 (e.g., high speed, high density traffic, or crowds of pedestrians). Moreover, because multiple LiDAR devices at different locations are used, if one LiDAR device is occluded (e.g., occludes another object of interest by an intervening object), other LiDAR devices can not be occluded and can detect a point at that location or object of interest. For example, the intervening object can be another vehicle, and the object of interest can be a pedestrian walking behind the other vehicle; detecting movement of the pedestrian can be important to ensure that the AV 1300 will avoid the path of the pedestrian.

[0136] As noted above, if each LiDAR device 1302, 1304 generates its own point cloud, then the two point clouds 1316 and 1318 are integrated according to techniques described below, e.g., for use by the perception module 402. FIG. 4 ) uses. The integrated point cloud includes points from both point clouds 1316 and 1318. In embodiments, the two point clouds 1316 and 1318 are integrated or merged or joined or combined together as soon as each LiDAR device 1302 and 1304 respectively generates the point clouds 1316 and 1318. In embodiments, the two point clouds 1316 and 1318 are merged after each LiDAR device 1302 and 1304 respectively completes generating the point clouds 1316 and 1318. One technique to integrate the point clouds is to normalize the coordinates of each point to a common reference point, e.g., a particular location 1320 on the AV 1300. In this way, the integrated point cloud (sometimes referred to as a merged point cloud) is defined using the particular location 1320 as the origin (e.g., coordinates 0, 0, 0 on Cartesian x-y-z axes, or an origin defined using polar coordinates as described in more detail below). Points from both point clouds 1316 and 1318 are converted to the integrated point cloud by normalizing the coordinates to the common origin. In other words, if one point from one cloud and another point from the other cloud are detected at approximately the same location in the environment 190, then the coordinates of these points are changed so that the points have approximately the same coordinates, and thus occupy approximately the same location in the integrated point cloud.

[0137] One technique to integrate the point clouds involves associating each point with a timestamp (e.g., the time at which the point was detected), and using these timestamps to determine how points from different point clouds are spatially related to each other. In embodiments, each LiDAR device 1302, 1304 assigns a timestamp to each point when the point is detected. Typically, the timestamps of points are used to coordinate between two or more LiDARs that have different start angles and / or different frequencies. One LiDAR can observe a point corresponding to a given location at time t = x, while a second LiDAR can observe a point corresponding to the same location at time t = y. The relationship between x and y can be evaluated based on information about the start point and frequency.

[0138] As FIG. 15As shown, in an embodiment, the LiDAR devices 1302 detect a set of points located near each other at the same time. The set of points located near each other detected at the same time is referred to as a block 1502. Since a typical LiDAR device emits light in pulses, the block 1502 represents points detected on a particular pulse of the light emitter of the LiDAR device. The time at which the light emitter of the LiDAR device emits light is used to determine a timestamp to be assigned to the points of the block 1502. There is a timestamp assigned to the first emission of a diode in a particular block. Subsequent emissions from other diodes in the block can be calculated based on inherent LiDAR properties, such as azimuth angle. In an embodiment, an offset is assigned to the points of the block 1502, for example, to compensate for position differences between the points of the block 1502. In an embodiment, the offset is a mechanical property of the LiDAR that is predetermined by the manufacturer during manufacturing of the LiDAR. The spatial position of each block 1502 is known based on the angle of the laser light emitter of the LiDAR device 1302 (sometimes referred to as azimuth angle or angle on the horizontal plane) at the time the block 1502 is detected.

[0139] In an example scenario, one of the LiDAR devices 1302 detects a block 1502 of points at a first time 1512 and another LiDAR device 1304 detects a block 1504 of points at a second time 1514. For example, if the LiDAR devices 1302, 1304 have the same frequency of 10 hertz, but the start angle differs by 180°, then the first time 1512 is ti = 100 ms and the second time 1514 is t2 = 150 ms. In this example scenario, the two blocks 1502, 1504 represent points detected at the same location on the object. Since the two LiDAR devices 1302, 1304 are at different locations on the AV 1300, the two blocks 1502, 1504 will be detected at different angles (azimuth angles) relative to the respective detecting LiDAR devices 1302, 1304. Since the blocks 1502, 1504 have different detection times and different azimuth angles, additional processing is used to determine that the points in the blocks 1502, 1504 are the same location.

[0140] Continuing with the same example scenario, the perception module 402( FIG. 4 ) receives the blocks 1502, 1504, the timestamps (e.g., based on the first time 1512 and the second time 1514), the frequency, and the start angle to generate consolidated point cloud information 1506 (e.g., containing points from both blocks 1502, 1504). The coordinates of all points in the consolidated point cloud information 1506 are represented relative to the same origin (e.g., FIG. 13 the location 1320 shown).

[0141] As an example of the type of calculation performed, the sensing module 402 uses the starting angle of the first LiDAR device 1302 to determine the position of the first point block detected by the LiDAR device (e.g., the point block detected at t=0). For example, if the starting angle of the first LiDAR device corresponds to the position of the emitter (0 degrees), the sensing module 402 can determine that a point in the first block is at the position corresponding to 0 degrees, and calculate the coordinates relative to the origin of the integrated point cloud using the known position of the LiDAR device 1302. If the point has a timestamp indicating that the point was detected at t=50ms and the frequency is 10 Hz, then since the emitter needs 50ms to complete half of its rotation, the sensing module 402 can determine that the point was detected when the emitter is at an angle (azimuth) of 180 degrees. Similar calculations can be performed using points from another LiDAR device 1304.

[0142] FIG. 16 The components of a system for generating an integrated point cloud 1600 are shown. Each LiDAR device 1602, 1612 has processors 1604, 1614 (e.g., microprocessors, microcontrollers), each configured using corresponding start angles 1606, 1616 and frequencies 1608, 1618. In use, the LiDAR devices 1602, 1612 generate point clouds 1622, 1624 (e.g., ...) received by processor 1626. FIG. 4 (The implementation or components of the sensing module 402 shown). Points in point clouds 1622, 1624 are associated with timestamp data 1628, 1630. Processor 1626 uses start angles 1606, 1616, frequencies 1608, 1618, and timestamp data 1628, 1630 to generate integrated point cloud 1600. In an embodiment, LiDAR devices 1602, 1612 are synchronized, for example, operating according to a common time reference and / or having synchronized clocks. In an embodiment, processors 1604, 1614 share a common clock 1632, such that the timestamps of these processors are generated from a common reference point. In other words, a point with timestamp t=x generated by one of the LiDAR devices will be detected at the same time as a point with timestamp t=x generated by another synchronized(one or more) LiDAR device(s). In an embodiment, processor 1626 configures start angles 1606, 1616 and / or frequencies 1608, 1618.

[0143] FIG. 17A and 17BThe diagram shows a representation of the integrated point cloud 1700 in the form of a set of polar voxels 1702a-d. In this embodiment, points detected by multiple LiDAR devices are modeled as polar voxels, and the points detected by the LiDAR devices are placed at corresponding coordinates within the polar voxels. A voxel (sometimes called a volume element) is a unit of volume in a data structure representing three-dimensional space. Point 1704 within polar voxel 1702a is defined by polar coordinates, compared to Cartesian coordinates used for Cartesian voxels. Polar coordinates have three values: azimuth (sometimes called horizontal angle), height (sometimes called vertical angle), and radius. FIG. 17A and 17B Point 504 shown has an azimuth angle 1706 and a height 1708 defined relative to an origin (as in the example above) at position 1320 at the rear axle center of AV 1300. Similarly, the radius 1710 of this point is the distance from position 1320. In this embodiment, the data structure representing polar voxels 1702a-d is provided by sensing module 402 ( FIG. 4 Maintain, and is FIG. 16 The implementation of the integrated point cloud 1600 is shown.

[0144] The use of octrees enables the perception module 402 (or other data processing system) to segment the integrated point cloud, for example, to improve processing efficiency. In embodiments, some of the octrees 1702a-b are updated relatively frequently (i.e., points from the LiDAR devices 1302, 1304 are incorporated into the octrees 1702a-b), while some of the other octrees 1702c-d are updated relatively infrequently. In embodiments, updating the octrees 1702a-d involves changing the resolution of the octrees a-d, i.e., changing the volume of 3D space that is captured for each voxel. As such, the octrees representing areas of interest (e.g., areas where other vehicles, pedestrians, and other objects of interest are likely to be present) are updated relatively frequently (e.g., once per revolution) to ensure that the data of the octrees is current, while the other octrees are updated relatively infrequently (e.g., once every five revolutions) to conserve data processing resources. The particular update frequency of the octrees can be adjusted based on the motion of the vehicle and environmental conditions such as weather, traffic, pedestrians, etc. In embodiments, the frequency at which some of the octrees are updated is based on a pre-determined classification of areas of interest in different scenarios based on historical data. The classification can be based on classical or machine learning techniques. For example, a machine learning model can determine that the most important areas of interest in areas of pedestrian traffic congestion are likely to be in front of the vehicle. As such, the octrees used to quantify a portion of the environment 190 in front of the vehicle are updated most frequently. In embodiments, the octrees 1702a-d can have different resolutions based on various factors. For example, some of the octrees used to quantify a portion of the environment 190 in front of the vehicle can have the largest resolution (i.e., the smallest size when the size of the octree is inversely proportional to the resolution) to ensure high-fidelity object detection, classification, and tracking by the perception system in front of the vehicle.

[0145] In embodiments, the volume of each of the octrees 1702a-d varies depending on the environment of the AV 1300. For example, the octrees can be relatively small, which corresponds to a relatively high resolution and requires more processing resources, or relatively large, which corresponds to a relatively low resolution and requires less processing resources. As such, the size (e.g., resolution) of the voxels can be adapted to the environment of the AV 1300. For example, if the AV 1300 is in a dense environment (e.g., an urban street), the resolution of the voxels is configured to be higher. In contrast, if the AV 1300 is in a sparse environment (e.g., an interstate highway), the resolution of the voxels is configured to be lower.

[0146] In embodiments, the pole voxels 1702a-d are updated based on one or more metrics associated with the respective pole voxel. One type of metric is the time at which the pole voxel was last updated. In embodiments, the pole voxels 1702a-d are updated in a staggered manner in which some of the voxels are updated at different times than other voxels. The time at which a particular pole voxel was updated can be used to determine whether the pole voxel should be updated. Another metric is which LiDAR devices are used to update the pole voxel. In embodiments, some of the pole voxels 1702a-d are updated using data from both LiDAR devices 1302 and 1304, while some of the pole voxels 1702a-d are updated using data from only one of the LiDAR devices 1302 and 1304. Another voxel metric is the density of the points of the pole voxel. If the points of a particular pole voxel have a high density, which indicates that the voxel likely contains a physical object, the voxel can be updated more frequently to ensure that current information about the object is available. In embodiments, the density of the points of a particular pole voxel is measured periodically (e.g., by the perception module 404). In this embodiment, the frequency of updates to the voxel is reduced if the density does not satisfy a threshold. In embodiments, some other voxel metrics include a timestamp of the last update to a particular pole voxel, a number of LiDARs that are updating a particular given pole voxel, a covariance of the spread of points in a particular pole voxel, and a ratio of a number of LiDARs attached to a roof of the vehicle to a number of LiDARs attached to a body of the vehicle.

[0147] FIG. 18 is a flowchart representative of a process 1800 for operating a vehicle based on point cloud information. In embodiments, the vehicle is the AV 1300 shown in FIG. 13 and the process 1800 is performed by a processor, such as the processor 1626 shown in FIG. 16 or the perception module 402 shown in FIG. 4 .

[0148] The processor receives (1802) first LiDAR point cloud information from a first LiDAR device and second LiDAR point cloud information from a second LiDAR device. In embodiments, the LiDAR devices are the LiDAR devices 1302, 1304 shown in FIG. 13 and the LiDAR point cloud information is the LiDAR point cloud information 1306 shown in FIG. 16The illustrated point clouds 1622, 1624. The first LiDAR device and the second LiDAR device are each configured to detect light reflected from objects proximate to the vehicle and generate LiDAR point cloud information based on the detected light. The first LiDAR device is at a first position of the vehicle and is configured with a first start angle and a first frequency, and the second LiDAR device is at a second position of the vehicle and is configured with a second start angle and a second frequency. In embodiments, the first frequency is different than the second frequency. In embodiments, the first LiDAR device is configured with a first start angle that is different than the second start angle. In embodiments, the LiDAR devices are synchronized, e.g., operate according to a common time reference and / or have synchronized clocks.

[0149] The processor receives (1804) first timestamp information associated with the first LiDAR point cloud information and second timestamp information associated with the second LiDAR point cloud information. In embodiments, the timestamp information is FIG. 16 The illustrated timestamp data 1628, 1630.

[0150] In embodiments, each point observed by the LiDAR is associated with a timestamp. The first timestamp information represents a first time, where the first time is a time at which a first point of the first LiDAR point cloud information was detected, and the second timestamp information represents a second time, where the second time is a time at which a second point of the second LiDAR point cloud information was detected. For example, the time at which the first point was detected is based on a laser emission time of a point block detected at a first azimuth angle and a first offset value specific to the first point. In embodiments, the processor further determines a correspondence between the first time and the second time according to the first start angle, the first frequency, the second start angle, and the second frequency.

[0151] The processor generates (1806) third point cloud information according to merging the first and second LiDAR point cloud information. The above is directed to FIG. 13-15 Techniques for merging point cloud information are described. The generation of the third point cloud information is according to the first and second LiDAR point cloud information, the first and second timestamp information, the first start angle, the first frequency, the second start angle, and the second frequency. In embodiments, the generation of the third point cloud information includes processing the first LiDAR point cloud information and the second LiDAR point cloud information based on the first position and the second position.

[0152] In embodiments, the processor determines that a first detected light point for the first LiDAR point cloud information and a second detected light point for the second LiDAR point cloud information correspond to a same position relative to the vehicle (e.g., a distance at a same azimuth angle and height relative to a fixed position of the vehicle, such as a center of a rear axle, etc.). According to the determination, the processor generates combined LiDAR point cloud information that includes the first and second detected light points.

[0153] In an embodiment, the processor generates a data structure representing voxels oriented according to polar coordinates (e.g., FIG. 17A-17B The integrated point cloud 1700 shown assigns points from either the first or second LiDAR point cloud information to one of the voxels of a data structure. In one embodiment, this includes generating a certain number of voxels for the data structure based on a predetermined resolution. In another embodiment, this further includes receiving multiple voxel measurements and merging the first and second point cloud information based on the voxel measurements to generate a third point cloud information. In another embodiment, a certain number of voxels for the data structure are generated based on the number of objects proximate to the vehicle.

[0154] FIG. 19 This is a flowchart illustrating the process 1900 for operating a vehicle based on point cloud information. In this embodiment, the vehicle is... FIG. 13 The AV 1300 shown, and the processing 1900 by, for example FIG. 16 The processor shown is a processor such as 1626 or a processor FIG. 4 The sensing module 402 shown is executed.

[0155] The processor configures the first LiDAR device (1902) to spin from a first starting angle at a first frequency. In the implementation, the first LiDAR device is FIG. 13 The LiDAR device 1302 shown has a first frequency of FIG. 14 The frequency shown is 1410, and the first starting angle is FIG. 14 The starting angle shown is 140°.

[0156] The processor configures the second LiDAR device (1904) to spin at a second frequency from a second starting angle different from the first starting angle. In the implementation, the second LiDAR device is FIG. 13 The LiDAR device 1304 shown has a first frequency of FIG. 14 The frequency shown is 1412, and the first starting angle is FIG. 14 The starting angle shown is 1404.

[0157] The processor receives (1906) information representing a first detection spot and a first timestamp representing the time of illumination of the first spot from the first LiDAR device. The processor receives (1908) information representing a second detection spot and a second timestamp representing the time of illumination of the second spot from the second LiDAR device. The difference between the timestamps is less than the reciprocal of the first frequency. In other words, the timestamps indicate that the second detection spot was detected before the first LiDAR device completed a full rotation from detecting the first detection spot.

[0158] The processor determines that the first detected light point and the second detected light point correspond to the same location relative to the vehicle (1910). The above, for example, is for the case where the first detected light point and the second detected light point are both detected by the same LiDAR sensor. FIG. 15 Techniques for making this determination are illustrated. In response, the processor generates (1912) LiDAR point cloud information that includes the first detected light point and the second detected light point at the same coordinates relative to a fixed origin of the vehicle. In embodiments, the generated LiDAR point cloud information is FIG. 16 An example of the consolidated point cloud information 1600 is shown. The vehicle is then operated (1914) in accordance with the LiDAR point cloud information, for example by a control module of the vehicle communicating with the processor 1626 or the perception module 404.

[0159] In the foregoing description, embodiments of the application have been described with reference to numerous specific details that can vary with implementation. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the application, and what is intended by the applicants to be the scope of the application, is the literal and equivalent scope of the claims as issued by the US Patent and Trademark Office, including any subsequent correction. Any definitions of terms here in this detailed description are expressly incorporated herein by reference only to the extent that the terms are expressly used therein. In addition, when the description or appendices use the term "comprising", it can be taken that the contents of what follows in the passage is additional or optional.

Claims

1. A method for a vehicle, comprising: The first LiDAR device of the vehicle is configured to spin from a first starting angle at a first frequency; The second LiDAR device of the vehicle is configured to spin at a second frequency from a second starting angle different from the first starting angle; Receive point cloud information at the first detection point and the first timestamp of detecting the first detection point from the first LiDAR device; Receive point cloud information at the second detection point and a second timestamp indicating that the second detection point was detected from the second LiDAR device; It is determined that the first detection light spot and the second detection light spot correspond to the same position relative to the vehicle. as well as Based on the determination that the first detection light point and the second detection light point correspond to the same position and that the difference between the first timestamp and the second timestamp is less than the reciprocal of the first frequency, LiDAR point cloud information including the first detection light point and the second detection light point at the same coordinates relative to the fixed origin of the vehicle is generated. Wherein, the first frequency is different from the second frequency. The first LiDAR device is located at a first position on the vehicle, and the second LiDAR device is located at a second position on the vehicle.

2. The method according to claim 1, wherein, The time of detecting the first detection spot is based on the laser emission time of the spot block detected with a first offset value specific to the first detection spot and a first azimuth angle.

3. The method according to claim 1, further comprising: The correspondence between the first timestamp and the second timestamp is determined based on the first starting angle, the first frequency, the second starting angle, and the second frequency.

4. The method according to claim 1, wherein, Generating the LiDAR point cloud information includes: generating a data structure representing voxels oriented according to polar coordinates.

5. The method according to claim 4, wherein, Generating the LiDAR point cloud information includes: generating a certain number of voxels for the data structure according to a predetermined resolution.

6. The method according to claim 4, further comprising: Receives multiple voxel measurements; as well as The first point cloud information of the first detection light point and the second point cloud information of the second detection light point are merged based on the voxel measurement to generate the third point cloud information.

7. The method according to claim 4, wherein, A certain number of voxels in the data structure are generated based on the number of objects close to the vehicle.

8. The method according to claim 1, further comprising: The LiDAR point cloud information is generated based on the first position and the second position.

9. The method according to claim 1, wherein, The first LiDAR device and the second LiDAR device are synchronized.

10. A vehicle, comprising: At least one computer-readable medium for storing computer-executable instructions; At least one processor configured to execute the computer-executable instructions, the execution of which includes: The first LiDAR device is configured to spin from a first starting angle at a first frequency; The second LiDAR device is configured to spin at a second frequency from a second starting angle different from the first starting angle; Receive point cloud information at the first detection point and the first timestamp of detecting the first detection point from the first LiDAR device; Receive point cloud information at the second detection point and a second timestamp indicating that the second detection point was detected from the second LiDAR device; It is determined that the first detection light spot and the second detection light spot correspond to the same position relative to the vehicle; and Based on the determination that the first detection light point and the second detection light point correspond to the same position and that the difference between the first timestamp and the second timestamp is less than the reciprocal of the first frequency, LiDAR point cloud information including the first detection light point and the second detection light point at the same coordinates relative to the fixed origin of the vehicle is generated. Wherein, the first frequency is different from the second frequency. The first LiDAR device is located at a first position on the vehicle, and the second LiDAR device is located at a second position on the vehicle.

11. The vehicle according to claim 10, wherein, The time of detecting the first detection spot is based on the laser emission time of the spot block detected with a first offset value specific to the first detection spot and a first azimuth angle.

12. The vehicle according to claim 10, further comprising: The correspondence between the first timestamp and the second timestamp is determined based on the first starting angle, the first frequency, the second starting angle, and the second frequency.

13. The vehicle according to claim 10, wherein, Generating the LiDAR point cloud information includes: generating a data structure representing voxels oriented according to polar coordinates.

14. The vehicle according to claim 13, wherein, Generating the LiDAR point cloud information includes: generating a certain number of voxels for the data structure according to a predetermined resolution.

15. A non-transitory computer-readable storage medium comprising at least one program executable by at least one processor, the at least one program including instructions that, when executed by the at least one processor, cause a vehicle to perform computer-implemented operations, the computer-implemented operations including: The first LiDAR device of the vehicle is configured to spin from a first starting angle at a first frequency; The second LiDAR device of the vehicle is configured to spin at a second frequency from a second starting angle different from the first starting angle; Receive point cloud information at the first detection point and the first timestamp of detecting the first detection point from the first LiDAR device; Receive point cloud information at the second detection point and a second timestamp indicating that the second detection point was detected from the second LiDAR device; The first detection light spot and the second detection light spot are determined to correspond to the same position relative to the vehicle. as well as Based on the determination that the first detection light point and the second detection light point correspond to the same position and that the difference between the first timestamp and the second timestamp is less than the reciprocal of the first frequency, LiDAR point cloud information including the first detection light point and the second detection light point at the same coordinates relative to the fixed origin of the vehicle is generated. Wherein, the first frequency is different from the second frequency. The first LiDAR device is located at a first position on the vehicle, and the second LiDAR device is located at a second position on the vehicle.

16. The non-transitory computer-readable storage medium according to claim 15, wherein, The time of detecting the first detection spot is based on the laser emission time of the spot block detected with a first offset value specific to the first detection spot and a first azimuth angle.

17. A computer program product comprising a program for causing a computer to perform the method according to any one of claims 1-9.

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