WO2022228523A1 - 车辆定位系统、方法及路侧装置 - Google Patents

车辆定位系统、方法及路侧装置 Download PDF

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Publication number
WO2022228523A1
WO2022228523A1 PCT/CN2022/089944 CN2022089944W WO2022228523A1 WO 2022228523 A1 WO2022228523 A1 WO 2022228523A1 CN 2022089944 W CN2022089944 W CN 2022089944W WO 2022228523 A1 WO2022228523 A1 WO 2022228523A1
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Prior art keywords
vehicle
roadside
information
position information
unit
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PCT/CN2022/089944
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English (en)
French (fr)
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孟毅
孟祥赞
邹子君
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株式会社日立制作所
孟毅
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Publication of WO2022228523A1 publication Critical patent/WO2022228523A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
    • G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]

Definitions

  • the present invention relates to a vehicle positioning system, method and roadside device, in particular to a vehicle positioning system, method and roadside device based on roadside sensors and vehicle road coordination.
  • GPS equipment widely used in ordinary vehicles its positioning accuracy can only reach a few meters, or even nearly 10 meters. In ordinary navigation, such accuracy is no problem, but for applications such as autonomous driving, it is far from meeting the requirements. And in the GPS positioning, there are three parts of errors: the first part of the error is the satellite orbit error; the second part of the error is the transmission error caused by the signal ionosphere, troposphere, etc.; the third part of the error is the inherent error of the receiver.
  • differential GPS is mostly used in autonomous driving to achieve high-precision positioning.
  • Differential GPS first uses the differential GPS reference station with known precise three-dimensional coordinates to obtain the pseudorange correction amount or position correction amount, and then sends the correction amount to the user in real time or afterwards, and uses the correction amount to correct the user's measurement data. , to improve GPS positioning accuracy.
  • Patent Document 1 discloses a method of obtaining differential positioning correction parameters through roadside units, and broadcasting the differential positioning correction parameters to vehicle-mounted units within a preset range; obtaining the original positioning data of the vehicle-mounted unit through the vehicle-mounted unit, and Based on the differential positioning correction parameter, the original positioning data is corrected to obtain the final positioning data of the vehicle-mounted unit based on the differential positioning method and technology of the intelligent vehicle-road coordination system.
  • Patent Document 1 Chinese Patent Application CN105974453A
  • the differential positioning correction parameter calculated by the roadside unit can correct the first part error and the second part error existing in GPS positioning, but cannot correct the third part error. Since the third part of the error is a more important error in many practical applications, the differential GPS system provider not only needs to provide differential GPS information, but also needs to provide a corresponding receiver chip to correct the above-mentioned third part of the error. In addition, measuring the GPS differential information in Patent Document 1 requires complex equipment, and the device needs to be installed in a position with a wide field of view that can simultaneously observe multiple satellites, and the GPS differential information cannot be accurately measured in urban roads due to the occlusion of tall buildings. .
  • the purpose of the present invention is to propose a vehicle positioning system, method and method for positioning the vehicle with high precision even in the road blocked by the surrounding tall buildings based on the simple roadside equipment and the vehicle positioning equipment with ordinary positioning accuracy. roadside device.
  • the invention relates to a vehicle positioning system, comprising: a roadside unit, the roadside unit further comprises a roadside sensor part, a roadside calculation part, a roadside positioning part and a roadside communication part, wherein the roadside sensor part detects the entry detection Vehicles within the range; a roadside calculation section that calculates the position information of the detected vehicle; a roadside positioning section that calculates the position information of the detected vehicle based on the high-precision position information of the roadside sensor section and the position information of the detected vehicle high-precision absolute position information; and a roadside communication unit that broadcasts broadcast information containing the detected vehicle's high-precision absolute position information, the system further includes a vehicle-mounted unit, the vehicle-mounted unit further includes a vehicle-mounted positioning unit, a vehicle-mounted communication unit, and a vehicle-mounted The calculation part, wherein the vehicle positioning part records the position information of the own vehicle through the vehicle sensor; the vehicle communication part receives the broadcast information; and the vehicle calculation part obtains the high-precision absolute position information of the
  • the vehicle can be positioned with high precision even on roads blocked by tall buildings around.
  • FIG. 1 is a configuration diagram showing a vehicle positioning system according to a first embodiment.
  • FIG. 2 is a configuration diagram showing a roadside unit according to the first embodiment.
  • FIG. 3 is a diagram showing the configuration of the vehicle-mounted unit according to the first embodiment.
  • FIG. 4 is a flowchart showing the processing of the roadside unit according to the first embodiment.
  • FIG. 5 is a flowchart showing the processing of the vehicle-mounted unit according to the first embodiment.
  • FIG. 6 is a flowchart showing the processing of the roadside unit according to the second embodiment.
  • FIG. 7 is a flowchart showing the processing of the vehicle-mounted unit according to the second embodiment.
  • FIG. 8 is a flowchart showing the processing of the roadside unit according to the third embodiment.
  • FIG. 9 is a schematic diagram of coordinates showing the third embodiment.
  • FIG. 10 is a schematic diagram showing the format of broadcast information.
  • FIG. 1 is a configuration diagram showing a vehicle positioning system according to a first embodiment.
  • Roadside units are typically installed at intersections or critical sections of roads.
  • the vehicle positioning system is described by taking the roadside unit installed at the intersection and two vehicles entering the detection range of the sensor as an example.
  • the vehicle positioning system mainly includes roadside unit 1, vehicle 2, vehicle 3 and other vehicles.
  • Vehicles such as vehicle 2 and vehicle 3 receive GNSS (Global Navigation Satellite System) signals through sensors such as general GPS (Global Positioning System) during driving to record the position information of the vehicle.
  • GNSS Global Navigation Satellite System
  • GPS Global Positioning System
  • the roadside unit 1 detects the position information of vehicles such as vehicle 2 and vehicle 3 entering the detection range through sensors, uses the detected data to calculate the high-precision position information of vehicles such as vehicle 2 and vehicle 3, and packs the calculation results to pass the V2X (vehicle to everything, that is, vehicle-road-cloud collaboration technology) communication broadcast.
  • Vehicle-mounted units mounted on vehicles such as vehicle 2 and vehicle 3 receive broadcast information broadcast by roadside unit 1, and acquire high-precision position information belonging to the own vehicle from the broadcast information.
  • the on-board unit uses the high-precision position information belonging to the own vehicle from the roadside unit to update the ordinary positioning accuracy position information recorded by the on-board unit of the own vehicle.
  • FIG. 2 is a configuration diagram showing a roadside unit according to the first embodiment.
  • the roadside unit includes a roadside sensor unit, a roadside calculation unit, a roadside positioning unit, and a roadside communication unit.
  • the roadside sensor unit detects a vehicle entering the detection range.
  • the roadside calculation unit calculates vehicle information detected by the roadside sensor unit.
  • the roadside positioning unit calculates high-precision absolute position information of the detected vehicle based on the high-precision position information of the roadside sensor unit and the detected position information of the vehicle.
  • the high-precision position information of the roadside sensor unit can be realized by corresponding hardware or by software such as external settings.
  • the roadside communication unit broadcasts broadcast information including high-precision absolute position information of the detected vehicle.
  • FIG. 3 is a diagram showing the configuration of the vehicle-mounted unit according to the first embodiment.
  • the in-vehicle unit includes an in-vehicle positioning part, an in-vehicle communication part and an in-vehicle computing part.
  • the in-vehicle positioning part records the position information of the ordinary positioning accuracy of the self-vehicle through the in-vehicle sensors.
  • the vehicle positioning part is realized by the GPS system or Beidou system with ordinary positioning accuracy, that is, the positioning system with ordinary positioning accuracy.
  • the in-vehicle communication unit receives broadcast information broadcast by the roadside communication unit.
  • the in-vehicle computing part obtains the high-precision absolute position information of the self-vehicle from the broadcast information received by the in-vehicle communication part, and replaces the ordinary positioning precision position information of the self-vehicle with the acquired high-precision absolute position information of the self-vehicle.
  • the vehicle can be positioned with high precision even in the roads blocked by tall buildings around.
  • FIG. 4 is a flowchart showing the processing of the roadside unit according to the first embodiment.
  • Step S401 indicates that the roadside unit starts to work.
  • Step S402 is a vehicle detection step in which a vehicle entering the detection range is detected by the roadside sensor unit.
  • Step S403 is a roadside calculation step of calculating the position information of the vehicle detected by the roadside sensor unit.
  • Step S404 is a roadside positioning step, which calculates the position of the vehicle detected by the roadside sensor unit according to the high-precision position information of the roadside sensor unit and the position information of the vehicle detected by the roadside sensor unit calculated in the roadside calculation step.
  • High-precision absolute position information The high-precision position information of the roadside sensor unit can be realized by corresponding hardware or by software such as external settings.
  • Step S405 is a roadside communication step, which broadcasts broadcast information including high-precision absolute position information of the vehicle.
  • Step S406 indicates that the roadside unit ends its work.
  • FIG. 5 is a flowchart showing the processing of the vehicle-mounted unit according to the first embodiment.
  • Step S501 indicates that the vehicle-mounted unit starts to work.
  • Step S502 is an on-board positioning step, which records the position information of the self-vehicle through on-board sensors such as general-purpose GPS (Global Positioning System). The self-vehicle position information recorded at this time is the ordinary positioning accuracy position information.
  • Step S503 is an in-vehicle communication step, which receives broadcast information broadcast by the roadside communication step.
  • Step S504 is an on-board calculation step, which acquires the high-precision absolute position information of the own vehicle from the broadcast information received in the on-board communication step, and replaces the self-vehicle position information with the high-precision absolute position information of the own vehicle.
  • Step S505 represents the end of the operation of the vehicle-mounted unit.
  • FIG. 6 is a flowchart showing the processing of the roadside unit according to the second embodiment.
  • FIG. 7 is a flowchart showing the processing of the vehicle-mounted unit according to the second embodiment.
  • a lidar is used as a roadside sensor.
  • Lidar emits laser beams to the surroundings, and can determine the position, speed and other information of obstacles by analyzing the beam information reflected by the laser beam encountering obstacles.
  • Step S601 indicates that the roadside unit starts to work.
  • Step S602 is to read the high-precision position information of the laser radar from the auxiliary positioning device or obtain the high-precision position information of the laser radar from a specific server.
  • Step S603 is to acquire background information, that is, environmental information. The acquisition of the point cloud information of the background, that is, the environment, by the lidar is beneficial to improve the subsequent detection accuracy.
  • Step S604 is to acquire point cloud data of the lidar.
  • Step S605 is to remove background data and noise in the lidar point cloud.
  • Step S606 is to perform clustering processing on the point cloud of the lidar. Through the clustering algorithm, information such as the type and location of obstacles can be obtained.
  • Step S607 is to measure the information of the vehicles within the detection range and track the vehicles. Tracking vehicles can improve detection accuracy. Vehicle tracking can be achieved by existing algorithms such as the DeepSORT algorithm.
  • Step S608 is to calculate the vehicle position and vehicle speed. Since the obstacle information detected by the lidar is the position of the lidar as the center, it needs to be converted into the absolute position information of the vehicle before being sent to the vehicle. The high-precision absolute position information of the vehicle is calculated from the high-precision position information of the lidar and the height of the radar from the ground. The installation height of the radar is stored in the setting information of the lidar as known information. The location information such as the center coordinates of the detected vehicle can be obtained by calculation.
  • Step S609 is to extract information such as high-precision absolute position information and vehicle speed information of the vehicle as broadcast information, and broadcast the broadcast information through V2X communication.
  • Step S610 is to determine whether the processing flow of the roadside unit ends.
  • Step S611 represents the end of the processing flow of the roadside unit.
  • Step S701 indicates that the vehicle-mounted unit starts to work.
  • Step S702 is for the vehicle to obtain the position information of the own vehicle from the GNSS system.
  • Step S703 is to record the running track of the self-vehicle according to the location information of the self-vehicle obtained from the GNSS system.
  • Step S704 is to determine whether there is broadcast information from the roadside unit. When it is determined that there is broadcast information from the roadside unit, the process proceeds to step S705. When it is determined that there is no broadcast information from the roadside unit, the process proceeds to step S708 to end the processing flow of the vehicle-mounted unit.
  • Step S705 is to determine the vehicle information belonging to the own vehicle from the broadcast information after the vehicle receives the information sent by the roadside unit.
  • the vehicle number is identification information set by the roadside module and cannot be directly related to the actual vehicle. The vehicle end determines the vehicle information belonging to its own vehicle in the following manner.
  • the vehicle itself records the running track of the own vehicle according to the position information of the own vehicle obtained from the GNSS system, and obtains multiple trajectories of the same vehicle number according to the position information of the same vehicle number in the detection information sent by the roadside unit.
  • the DTW Dynamic Time Warping
  • tr 1 ⁇ (x 1 ,y 1 ),(x 2 ,y 2 ),...,(x n ,y n ) ⁇ ,
  • tr 2 ⁇ (x 1 ′,y 1 ′),(x 2 ′,y 2 ′),...,(x m ′,y m ′) ⁇ ,
  • (x i , y i ) are the position coordinates of each point on the trajectories of tr 1 and tr 2
  • Head(tr) represents the first point of the trajectory
  • Rest(tr) represents all points except the first point A subsequence composed; other means other, and refers to conditions other than the previous ones.
  • n, m are the number of points in the trajectories tr 1 and tr 2 , respectively.
  • Step S706 is to use the high-precision absolute position information of the own vehicle detected by the roadside unit to fuse and correct the vehicle's own vehicle position information obtained from the GNSS system after determining the vehicle information belonging to the own vehicle in the broadcast information.
  • An example of the fusion correction process is as follows:
  • Er is the vehicle speed error ratio
  • v is the vehicle speed detected by the vehicle sensor
  • v' is the vehicle speed detected by the roadside sensor.
  • P is the reliability.
  • U is the corrected measurement result
  • U' is the measurement result
  • ⁇ is the weighted correction constant, which can be obtained through experiments.
  • Step S707 is to update the self-vehicle position information obtained by the vehicle from the GNSS system to the above-mentioned corrected measurement result U.
  • Step S708 represents the end of the processing of the in-vehicle unit.
  • FIG. 8 is a flowchart showing the processing of the roadside unit according to the third embodiment.
  • FIG. 9 is a schematic diagram of coordinates showing the third embodiment.
  • a combination of a camera and a millimeter-wave radar is used as a roadside sensor.
  • Step S801 indicates that the roadside unit starts to work.
  • Step S802 is to read the high-precision position information of the sensor combination itself from the auxiliary positioning device or obtain the high-precision position information and related installation information of the sensor combination from a specific server. After that, the camera and the millimeter-wave radar each detect the information.
  • the camera captures an image.
  • Step S804 is to detect and track the vehicle image in the collected image.
  • Step S805 is to detect the license plate information in the vehicle image.
  • the detection of vehicle and license plate information is implemented, for example, by existing deep learning algorithms, such as Yolo algorithm.
  • Step S806 is to estimate the position of the detected vehicle through the image.
  • the imaging principle of the camera is simplified as a pinhole imaging model, for example.
  • O C -X C Y C Zc is the camera coordinate system
  • O'-X'Y' is the image physical coordinate system
  • O-uv is the image pixel coordinate system.
  • the coordinates of the point Q in the camera coordinate system are (Xc, Yc, Zc)
  • the point q is the projection of the point Q on the imaging plane
  • the coordinates in the image physical coordinate system are (Xc', Yc')
  • the image pixel coordinates The coordinates under the system are (u, v). From similar triangles, we can see that:
  • f x , f y , u 0 and v 0 are the internal parameters of the camera, and f is the focal length of the camera.
  • the position information of the object in the image can be calculated as [id i , xi ,y i ,v xi ,v yi ] by using the information such as the installation height and pitch angle of the camera in the sensor installation information.
  • Step S807 is to acquire the radar point cloud for the millimeter wave radar.
  • Step S808 is to perform noise reduction filtering on the millimeter wave radar point cloud.
  • Step S809 is to measure the position information of the vehicle entering its measurement range.
  • Step S810 is calculating the distance and speed information of the vehicle. Using the Doppler effect, millimeter-wave radar can accurately measure the distance and speed information of the detected vehicle.
  • Step S811 is to calculate the distance of each detected object, that is, to calculate the distance between each vehicle in the vehicle set A detected by the camera and all the vehicles in the vehicle set B detected by the millimeter wave radar.
  • Step S812 is to fuse the position and speed information [id j , x j , y j , v xj , v yj ] of the vehicle with the camera data.
  • the closest point to a vehicle in vehicle set A is the information of the same vehicle detected by the millimeter wave, which can correlate the detection information of the same vehicle camera with the detection information of the millimeter wave radar, and fuse the position and speed information of the same vehicle with the camera data. .
  • Step S813 is the extracted high-precision position information of the vehicle, vehicle speed and other information.
  • Step S813 is to extract, as broadcast information, information such as high-precision absolute position information and vehicle speed information of the vehicle in the information obtained by fusing the position and speed information of the same vehicle with the camera data.
  • Step S814 is to broadcast the extracted information as broadcast information.
  • Step 815 is to determine whether the processing flow of the roadside unit ends.
  • Step S816 represents the end of the processing flow of the roadside unit.
  • the license plate information is directly obtained as the vehicle number through the camera included in the roadside unit, and broadcast information including the vehicle number is broadcast.
  • the on-board unit acquires the broadcast information, it can use the license plate information contained in the broadcast information to easily identify the vehicle information belonging to the own vehicle, and use the high-precision absolute position information of the own vehicle detected by the corresponding roadside unit to detect the vehicle from GNSS.
  • the system obtains the position information of the self-vehicle for fusion correction.
  • the detection accuracy can be improved by the fusion with the roadside information.
  • the combination of a camera and a millimeter-wave radar in the roadside unit can be used as a roadside sensor to realize low-cost roadside hardware equipment and vehicle positioning equipment with ordinary positioning accuracy, even on roads blocked by tall buildings.
  • the vehicle can be positioned with high precision.
  • FIG. 10 is a schematic diagram showing the format of broadcast information.
  • the broadcast information is divided into two parts: head information and vehicle information, wherein the vehicle information can be multiple items according to the number of detected vehicles.
  • the header information includes the message number, time stamp, number of detected vehicles and reserved items.
  • the message number increases monotonically with the amount of broadcast information of the RSU.
  • the timestamp is the moment when the message was sent.
  • the number of vehicles is the number of vehicles included in the current broadcast information, and the length of the vehicle information part can be determined through this information.
  • Each item of vehicle information includes vehicle number, vehicle lateral size, vehicle longitudinal size, vehicle center coordinates, vehicle speed, vehicle travel direction, and reserved items.
  • the vehicle number is the identification number of the vehicle.
  • the vehicle number is a system-defined number, such as a 7-digit string, etc.
  • the same vehicle detected uses the same number, and different vehicles use different numbers.
  • the identification of the same vehicle can be achieved by a known signed vehicle tracking algorithm.
  • the vehicle number may be the license plate number. By using the license plate number as the vehicle number, the vehicle can more easily extract the self-vehicle information.
  • Vehicle-related information such as vehicle lateral size, vehicle longitudinal size, vehicle center coordinates, vehicle speed, and vehicle traveling direction are the detection and calculation results of roadside sensors.

Abstract

本发明涉及一种车辆定位系统、方法及路侧装置，其中车辆定位系统包括：路侧单元，其进一步包括：路侧传感器部，其检测进入检测范围内的车辆；路侧计算部，其计算检测到的车辆的位置信息；路侧定位部，其根据路侧传感器部的高精度位置信息及检测到的车辆的位置信息计算检测到的车辆的高精度绝对位置信息；以及路侧通信部，其将包含检测到的车辆的高精度绝对位置信息的广播信息进行广播，系统还包括车载单元，车载单元进一步包括：车载定位部，其通过车载传感器记录自车位置信息；车载通信部，其接收广播信息；以及车载计算部，其从接收到的广播信息中获取自车的高精度绝对位置信息，并将自车位置信息替换为自车的高精度绝对位置信息。

Description

车辆定位系统、方法及路侧装置
本申请主张2021年04月30日申请的中国专利申请ZL202110479595.3的优先权，通过参照上述中国专利申请的内容，而其内容并入本申请。
技术领域
本发明涉及车辆定位系统、方法及路侧装置，特别涉及基于路侧传感器与车路协同实现的车辆定位系统、方法及路侧装置。
背景技术
广泛应用于普通车辆的GPS设备，其定位精度仅能够达到几米，甚至近10米。在普通的导航中，这样的精度没有问题，但对于自动驾驶等应用来说却远远不能满足要求。且在GPS的定位中，存在三部分误差：第一部分误差是卫星轨道误差等；第二部分误差是信号电离层，对流层等引起的传输误差；第三部分误差是接收机的固有误差。
目前，在自动驾驶中较多使用差分GPS来实现高精度定位。差分GPS首先利用已知精确三维坐标的差分GPS基准台，求得伪距修正量或位置修正量，再将该修正量实时或事后发送给用户，并利用该修正量对用户的测量数据进行修正，以提高GPS定位精度。
专利文献1中公开了一种通过路侧单元获取差分定位修正参数，并将所述差分定位修正参数广播至预设范围内的车载单元；通过车载单元获取所述车载单元的原始定位数据，并基于所述差分定位修正参数，对所述原始定位数据进行修正，得到所述车载单元的最终定位数据的基于智能车路协同系统的差分定位方法及智能车路协同系统的技术。
现有技术文献
专利文献
专利文献1：中国专利申请CN105974453A
发明内容
发明要解决的问题
在专利文献1中，路侧单元计算的差分定位修正参数可以对GPS定位中存在的第一部分误差及第二部分误差进行修正，但对于第三部分误差无法修正。由于在很多实际应用中第三部分误差是更为主要的误差，差分GPS系统提供商不但需要提供差分GPS信息，还需要提供相应接收芯片，用以对上述第三部分的误差进行纠正。此外，测量专利文献1中的GPS差分信息需要复杂的设备,且该设备需要设置在能同时观测多颗卫星的视野开阔的位置，在城市道路中由于高大建筑物的遮挡不能精确测量GPS差分信息。
因此，本发明的目的在于提出一种基于简单的路侧设备及普通定位精度车载定位设备，即使在周围有高大建筑物遮挡的道路中也能对车辆进行高精度定位的车辆定位系统、方法及路侧装置。
解决问题的技术手段
本发明涉及一种车辆定位系统，包括：路侧单元，路侧单元进一步包括路侧传感器部、路侧计算部、路侧定位部以及路侧通信部，其中路侧传感器部，其检测进入检测范围内的车辆；路侧计算部，其计算检测到的车辆的位置信息；路侧定位部，其根据路侧传感器部的高精度位置信息及检测到的车辆的位置信息计算检测到的车辆的高精度绝对位置信息；以及路侧通信部，其将包含检测到的车辆的高精度绝对位置信息的广播信息进行广播，系统还包括车载单元，车载单元进一步包括车载定位部、车载通信部以及车载计算部，其中车载定位部，其通过车载传感器记录自车位置信息；车载通信部，其接收广播信息；以及车载计算部，其从接收到的广播信息中获取自车的高精度绝对位置信息，并将自车位置信息替换为自车的高精度绝对位置信息。
发明的效果
根据本发明，能够基于简单的路侧设备及普通定位精度车载定位设备，即使在周围有高大建筑物遮挡的道路中也能对车辆进行高精度定位。
附图说明
图1为表示第1实施方式的车辆定位系统的构成图。
图2为表示第1实施方式的路侧单元的构成图。
图3为表示第1实施方式的车载单元的构成图。
图4为表示第1实施方式的路侧单元的处理流程图。
图5为表示第1实施方式的车载单元的处理流程图。
图6为表示第2实施方式的路侧单元的处理流程图。
图7为表示第2实施方式的车载单元的处理流程图。
图8为表示第3实施方式的路侧单元的处理流程图。
图9为表示第3实施方式的坐标示意图。
图10表示广播信息的格式的示意图。
具体实施方式
下面，参照附图，说明用于实施本发明的实施方式。此外，本发明不限定于以下的实施方式，在本发明的技术概念中还将各种变形例、应用例包含在该范围内。
图1为表示第1实施方式的车辆定位系统的构成图。路侧单元一般安装在交叉路口或道路的关键路段部分。本实施例以路侧单元安装在交叉路口，有两辆车进入传感器检测范围为例说明本车辆定位系统。车辆定位系统主要包含路侧单元1，车辆2、车辆3等车辆等。车辆2、车辆3等车辆在行驶过程中通过通用GPS(全球定位系统)等传感器接收GNSS(全球导航卫星系统)信号记录自车位置信息。此时记录的自车位置信息为普通定位精度位置信息。路侧单元1通过传感器检测进入检测范围内的车辆2、车辆3等车辆的位置信息，利用所检测到的数据计算车辆2、车辆3等车辆的高精度位置信息，并将计算结果打包后通过V2X(vehicle to everything，即车路云协同技术)通信广播。搭载于车辆2、车辆3等车辆的车载单元接收路侧单元1广播的广播信息，并从该广播信息中获取属于自车的高精度位置信息。车载单元利用来自路侧单元的属于自车的高精度位置信息更新自车车载单元记录的普通定位精度位置信息。
图2为表示第1实施方式的路侧单元的构成图。路侧单元包括路侧传感器部、路侧计算部、路侧定位部以及路侧通信部。路侧传感器部检测进入检测范围内的车辆。路侧计算部计算路侧传感器部检测到的车辆信息。路侧定位部根据路侧传感器部的高精度位置信息及检测到的车辆的位置信息计算检测到的车辆的高精度绝对位置信息。路侧传感器部的高精度位置信息可以通过相应硬件或通过外部设置等软件形式来实现。路侧通信部将包含检测到的车辆的高精度绝对位置信息的广播信息进行广播。
图3为表示第1实施方式的车载单元的构成图。车载单元包括车载定位部、车载通信部以及车载计算部。车载定位部通过车载传感器记录自车的普通定位精度位置信息。车载定位部通过普通定位精度的GPS系统或北斗系统，即普通定位精度的定位系统实现。车载通信部接收路侧通信部广播的广播信息。车载计算部从车载通信部接收到的广播信息中获 取自车的高精度绝对位置信息，并将自车的普通定位精度位置信息替换为所获取的自车的高精度绝对位置信息。
通过本系统，基于简单的路侧设备及普通定位精度车载定位设备，即使在周围有高大建筑物遮挡的道路中也能对车辆进行高精度定位。
图4为表示第1实施方式的路侧单元的处理流程图。步骤S401表示路侧单元开始工作。步骤S402为车辆检测步骤，其通过路侧传感器部检测进入检测范围内的车辆。步骤S403为路侧计算步骤，其计算路侧传感器部检测到的车辆的位置信息。步骤S404为路侧定位步骤，其根据路侧传感器部的高精度位置信息及路侧计算步骤中计算得的路侧传感器部检测到的车辆的位置信息，计算路侧传感器部检测到的车辆的高精度绝对位置信息。路侧传感器部的高精度位置信息可以通过相应硬件或通过外部设置等软件形式来实现。步骤S405为路侧通信步骤，其将包含车辆的高精度绝对位置信息的广播信息进行广播。步骤S406表示路侧单元结束工作。
图5为表示第1实施方式的车载单元的处理流程图。步骤S501表示车载单元开始工作。步骤S502为车载定位步骤，其通过例如通用GPS(全球定位系统)等车载传感器记录自车位置信息。此时记录的自车位置信息为普通定位精度位置信息。步骤S503为车载通信步骤，其接收路侧通信步骤广播的广播信息。步骤S504为车载计算步骤，其从车载通信步骤中所接收到的广播信息中获取自车的高精度绝对位置信息，并将自车位置信息替换为自车的高精度绝对位置信息。步骤S505表示车载单元结束工作。
图6为表示第2实施方式的路侧单元的处理流程图。图7为表示第2实施方式的车载单元的处理流程图。
在本实施方式中利用激光雷达作为路侧传感器。激光雷达向周围发射激光束，通过分析激光束遇到障碍物被反射的光束信息，能够确定障碍物的位置，速度等信息。
步骤S601表示路侧单元开始工作。步骤S602为从辅助定位设备读取激光雷达的高精度位置信息或从特定服务器获取激光雷达的高精度位置信息。步骤S603为获取背景信息即环境信息。激光雷达获取背景即环境的点云信息有利于提高后续检测精度。步骤S604为获取激光雷达的点云数据。步骤S605为去除激光雷达点云中的背景数据与噪声。步骤S606为对激光雷达的点云进行聚类处理。通过聚类算法，可以得到障碍物的种类，位置等信息。步骤S607为测量检测范围内的车辆的信息并对车辆进行追踪。对车辆进行追踪能够提高检测精度。车辆的追踪可以通过如DeepSORT算法等现有算法实现。步骤S608为计算车辆位置及车速。由于激光雷达检测的障碍物信息是以激光雷达为中心的位置，在发送 给车辆之前需要转换成车辆的绝对位置信息。通过激光雷达的高精度位置信息以及雷达距离地面的高度计算车辆的高精度绝对位置信息。雷达的安装高度作为已知信息存储于激光雷达的设置信息中。通过计算可得所检测车辆的中心坐标等位置信息。步骤S609为提取车辆的高精度绝对位置信息、车速信息等信息作为广播信息，并通过V2X通信将广播信息进行广播。步骤S610为判断路侧单元的处理流程是否结束。步骤S611表示结束路侧单元的处理流程。
步骤S701表示车载单元开始工作。步骤S702为车辆从GNSS系统获取自车位置信息。步骤S703为根据从GNSS系统获取的自车位置信息记录自车的运行轨迹。步骤S704为判断是否有来自路侧单元的广播信息。判断结果为存在来自路侧单元的广播信息时，进入步骤S705。判断结果为不存在来自路侧单元的广播信息时，进入步骤S708结束车载单元的处理流程。步骤S705为当车辆接收到路侧单元发送的信息后，从广播信息中判断属于自身车辆的车辆信息。在本实施例中，车辆号码为路端模块设置的识别信息，并不能直接关联到实际车辆，车辆端通过如下方式判断属于自身车辆的车辆信息。
如前所述，车辆自身根据从GNSS系统获取的自车位置信息记录自车的运行轨迹，根据路侧单元发送的检测信息中相同车辆号码的位置信息得到多条相同车辆号码的轨迹。
通过如下算法计算车辆GPS记录轨迹与多条相同车辆号码的轨迹中各轨迹的距离，找到多条相同车辆号码的轨迹中与车辆GPS轨迹距离最近的车辆号码的轨迹，即为广播信息中属于自身车辆的车辆信息。
对于两条轨迹tr 1和tr 2，定义两条轨迹的DTW(Dynamic Time Warping即动态时间规整)距离如下：
tr 1＝{(x 1,y 1),(x 2,y 2),······,(x n,y n)},
tr 2＝{(x 1′,y 1′),(x 2′,y 2′),······,(x m′,y m′)},
Figure PCTCN2022089944-appb-000001
Figure PCTCN2022089944-appb-000002
其中(x i,y i)为tr 1和tr 2轨迹上各点的位置坐标，Head(tr)表示该轨迹的第一个点；Rest(tr)表示除第一个点之外的所有点组成的子序列；other表示其他，指前面以外的条件。n，m分别为轨迹tr 1和tr 2中点的数目。
步骤S706为确定了广播信息中属于自身车辆的车辆信息后，利用路侧单元检测的自身车辆的高精度绝对位置信息对车辆从GNSS系统获取自车位置信息进行融合修正。融合修正的过程举例如下：
(1)计算激光雷达检测的车辆速度误差。由于车辆传感器的检测精度较高，该误差例如通过激光雷达检测结果与车辆传感器检测结果的差值除以车速得出。
Figure PCTCN2022089944-appb-000003
其中，E r为车辆速度误差比，v为车辆传感器检测的车辆速度，v’为路侧传感器检测的车辆速度。
(2)通过速度检测误差比确定路侧数据的可信度。误差越大，可信度越小；误差越小，可信度越大。例如可信度可通过如下计算得出：
P＝1-E r
其中，P为可信度。
(3)对激光传感器的测量结果进行加权修正。
U＝U′+P*Δ
其中，U为修正后的测量结果，U′为测量结果，Δ为加权修正的常数，该常数可通过试验得到。
步骤S707为将车辆从GNSS系统获取的自车位置信息更新为上述修正后的测量结果U。
步骤S708表示结束车载单元的处理。
通过如上的路侧信息与车载信息的融合，能够实现通过与路侧信息的融合来提高定位精度。且通过路侧单元中包含激光雷达的构成可实现通过较少的路侧硬件设备及普通定位精度车载定位设备，即使在周围有高大建筑物遮挡的道路中也能对车辆进行高精度定位。
图8为表示第3实施方式的路侧单元的处理流程图。图9为表示第3实施方式的坐标示意图。
在本实施方式中利用摄像头与毫米波雷达的组合作为路侧传感器。
步骤S801表示路侧单元开始工作。步骤S802为从辅助定位设备读取该传感器组合自身的高精度位置信息或从特定服务器获取该传感器组合的高精度位置信息及相关安装信息。其后，相机与毫米波雷达分别各自检测信息。步骤S803为摄像头采集图像。步骤S804为检测所采集图像中的车辆图像并进行追踪。步骤S805为检测车辆图像中的车牌信息。对于车辆及车牌信息的检测例如通过现有的深度学习算法，如Yolo算法等实现。
步骤S806为通过图像对检测的车辆进行位置估计。如图10所示，摄像头的成像原理例如被简化为小孔成像模型。其中O C-X CY CZc为摄像头坐标系，O’-X’Y’为图像物理坐标系，O-uv为图像像素坐标系。点Q在摄像头坐标系下的坐标为(Xc,Yc,Zc)，点q为点Q在成像平面的投影，在图像物理坐标系下的坐标为(Xc’,Yc’)，在图像像素坐标系下的坐标为(u,v)。通过相似三角形可知：
Figure PCTCN2022089944-appb-000004
图像像素坐标系和图像物理坐标系之间存在缩放和平移，即
Figure PCTCN2022089944-appb-000005
结合上述两式可以得到摄像头坐标系向图像像素坐标系的转换关系为：
Figure PCTCN2022089944-appb-000006
式中，f x，f y，u 0和v 0为摄像头的内部参数、f为相机焦距。
进一步，利用传感器安装信息中摄像头的安装高度及仰俯角等信息可计算出图像中物体的位置信息为[id i,x i,y i,v xi,v yi]。
步骤S807为毫米波雷达获取雷达点云。步骤S808为对毫米波雷达点云进行降噪滤波。步骤S809为测量进入其测量范围的车辆的位置信息。步骤S810为计算车辆的距离与速度信息。利用多普勒效应，毫米波雷达可以准确测量出检测车辆的距离与速度信息。
步骤S811为计算检测各检测物距离，即计算摄像头检测到的车辆集合A中各车辆与毫米波雷达检测到的车辆集合B中所有车辆的距离。步骤S812为将车辆的位置速度信息 [id j,x j,y j,v xj,v yj]，与摄像头数据进行融合。距离车辆集合A中某车辆最近的点，就是毫米波检测的相同车辆的信息，即可关联同一车辆摄像头的检测信息与毫米波雷达的检测信息并对同一车辆的位置速度信息与摄像头数据进行融合。由于毫米波雷达无法对车辆进行识别，采用摄像头的检测到的车牌信息；同时，由于毫米波雷达检测到的速度、位置、车辆朝向角等精度远高于摄像头，故采用毫米波雷达检测到的位置与车速信息等信息，得到摄像头与毫米波雷达检测的融合结果为：[id k,x k,y k,v xk,v yk,θ k]。步骤S813为提取的车辆高精度位置信息、车速等信息。
步骤S813为提取对同一车辆的位置速度信息与摄像头数据进行融合后的信息中的车辆的高精度绝对位置信息、车速信息等信息作为广播信息。步骤S814为将提取的信息作为广播信息进行广播。步骤815为判断路侧单元的处理流程是否结束。步骤S816表示结束路侧单元的处理流程。
本实施例中，通过路侧单元中包含的摄像头直接获取车牌信息作为车辆号码，并将包含车辆号码的广播信息进行广播。车载单元获取该广播信息后，可利用广播信息中所包含的车牌信息容易地判别其中属于自身车辆的车辆信息，并利用对应的路侧单元检测的自身车辆的高精度绝对位置信息对车辆从GNSS系统获取自车位置信息进行融合修正。
通过如上的路侧信息与车载信息的融合，能够实现通过与路侧信息的融合来提高检测精度。且通过路侧单元中包含摄像头与毫米波雷达的组合作为路侧传感器的构成可实现通过成本低廉的路侧硬件设备及普通定位精度车载定位设备，即使在周围有高大建筑物遮挡的道路中也能对车辆进行高精度定位。
图10为表示广播信息的格式的示意图。广播信息分为头部信息与车辆信息两部分，其中车辆信息根据检测到的车辆数目可以为多项。头部信息中包括消息编号、时间戳、检测到的车辆数量及预留项。消息编号随路侧单元的广播信息的数量单调递增。时间戳为消息发送时的时刻。车辆数量为当前广播信息中所包含的车辆数量，通过该项信息可以确定车辆信息部分的长度。每项车辆信息包含车辆编号、车辆横向尺寸、车辆纵向尺寸、车辆中心坐标、车速、车辆行进方向及预留项等。车辆号码为车辆的识别号。在路侧单元中包含激光雷达时，车辆号码为系统定义的编号，如7位字符串等。检测到的同一车辆使用同一编号，不同车辆使用不同编号。同一车辆的判别可以通过已知的签署的车辆跟踪算法来实现。在路侧单元中包含摄像部和毫米波雷达时，车辆号码可以为车牌号。通过将车牌号 码作为车辆号码可使车辆更容易地提取自车信息。车辆横向尺寸、车辆纵向尺寸、车辆中心坐标、车速、车辆行进方向等车辆相关信息为路端传感器的检测与计算结果。
以上说明了本发明的各种实施方式以及变形例，但本发明不限定于这些内容。在本发明的技术思想的范围内考虑到的其他方式也包括在本发明的范围内。
符号说明
1：路侧单元，2：车辆1，3：车辆2，4：GNSS。

Claims (8)

  1. 一种车辆定位系统，其特征在于，所述系统包括：
    路侧单元，所述路侧单元进一步包括路侧传感器部、路侧计算部、路侧定位部以及路侧通信部，其中
    所述路侧传感器部，其检测进入检测范围内的车辆；
    所述路侧计算部，其计算检测到的车辆的位置信息；
    所述路侧定位部，其根据所述路侧传感器部的高精度位置信息及所述检测到的车辆的位置信息计算所述检测到的车辆的高精度绝对位置信息；以及
    所述路侧通信部，其将包含所述检测到的车辆的高精度绝对位置信息的广播信息进行广播，
    所述系统还包括车载单元，所述车载单元进一步包括车载定位部、车载通信部以及车载计算部，其中
    所述车载定位部，其通过车载传感器记录自车位置信息；
    所述车载通信部，其接收所述广播信息；以及
    所述车载计算部，其从接收到的所述广播信息中获取自车的高精度绝对位置信息，并将所述自车位置信息替换为所述自车的高精度绝对位置信息。
  2. 根据权利要求1所述的车辆定位系统，其特征在于，
    所述路侧传感器部进一步包含摄像部及毫米波雷达，其中
    所述摄像部，其采集图像并检测所述图像中的车辆图像与所述车辆图像中的车牌信息；以及
    所述毫米波雷达，其测量进入其测量范围的车辆的距离与速度信息。
  3. 根据权利要求2所述的车辆定位系统，其特征在于，
    所述路侧计算部融合所述车牌信息与所述车辆的距离与速度信息，得到与所述车牌信息对应的车辆的距离与速度信息，并根据所述车辆的距离与速度信息进一步计算得与所述车牌信息对应的车辆的位置信息。
  4. 根据权利要求1所述的车辆定位系统，其特征在于，
    所述路侧传感器部进一步包含激光雷达，所述激光雷达测量检测范围内的车辆的信息，
    所述路侧计算部根据所述车辆的信息计算所述检测范围内的车辆的位置信息。
  5. 根据权利要求4所述的车辆定位系统，其特征在于，
    所述车载计算部根据所述车载通信部接收的所述广播信息得到多条接收轨迹，从所述多条接收轨迹中判别自车的轨迹，并将所述自车的轨迹作为所述自车的高精度绝对位置信息。
  6. 根据权利要求5所述的车辆定位系统，其特征在于，
    所述车载计算部根据所述车载定位部记录的自车位置信息得到自车记录轨迹，通过计算所述自车记录轨迹与所述多条接收轨迹的距离来从所述多条接收轨迹中判别自车的轨迹。
  7. 一种车辆定位方法，其特征在于，包括如下步骤：
    车辆检测步骤，其通过路侧传感器部检测进入检测范围内的车辆；
    路侧计算步骤，其计算检测到的车辆的位置信息；
    路侧定位步骤，其根据所述路侧传感器部的高精度位置信息及所述车辆的位置信息计算所述车辆的高精度绝对位置信息；
    路侧通信步骤，其将包含所述车辆的高精度绝对位置信息的广播信息进行广播；
    车载定位步骤，其通过车载传感器记录自车位置信息；
    车载通信步骤，其接收所述广播信息；以及
    车载计算步骤，其从接收到的所述广播信息中获取自车的高精度绝对位置信息，并将所述自车位置信息替换为所述自车的高精度绝对位置信息。
  8. 一种用于车辆定位的路侧装置，其特征在于，所述路侧装置包括：
    路侧传感器部，其检测进入检测范围内的车辆；
    路侧计算部，其计算检测到的车辆的位置信息；
    路侧定位部，其根据所述路侧传感器部的高精度位置信息及所述车辆的位置信息计算所述车辆的高精度绝对位置信息；以及
    路侧通信部，其将包含所述车辆的高精度绝对位置信息的广播信息进行广播。
PCT/CN2022/089944 2021-04-30 2022-04-28 车辆定位系统、方法及路侧装置 WO2022228523A1 (zh)

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