WO2022089194A1 - 一种基于毫米波雷达数据的车道线形检测方法 - Google Patents

一种基于毫米波雷达数据的车道线形检测方法 Download PDF

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WO2022089194A1
WO2022089194A1 PCT/CN2021/123242 CN2021123242W WO2022089194A1 WO 2022089194 A1 WO2022089194 A1 WO 2022089194A1 CN 2021123242 W CN2021123242 W CN 2021123242W WO 2022089194 A1 WO2022089194 A1 WO 2022089194A1
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data
lane
vehicle
wave radar
millimeter
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PCT/CN2021/123242
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French (fr)
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王俊骅
宋昊
傅挺
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同济大学
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    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/42—Simultaneous measurement of distance and other co-ordinates
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/42—Simultaneous measurement of distance and other co-ordinates
    • G01S13/44—Monopulse radar, i.e. simultaneous lobing
    • G01S13/4418—Monopulse radar, i.e. simultaneous lobing with means for eliminating radar-dependent errors in angle measurements, e.g. multipath effects
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/52—Discriminating between fixed and moving objects or between objects moving at different speeds
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G01S13/60—Velocity or trajectory determination systems; Sense-of-movement determination systems wherein the transmitter and receiver are mounted on the moving object, e.g. for determining ground speed, drift angle, ground track
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726—Multiple target tracking
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/91—Radar or analogous systems specially adapted for specific applications for traffic control
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/415—Identification of targets based on measurements of movement associated with the target
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/418—Theoretical aspects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques

Definitions

  • the invention relates to the technical field of big data application, in particular to a lane alignment detection method based on millimeter wave radar data.
  • the existing lane alignment detection methods rely on video images for secondary development and identification, and there is no non-visual alignment detection method.
  • the secondary development data fusion with the radar can match the lane data and the traffic flow data obtained by the millimeter wave radar together, which has poor adaptability and high cost.
  • the purpose of the present invention is to provide a lane alignment detection method based on millimeter-wave radar data in order to overcome the above-mentioned defects in the prior art. Get more accurate lane lines.
  • the lane alignment detection method based on millimeter wave radar data provided by the present invention at least includes the following beneficial effects:
  • the data used in the detection of the lane line by the method of the present invention is the data collected by the fixed millimeter-wave radar detection equipment on the roadside, and the historical radar data and real-time radar data are used, which has the characteristics of high detection accuracy and fast detection speed.
  • the horizontal clustering determines the number of lanes according to the vehicle trajectory, and uses this as the reference point for subsequent radial clustering. Based on the horizontal reference point, the radial extension is carried out to obtain a more accurate lane line;
  • the lane lines are corrected by the statistical analysis module, which can effectively avoid the uneven driving trajectory and the problem of uneven or biased radial clustering points of the extracted lane lines;
  • the present invention eliminates the erroneous data contained in the radar detection track data of the vehicle, and by judging the continuity of the reflected data, eliminates the loss of data, the occlusion of the reflection area between two adjacent vehicles, the positioning failure, the network transmission error, the static If the trajectory data is missing or the data field is abnormal due to object reflection noise and other reasons, the data can be made more accurate and more accurate lane lines can be obtained;
  • the lane alignment of the road can be accurately determined only according to the data obtained by the millimeter wave radar, the cost is low, and the lane data and the traffic flow data obtained by the millimeter wave radar can be well matched together, and the adaptability is higher. .
  • FIG. 1 is a schematic diagram of a millimeter-wave radar installation method and a sensing range of a lane alignment detection method based on millimeter-wave radar data in an embodiment
  • FIG. 2 is a schematic flowchart of a lane alignment detection method based on millimeter wave radar data in an embodiment.
  • the invention relates to a lane alignment detection method based on millimeter-wave radar data, which can make full use of the data returned by the millimeter-wave radar to realize lane alignment perception in a statistical sense.
  • the method includes the following steps:
  • the millimeter-wave radar installed on the traffic road is used to perceive the vehicles moving on the traffic road, and the vehicle trajectory data and vehicle radar reflection data detected by the millimeter-wave radar are obtained.
  • Millimeter-wave radar can detect and perceive the position of objects within a certain distance by installing it on a pole of a certain height while being properly inclined.
  • the installation method and sensing range of the millimeter-wave radar are shown in Figure 1.
  • cross bars of a certain height are set on both sides of the road, and the millimeter The wave radar is installed in the center of the crossbar to detect the position and speed of the objects in the lane including the vehicle.
  • the width of lane 1 and lane 2 is 3.75m
  • the width of the emergency stop zone is 3m
  • the height of the pole is set to 8m
  • the length of 250m can be obtained by using millimeter-wave radar, and the width exceeds the detection range of the overall road width.
  • the fields of the vehicle trajectory data detected by the millimeter wave radar include: vehicle ID, timestamp, the radial coordinate of the vehicle relative to the radar, the tangential coordinate of the vehicle relative to the radar, the radial component of the vehicle speed, and the tangential component of the vehicle speed.
  • Vehicle radar reflection data includes radar reflection area, latitude and longitude of track points, average speed and direction recognition track data corresponding to track points.
  • two sets of data sets are established in the database, one set is trace, which is the vehicle trajectory data set, and the other set is roadpoint, which is the road point data set obtained after the road is rasterized.
  • the average velocity corresponding to the trajectory point refers to the average velocity of the trajectory segment composed of the trajectory point and the trajectory point preceding the trajectory point.
  • the vehicle trajectory data detected by the millimeter-wave radar is read through the trajectory reading algorithm.
  • the data uses historical data and real-time input data as input data, which is convenient to quickly enable lane line extraction, and can be continuously adjusted to reduce radar detection.
  • the data errors caused by the vibration of the detection equipment due to road traffic, wind and other factors are detected, and the radar time series data is obtained.
  • a trajectory data screening module is established to perform preliminary data quality screening and read the radar data detected by the millimeter-wave radar equipment.
  • the trajectory data screening module identifies the erroneous data contained in the trajectory data according to the reflection area in the vehicle radar reflection data, the latitude and longitude of the trajectory point and/or the average speed and direction corresponding to the trajectory point, and eliminates the data loss and the gap between two adjacent vehicles due to data loss.
  • the trajectory data is missing or the data field is abnormal due to reasons such as reflection area occlusion, positioning failure, network transmission error, static object reflection noise, etc.
  • the radar reflection area is judged, and the reflection data with a width of more than 5 meters and a length of more than 25 meters are excluded. Because the width of the reflective area is more than 5 meters, the object with a length of more than 25 meters must not be a vehicle. It is likely to be a large area of green plants, guardrails and auxiliary sign billboards.
  • the erroneous trajectory contained in the trajectory data is identified.
  • the radar reflection data also includes the reflection time, and the time stamp is obtained by obtaining the reflection time to obtain the time stamp of the frame data.
  • the frame is defined by the acquired reflection time, and each reflection time corresponds to a timestamp, that is, a frame.
  • the continuity of the vehicle radar reflection data is judged. Since the pointer of the radar data is used cyclically, it is necessary to distinguish the objects with the same pointer. For the objects with the same pointer (that is, the radar data ID corresponds to the same object) ), if there are discontinuous occurrences in different frames, it is determined as a different car.
  • Cluster analysis is performed on the cleaned data, and the lane line output is extracted.
  • the clustering module is divided into horizontal clustering and radial clustering (horizontal refers to the direction parallel to the cross-section of the road surface, and radial refers to the direction parallel to the lane line. ).
  • Horizontal clustering is to perform horizontal initial stable point clustering.
  • the purpose of horizontal initial stable point clustering is to first determine the number of lanes according to the vehicle trajectory, and use this as a reference point for subsequent acquisition of the lane alignment.
  • the horizontal clustering is in the millimeter wave radar.
  • the device detects the cross-section of the midpoint segment of the data retention segment, and obtains the continuous centerline of each lane of the road.
  • the purpose of radial clustering is to extend radially based on a stable lateral reference point. This defines the lane alignment.
  • Radial clustering is to cluster all trajectory points.
  • the specific content of radial clustering is:
  • the vehicle trajectory of a certain road section obtained by the millimeter-wave radar is divided into several segments by 0.3 meters, and each segment of the trajectory is clustered according to the Euclidean distance. , obtain the average coordinate point of all trajectory points of each trajectory, as the virtual geometric center (X Ti , Y Ti ) of each trajectory.
  • the grid cell size is 0.1 m ⁇ 0.1 m, select the grid point (X Ri , YR i ) closest to (X Ti , Y Ti ) in the roadpoint dataset.
  • a series of (X Ri , Y Ri ) points that is, (X Ri , Y Ri ) points of each path segment, are radially connected and smoothed to obtain a continuous road centerline as the basis for road alignment.
  • the horizontal clustering is to obtain the linear shape of each lane. Specifically, the horizontal clustering is performed according to the lane-by-lane obtained by the millimeter-wave radar. Perform horizontal initial stable point clustering for each track respectively, and determine the horizontal clustering method according to the number of road lanes on site. The continuous centerline of each lane. If the on-site road has two lanes, the track points are horizontally clustered into two points, and this step is repeated to obtain the continuous centerline of each lane of the road, and the alignment of the entire road segment is obtained according to the continuous centerline.
  • the radial clustering of the present invention is to obtain the center point of the entire road section, and the lateral clustering is to obtain the center point of each lane of the road section, one is to obtain the line shape of the entire road section, and the other is to obtain the line shape of each lane.
  • the horizontal clustering obtains the center point of each lane of the road section, and then the lane width corresponding to each lane can be obtained, and the alignment of each lane can be further obtained according to the lane width of each lane.
  • the radial clustering can obtain the line shape of the entire road segment, and then can determine the lane direction. Combined with the alignment of each lane, the width of the lane and the direction of the lane, the actual lane alignment of the road segment can be determined.
  • Both horizontal clustering and vertical clustering require several clustering steps, and the point obtained by the first clustering is called the initial stable point.
  • the present invention firstly adopts a special clustering method sensitive to single point to determine the initial stable point. point, as the reference point for subsequent acquisition of this lane alignment.
  • the initial stable point is the center point of the entire road section, and the first point obtained through clustering must be the reference point for subsequent clustering and also the initial stable point. Because the number of vehicles in some lanes is much smaller than other lanes (such as truck lanes), for lanes with few vehicle trajectories, in order to avoid too few trajectory points and they are ignored during clustering, it is necessary to use a small number of points.
  • the clustering method is also very sensitive to clustering, that is, the initial stable point is determined for the single-point-sensitive clustering method.
  • the determined initial stable point represents the number of stable lanes, which ensures the accuracy and stability of the subsequent acquisition of the lane alignment. , thereby improving the calculation stability of the method of the present invention.
  • the longitudinal trajectory points of each lane are clustered. Uniformity, which brings about the problem of unevenness or deviation in the extraction of lane line radial clustering points.
  • the driving trajectory is corrected, mainly based on the statistical results of the interval trajectory to calculate the vehicle deflection angle in this process, so as to calculate the vehicle deflection angle in this process. Lane lines are corrected.
  • the statistical result of the interval trajectory is to calculate the average of the tangential angles of all the trajectories of the road section, and the average value is taken as the vehicle deflection angle, and the vehicle deflection angle is taken as the lane line deflection angle of the road section.
  • the lane lines with the perfect detection range are output. And through the data input, the above method process is repeated continuously, so as to continuously adjust and output the lane line.

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Abstract

本发明涉及一种基于毫米波雷达数据的车道线形检测方法，利用交通道路上安装的毫米波雷达，感知交通道路上移动的车辆，获取毫米波雷达检测的车辆轨迹数据和车辆雷达反射数据；在数据库中建立两套数据集，包括车辆轨迹数据集和将道路栅格化后得到的路点数据集；对毫米波雷达检测的车辆轨迹数据和车辆雷达反射数据进行筛选，剔除错误数据；对筛选后的数据进行径向聚类和横向初始稳定点聚类，提取车道线形并输出。与现有技术相比，本发明具有获取更加准确的车道线，成本低，适配性好等优点。

Description

一种基于毫米波雷达数据的车道线形检测方法 技术领域
本发明涉及大数据应用技术领域，尤其是涉及一种基于毫米波雷达数据的车道线形检测方法。
背景技术
随着毫米波雷达硬件成本逐步降低，部分军用高精度毫米波雷达逐步开源给民用领域，毫米波雷达在交通领域的应用逐步扩大，因此对路域的车辆轨迹信息采集分析可基于毫米波雷达的数据来进行。而在大数据时代，由于车辆轨迹数据量极大，车流轨迹数据可以在统计意义上反映车道线形，而车道线是道路本身的基本属性之一的体现，是后续进行相关研究的重要先决条件，因此通过毫米波雷达间接实现车道线形检测成为可行的方案。目前对车道线的检测主要是以实际拍摄的图像分析为主。在毫米波雷达作为主要传感器的路段，采用传统的图像来提取车道线势必会带来一些额外的经济成本，如安装视频设备，以及基于图像和轨迹数据的二次研发和调试等。此外可能还会带来两种设备的数据对接等一系列问题。
另外，现有的车道线形检测方法都是依托视频图像进行二次开发识别，没有非视认性的线形检测方法，而对于将毫米波雷达作为主要传感器的道路，如果采用摄像机做车道线形检测需要与雷达进行二次开发数据融合，才能将车道数据和毫米波雷达获取的车流数据匹配到一起，适配性差，成本高。
发明内容
本发明的目的就是为了克服上述现有技术存在的缺陷而提供一种基于毫米波雷达数据的车道线形检测方法，该方法充分利用毫米波雷达返回的数据，从统计学意义上实现车道线形感知，可获取更加准确的车道线。
本发明的目的可以通过以下技术方案来实现：
本发明提供的基于毫米波雷达数据的车道线形检测方法，相较于现有技术至 少包括如下有益效果：
一、本发明方法检测车道线所用的数据为路侧固定的毫米波雷达检测设备采集数据，采用的是历史的雷达数据以及实时的雷达数据，具有检测精度高，检测速度快的特点，填补了在使用毫米波雷达采集车辆轨迹领域的车道线形检测领域的空白；
二、利用横向聚类和径向聚类相结合的方法确定车道线，横向聚类依据车辆轨迹确定出车道数目，并以此为后续径向聚类的参考点，径向聚类以稳定的横向参考点为基础，进行径向的延伸，进而获取更加准确的车道线；
三、在聚类算法中，采用横向聚类确定稳定车道数目，可避免部分车道由于行车数量过少，带来的轨迹点过少，从而使得成为离群点导致的聚类类别缺少，带来车道线形提取错误的问题；
四、在径向聚类过程中，通过统计分析模块对车道线进行修正，能够有效避免行车轨迹不均匀，带来的提取车道线径向聚类点不均匀或者偏差的问题；
五、本发明消除车辆的雷达检测轨迹数据中包含的错误数据，并通过判断反射数据连续性，消除由于数据丢失、相邻两辆车之间存在反射面积遮挡、定位故障、网络传输错误、静态物体反射噪点等原因造成轨迹数据缺失或数据字段异常，可使得数据更加准确，有利于得到更加准确的车道线；
六、仅需要根据毫米波雷达获取的数据即可准确地确定道路的车道线形，所需成本低，且能够将车道数据和毫米波雷达获取的车流数据良好的匹配到一起，适配性更高。
附图说明
图1为实施例中基于毫米波雷达数据的车道线形检测方法的毫米波雷达安装方法及感知范围示意图；
图2为实施例中基于毫米波雷达数据的车道线形检测方法的流程示意图。
具体实施方式
下面结合附图和具体实施例对本发明进行详细说明。显然，所描述的实施例是本发明的一部分实施例，而不是全部实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都应属于本发 明保护的范围。
实施例
本发明涉及一种基于毫米波雷达数据的车道线形检测方法，该方法可以充分利用毫米波雷达返回的数据，从统计学意义上实现车道线形感知。该方法包括如下步骤：
首先利用交通道路上安装的毫米波雷达，感知交通道路上移动的车辆，获取毫米波雷达检测的车辆轨迹数据和车辆雷达反射数据。
毫米波雷达通过安装在一定高度的杆件上的同时适当倾斜，可实现对一定距离范围内物体位置的探测和感知。在本实施例中，毫米波雷达安装方法及感知范围如图1所示，对于三车道而言，包括车道1、车道2以及紧急停车带，在道路两侧设置一定高度的横杆，将毫米波雷达安装在横杆中央，以探测车道上包括车辆在的物体位置、速度等信息。当车道1、车道2的宽度为3.75m，紧急停车带的宽度为3m，并设置杆高为8m时，利用毫米波雷达可获知长度250m，宽度超过整体道路宽度的探测范围。
毫米波雷达检测的车辆轨迹数据的字段包括：车辆ID，时间戳，车辆相对雷达的径向坐标，车辆相对雷达的切向坐标，车辆速度的径向分量和车辆速度的切向分量。车辆雷达反射数据包括雷达反射面积、轨迹点的经纬度、轨迹点对应的平均速度及方向识别轨迹数据。同时在数据库中建立两套数据集，一套为trace，即车辆轨迹数据集，另一套为roadpoint，即将道路栅格化后得到的路点数据集。轨迹点对应的平均速度指的是该轨迹点与该轨迹点的前一位轨迹点组成的轨迹段的平均速度。
在本实施例中，通过轨迹读取算法读取毫米波雷达检测的车辆轨迹数据，该数据以历史数据以及实时输入的数据为输入数据，便于快速启用车道线提取，并且能够不断调整从而减少雷达设备在运行过程中由于路面交通，风等其他因素带来的检测设备的振动带来的数据误差，得到雷达时序数据。
然后建立轨迹数据筛选模块，进行初步数据质量筛选，读取毫米波雷达设备检测到的雷达数据。轨迹数据筛选模块依据车辆雷达反射数据中的反射面积、轨迹点的经纬度和/或轨迹点对应的平均速度及方向识别轨迹数据中包含的错误数据，消除由于数据丢失、相邻两辆车之间存在反射面积遮挡、定位故障、网络传输错误、静态物体反射噪点等原因造成轨迹数据缺失或数据字段异常，具体地：
对雷达反射面积进行判断，剔除反射面积宽度超过5米，长度超过25米的反射数据。因反射面积宽度超过5米，长度超过25米的物体一定不是车辆，很有可能是大面积绿植、护栏及附属标志广告牌。
依据毫米波雷达检测的车辆轨迹数据中轨迹点的经纬度和/或轨迹点对应的平均速度，识别轨迹数据中包含的错误轨迹。识别某一轨迹点是不是错误轨迹点，不仅需要分析该轨迹点本身的经纬度和或平均速度，还需要分析相邻轨迹点或相邻轨迹段的经纬度和或平均速度。当该轨迹点的经纬度超出同一时间戳相邻轨迹点的位置范围，或当该轨迹点的经纬度超出相邻时间戳轨迹点的位置范围，或当该轨迹点的平均速度与同一时间戳相邻轨迹点速度差超过5m/s，则同样认为很可能是错误轨迹点。雷达反射数据还包括反射时间，时间戳由通过获取反射时间，得到该帧数据的时间戳。通过获取的反射时间来定义帧，每个反射时间对应一个时间戳，也就是一帧。
剔除错误数据后，对车辆雷达反射数据的连续性进行判断，由于雷达数据的指针是循环使用的，所以要对相同指针的物体进行区分，对于相同指针的物体(即雷达数据ID对应相同的物体)，若在不同帧中出现的不连续出现的情况，则判定为不同的车。
对清洗后的数据进行聚类分析，提取车道线形输出，聚类模块分为横向聚类以及径向聚类(横向指的是平行于路面横断面方向，径向指的是平行于车道线方向)。横向聚类就是进行横向初始稳定点聚类，横向初始稳定点聚类的目的是依据车辆轨迹首先确定出车道数目，并以此为后续获取这个车道线形的参考点，横向聚类处于毫米波雷达设备检测数据保留段的中点段的横断面处，获取道路每个车道的连续中心线。径向聚类的目的是，以稳定的横向参考点为基础，进行径向的延伸。从而定出车道线形。径向聚类是对所有的轨迹点进行聚类。径向聚类的具体内容为：
对清洗后所有的轨迹点进行分段聚类，本实施例将毫米波雷达获取的某个路段的车辆轨迹按0.3米分段，分为若干段，对每段轨迹根据欧式距离分别进行聚类，获取每段轨迹所有轨迹点的平均坐标点，作为每段轨迹的虚拟几何中心(X Ti，Y Ti)。
以整个路段毫米波雷达第一个所监测的路段的聚类中心坐标(X T0，Y T0)为中心(也作为径向聚类的初始稳定点)，建立道路栅格网络，并放入roadpoint数据集，栅格单元大小为0.1米×0.1米，选取roadpoint数据集中与(X Ti，Y Ti)最近的栅格点(X Ri，YR i)。
对一系列(X Ri，Y Ri)点，也就是各段路径的(X Ri，Y Ri)点进行径向连接，并进行平滑处理，获取连续的道路中心线，作为道路线形依据。
横向聚类则是要获取每个车道的线形，具体地，横向聚类根据毫米波雷达获取的按车道进行聚类。对每段轨迹分别进行横向初始稳定点聚类，根据现场道路车道数量确定横向聚类的方法，如果是三车道则对轨迹点横向聚类为三个点位，重复上述步骤，从而获取道路每个车道的连续中心线。若现场道路为两车道，则对轨迹点横向聚类为两个点位，重复该步骤，从而获取道路每个车道的连续中心线，根据连续中心线获取整个路段的线形。本发明的径向聚类是获取整个路段的中心点，横向聚类是获取路段每个车道的中心点，一个是获取整个路段的线形，一个是获取各个车道的线形。横向聚类获取路段每个车道的中心点，进而能够获取各车道对应的车道宽度，根据各车道的车道宽度进一步能够得到各个车道的线形。而径向聚类能够获取整个路段的线形，进而可确定车道走向。结合各个车道的线形、车道宽度以及车道走向，可确定获取该路段的实际车道线形。
横向聚类和纵向聚类都是要通过若干次聚类的步骤，将第一次聚类获取的点称为初始稳定点。
为了避免在横向初始稳定点聚类过程中存在的聚类初始稳定点的选择导致聚类不准确或聚类类别缺失的问题，本发明首先采用特殊的针对单点敏感的聚类方法确定初始稳定点，作为后续获取这个车道线形的参照点。初始稳定点为整个路段的中心点，通过聚类得到的第一个点必然是后续聚类的参考点，也是初始稳定点。因部分车道的车辆数要远远小于其他车道(如货车车道)，对于车辆轨迹很少的车道，为避免其轨迹点过少而在聚类时被忽视，因而需要采用对数量很少的点聚类也很敏感的聚类方式，即针对单点敏感的聚类方法进行初始稳定点的确定，确定的初始稳定点即表示稳定车道的数目，保证了后续获取这个车道线形的精度与稳定性，进而提高本发明方法的计算的稳定性。
在径向聚类的方法中，由于车道线本身符合连续性，以及在平面上满足平曲线的线形设置，在聚类过程中，对各个车道纵向的轨迹点进行聚类，为了避免行车轨迹不均匀，带来的提取车道线径向聚类点不均匀或者偏差的问题，通过统计分析模块，对行车轨迹进行修正，主要为依据区间轨迹的统计结果，计算这一过程车辆偏转角度，从而对车道线进行修正。区间轨迹的统计结果为通过对该路段所有轨迹的切向角度求平均值，将改平均值作为车辆偏转角度，并将车辆偏转角度作为该路段 的车道线偏转角度。
经过以上步骤，输出已经完善的检测范围车道线。并不断经过数据的输入，不断的重复以上方法过程，从而不断的调整并输出车道线。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的工作人员在本发明揭露的技术范围内，可轻易想到各种等效的修改或替换，这些修改或替换都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以权利要求的保护范围为准。

Claims (10)

  1. 一种基于毫米波雷达数据的车道线形检测方法，其特征在于，包括下列步骤：
    1)利用交通道路上安装的毫米波雷达，感知交通道路上移动的车辆，获取毫米波雷达检测的车辆轨迹数据和车辆雷达反射数据；
    2)在数据库中建立两套数据集，包括车辆轨迹数据集和将道路栅格化后得到的路点数据集；
    3)对毫米波雷达检测的车辆轨迹数据和车辆雷达反射数据进行筛选，剔除错误数据；
    4)对筛选后的数据分别进行横向聚类和径向聚类，结合横向聚类和径向聚类的结果提取车道线形并输出。
  2. 根据权利要求1所述的基于毫米波雷达数据的车道线形检测方法，其特征在于，所述毫米波雷达检测的车辆轨迹数据包括车辆ID，时间戳，车辆相对雷达的径向坐标，车辆相对雷达的切向坐标，车辆速度的径向分量和车辆速度的切向分量。
  3. 根据权利要求1所述的基于毫米波雷达数据的车道线形检测方法，其特征在于，所述车辆雷达反射数据包括雷达反射面积、轨迹点的经纬度、轨迹点对应的平均速度及方向识别轨迹数据。
  4. 根据权利要求3所述的基于毫米波雷达数据的车道线形检测方法，其特征在于，步骤3)中，对车辆雷达反射数据剔除错误数据的具体内容为：
    对雷达反射面积进行判断，剔除雷达反射面积宽度超过5米，长度超过25米的反射数据；
    根据轨迹点经纬度是否在前后两个时间戳位置之间判断错误反射数据，若超出相邻时间戳的位置范围，则判断为错误轨迹点；或根据轨迹点平均速度是否与前后两个时间戳对应的速度差过大，若速度差过大，则判断为错误轨迹点。
  5. 根据权利要求1所述的基于毫米波雷达数据的车道线形检测方法，其特征在于，步骤4)中，所述横向聚类的具体内容为：
    根据现场道路车道数量确定横向聚类的方法，对某道路路段的车辆轨迹的轨迹 点横向聚类为对应数量的点位，若现场道路为三车道，则对轨迹点横向聚类为三个点位，若现场道路为两车道，则对轨迹点横向聚类为两个点位，重复该步骤，从而获取道路每个车道的连续中心线，根据连续中心线获取整个路段的线形。
  6. 根据权利要求5所述的基于毫米波雷达数据的车道线形检测方法，其特征在于，步骤4)中，所述径向聚类的具体内容为：
    将剔除错误数据后的毫米波雷达检测的某道路路段的车辆轨迹每隔一段距离进行分段，对每段轨迹的轨迹点分别进行聚类，获取每段轨迹所有轨迹点的平均坐标点，作为每段轨迹的虚拟几何中心(X Ti，Y Ti)；以(X T0，Y T0)为中心，建立道路栅格网络并存入roadpoint数据集，选取roadpoint数据集中与(X Ti，Y Ti)最近的栅格点(X Ri，YR i)；对一系列(X Ri，Y Ri)点进行径向连接，并进行平滑处理，获取连续的道路中心线，作为道路线形依据。
  7. 根据权利要求5所述的基于毫米波雷达数据的车道线形检测方法，其特征在于，所述横向聚类中，采用单点敏感聚类方法确定第一次聚类的初始稳定点。
  8. 根据权利要求6所述的基于毫米波雷达数据的车道线形检测方法，其特征在于，所述横向聚类获取某道路路段的每个车道的中心点，进而获取各车道对应的车道宽度，根据各车道的车道宽度得到各个车道的线形；径向聚类获取整个路段的线形后，确定车道走向，结合各个车道的线形、车道宽度以及车道走向，确定该道路路段的实际车道线形。
  9. 根据权利要求6所述的基于毫米波雷达数据的车道线形检测方法，其特征在于，所述径向聚类过程中设有统计分析修正步骤：依据区间轨迹的统计结果，计算这一过程车辆偏转角度，从而对车道线进行修正。
  10. 根据权利要求3所述的基于毫米波雷达数据的车道线形检测方法，其特征在于，步骤3)后还包括车辆雷达反射数据的连续性判断步骤：对于毫米波雷达数据中具有相同指针的物体进行判断，若物体在不同帧中出现了不连续出现的情况，则将其判定为不同的车辆。
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