CN115440034A - Vehicle-road cooperation realization method and system based on camera - Google Patents
Vehicle-road cooperation realization method and system based on camera Download PDFInfo
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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Abstract
The invention provides a vehicle-road cooperation realization method and a realization system based on a camera, which comprise an intelligent network-connected vehicle-mounted subsystem arranged on a vehicle and an intelligent network-connected road-side subsystem arranged on a road side, wherein the vehicle-mounted camera and the road-side camera are time-synchronized through GNSS time service, the intelligent network-connected vehicle-mounted subsystem and the intelligent network-connected road-side subsystem convert pixel coordinates into WGS84 coordinates to realize the spatial alignment of the vehicle-mounted camera and the road-side camera, the intelligent network-connected road-side subsystem pushes a detection result to the vehicle, the vehicle is subjected to correlation fusion with the road-side detection result based on self RTK position information, and obstacles around the vehicle fuse the vehicle detection result and the road-side detection result.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to a vehicle-road cooperation realization method and system based on a camera.
Background
In recent years, the rapid development of information and communication technology has initiated the deep revolution of the traditional automobile industry, which has promoted the maturity of intelligent driving automobiles, and automobile driving is becoming more and more simple and intelligent. However, conventional autonomous and assisted driving systems typically rely solely on-board sensors to sense and understand the surrounding driving environment. On the one hand, this requires the deployment of advanced and complex sensor devices and computing storage devices on the vehicle, greatly increasing the manufacturing and maintenance costs of the vehicle. On the other hand, because the viewing angle of the vehicle-mounted sensor is usually low, the vehicle-mounted sensor is physically limited by severe weather and complex driving environments such as tunnel entrances and exits, intersections and the like, and the range and the accuracy of vehicle perception are greatly limited. 2020 world intelligent networking automobile congress, the Chinese society of automotive engineering college, the Chinese academy of engineers Li Jun teach 5 major challenges for single-car intelligence: 1) The artificial intelligence is highly depended on, so that the 'black box effect' is difficult to overcome; 2) Finally, road tests which need billions of miles are realized, and the road tests are difficult to realize in a short period; 3) Full autonomous driving requires at least millions of extreme condition data trains; 4) Too many sensing devices result in too high cost; 5) The actual driving safety is difficult to be absolutely guaranteed.
Therefore, in order to accurately and effectively sense the information of the target state in the traffic system, it is far from sufficient to use only the sensor of the vehicle and the computing resource, and the internet of vehicles (IoV) technology develops the route in cooperation with the vehicle road, thereby taking advantage of the fact. The technology of the Vehicle-to-electrical networking (V2X) is to connect all traffic units such as vehicles and other vehicles, vehicles and pedestrians, vehicles and roadside infrastructure and the like into a network by using an advanced mobile communication technology.
For a vehicle-road cooperative sensing system, the most important task is to identify and continuously track traffic participants such as vehicles, pedestrians and the like on a road by using a sensor to obtain an accurate track of each target so as to support subsequent path planning and decision making. At present, most schemes detect the tracking target by setting up a camera, because the vehicle-mounted camera becomes standard configuration, the roadside camera is also widely deployed, but how to effectively fuse the vehicle-mounted camera and the roadside camera, and the realization of cooperative vehicle road perception is still rarely studied at present. The existing research idea is to solve the problem by using multi-sensor fusion, pixel coordinates can be effectively converted into laser point cloud coordinates through combined calibration of a laser radar and a camera, and the laser point cloud coordinates are easily converted into northeast coordinates through rigid transformation, which is a common method for establishing a unified coordinate system in the multi-sensor fusion process. However, under the condition of no laser radar, how to construct a unified coordinate system for a vehicle-mounted camera and a road side camera to realize the vehicle-road cooperative sensing based on the cameras has no relevant research at present.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a vehicle-road cooperative implementation method and a vehicle-road cooperative implementation system based on a camera, which ensure the spatial alignment of the vehicle-road cooperative system in the information fusion process and can provide a uniform reference coordinate system for vehicle-road cooperative sensing, thereby improving the sensing precision. To achieve the above objects and other advantages in accordance with the present invention, there is provided a camera-based vehicle road coordination implementation system, comprising:
the intelligent network connection vehicle-mounted subsystem is arranged on the vehicle, and the intelligent network connection side subsystem is arranged on the road side, and the intelligent network connection vehicle-mounted subsystem and the intelligent network connection side subsystem realize vehicle-road cooperation through V2X.
Preferably, the intelligent network connection vehicle-mounted subsystem comprises an RTK, a vehicle-mounted camera, a vehicle-mounted computing device and a V2X device, and the devices are integrated together.
Preferably, the intelligent network connection side subsystem comprises a road side camera, a road side computing device and a V2X device, which are integrated together.
A vehicle-road cooperation realization method based on a camera comprises the following steps:
s1, time synchronization is achieved between an intelligent network connection side subsystem and an intelligent network connection vehicle-mounted subsystem through GNSS time service;
s2, the intelligent network connection road side subsystem realizes the spatial alignment of the road side camera through the conversion from the pixel coordinate to the WGS84 coordinate;
s3, the intelligent networking vehicle-mounted subsystem realizes conversion from pixel coordinates to WGS84 coordinates;
s4, the subsystem on the intelligent network connection side pushes the detection result to the subsystem on the intelligent network connection vehicle through V2X;
s5, information fusion is realized between the intelligent network connection vehicle-mounted subsystem and the intelligent network connection side subsystem through a unified WGS84 coordinate system;
preferably, the step S2 relates to a roadside camera, and an RTK may be installed on a test vehicle (a self vehicle in the intelligent internet protocol vehicle-mounted subsystem) to obtain a real-time GPS coordinate of the test vehicle, record a driving video of the test vehicle in a field area of the roadside camera, recognize a position of the test vehicle in real time through a target detection algorithm to obtain a pixel coordinate of the test vehicle, obtain more corresponding GPS coordinates-pixel coordinate pairs after time synchronization, perform training and test set division on the obtained data, perform training by using decision tree regression, a random forest regression algorithm or a neural network deep learning algorithm, and finally obtain a conversion model from the roadside camera pixel coordinate to the GPS coordinate.
Preferably, the step S3 relates to a vehicle-mounted camera, wherein the camera does not need to be calibrated by internal reference and external reference, and the conversion from the pixel coordinate to the WGS84 coordinate is directly realized by establishing a homography transformation matrix. The method comprises the following steps:
s1: a camera is fixedly arranged on the vehicle body, and a plurality of paper sheets with bright colors are selected to be placed in a proper position in the visual field range of the camera to be used as marks;
s2: acquiring the GPS coordinates of the marking paper sheet by using a handheld high-precision GPS;
s3: acquiring any one frame of detection picture of a camera, and acquiring pixel point coordinates of a marking paper in a picture;
s4: and solving homography transformation of a sensor coordinate system and a GPS coordinate system by using a corresponding GPS coordinate-pixel point coordinate pair through a least square method, and projecting the camera coordinate to the GPS coordinate system. When the sensor observes the P point simultaneously with the GPS, the coordinates of the P point under the pixel coordinate system Ouv and the GPS coordinate system OXY are defined as (u, v) and (X, Y), respectively, the transformation relationship between the two can be expressed as:
the camera coordinates of the detected object can be projected to a GPS coordinate system through matrix transformation, and N groups of space corresponding sets (u, v) and (X) are given i ,Y i ) (i =1,2,.., n). From equation (1), the following equation can be determined:
defining:
the least squares solution of the transformation matrix N is then:
thus, mapping of the pixel coordinates of the camera to the WGS84 coordinate system (i.e., GPS coordinates) is achieved by the solved transformation matrix.
Preferably, step S4 relates to V2X communication, the intelligent network link-side subsystem may send the message sensed by the road-side camera to the intelligent network link-mounted subsystem through a PC5 air interface or Uu air interface using a message set RSM, and the message encapsulation format may adopt JSON, protoBuf, or XML. Preferably, the step S5 relates to information fusion between the vehicle-mounted subsystem of the intelligent network connection and the subsystem on the side of the intelligent network connection. Aiming at the intelligent network connection vehicle-mounted subsystem, the vehicle information can utilize the position information provided by the vehicle-mounted RTK, and the obstacle information around the vehicle can utilize the perception object information provided by the vehicle-mounted camera, and is respectively fused with the information perceived by the intelligent network connection side subsystem. Information fusion consists of two steps, first object matching and second filtering. The target matching can adopt methods such as Global Nearest Neighbor (GNN), joint Probability Data Association (JPDA), multi-hypothesis tracking (MHT) and the like, and the filtering algorithm can adopt methods such as Kalman filtering, extended Kalman filtering, lossless Kalman filtering, particle filtering and the like.
Compared with the prior art, the invention has the following beneficial effects: the system is designed aiming at the requirement of vehicle-road cooperation on space-time synchronization, joint calibration is not needed to be carried out depending on a laser radar, a conversion model can be directly mapped to a GPS coordinate under WGS84 from a pixel coordinate of a camera, even for different subsystems, such as an intelligent network connection vehicle-mounted subsystem or an intelligent network connection side subsystem, the state of each target object under a unified coordinate system can be obtained, and further information fusion based on position points can be carried out, including fusion of vehicles and fusion of obstacles around the vehicles, so that vehicle-road cooperation perception is realized.
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FIG. 1 is an architecture diagram of a camera-based vehicle-road cooperative implementation method and implementation system according to the present invention;
fig. 2 is a schematic layout diagram of a camera-based vehicle-road cooperation implementation method and system according to the present invention.
Detailed Description
A more detailed description of a camera-based vehicle-road coordination implementation method and system according to the present invention will be given below in conjunction with a schematic diagram, in which a preferred embodiment of the present invention is shown, it being understood that a person skilled in the art may modify the invention described herein while still achieving the advantageous effects of the present invention, and therefore the following description should be understood as being widely known to a person skilled in the art and not as limiting the present invention.
Referring to fig. 1-2, a camera-based vehicle-road cooperative implementation system includes: the intelligent network connection vehicle-mounted subsystem is arranged on the vehicle, and the intelligent network connection side subsystem is arranged on the road side, and the intelligent network connection vehicle-mounted subsystem and the intelligent network connection side subsystem realize vehicle-road cooperation through V2X.
The intelligent network connection vehicle-mounted subsystem comprises an RTK, a vehicle-mounted camera, vehicle-mounted computing equipment and V2X equipment, and all the equipment are integrated together through a switch. RTK can be big dipper or GPS difference system, and on-vehicle camera includes forward view camera, back vision camera and look around the camera, and on-vehicle computing equipment includes embedded controller and industrial computer, and V2X equipment includes 5G 4GCPE, V2XOBU, and its space alignment process is: for each camera, a conversion model is established, and conversion from pixel coordinates to WGS84 coordinates is achieved. Here, the vehicle-mounted camera may be replaced by other vehicle-mounted sensing devices, such as a laser radar and a millimeter wave radar.
The intelligent network connection road side subsystem comprises a road side camera, road side computing equipment and V2X equipment, and all the equipment are integrated together through a switch. The roadside camera includes industrial camera, network camera, and roadside computing equipment includes MEC server, industrial computer, and V2X equipment includes 5G/4GCPE, V2XRSU, and its space alignment process is: and establishing a conversion model for each roadside camera to realize conversion from pixel coordinates to WGS84 coordinates.
Furthermore, the vehicle-mounted camera and the road side camera are synchronized in time through the NTP time server. Aiming at the vehicle-mounted or road-side camera, a homography transformation matrix can be established, and the conversion from pixel coordinates to WGS84 coordinates is realized, and the method comprises the following steps:
s1, fixedly mounting a camera, selecting a plurality of positions in the detection range of the camera, and placing marker paper sheets with bright colors on the positions to mark. The positions are equivalent to sampling of the detection range of the camera, and the selected positions are proper in distance and cover the detection range of the camera as much as possible. The more the number of the position selection is, the higher the calibration precision is, but the workload can be increased; otherwise, the less the number of position selections, the lower the calibration accuracy. In order to solve the homography transformation matrix, at least 9 position points need to be selected, and the distribution positions are shown in fig. 2.
And S2, measuring and recording the GPS coordinates of the position selected in the step S1 by adopting a handheld GPS high-precision data acquisition device. It should be noted that the recorded GPS coordinates and the pixel positions of the paper sheet should correspond one-to-one, so as to correspond to the pixel point coordinates in the following;
and S3, after marking each position by using a marker paper sheet with bright color, acquiring a detection picture of a frame of camera, wherein each marker position can be clearly seen in the frame of picture as shown in FIG. 2. Acquiring pixel point coordinates of all marked positions in the picture, wherein the pixel point coordinates correspond to the GPS coordinates acquired in the step S2 one by one;
s4, solving homography transformation of a sensor coordinate system and a GPS coordinate system by a least square method by adopting a homography transformation principle of the camera coordinate system and the GPS coordinate system, and projecting the camera coordinate to a WGS84 coordinate system;
and S5, because the distortion and perspective phenomena exist in the picture detected by the camera, the conversion error is larger at some positions by using a homography transformation. Therefore, a partition calibration method can be considered. And repeating the steps S1-S4, acquiring a series of GPS coordinates and pixel point coordinates below and above the detected picture of the camera, and calculating a conversion matrix of the GPS coordinates and the pixel point coordinates. If the road is approximately parallel to the y-axis direction in the pixel coordinate system, the road can be partitioned according to the pixel coordinate values in the y-axis direction, and corresponding mapping transformation matrixes are calculated in different areas through the calibration method;
aiming at the road side camera, a running video of a test vehicle with RTK in a field area of the road side camera is recorded, a corresponding GPS coordinate-pixel point coordinate pair is obtained based on a target detection algorithm, and then training is carried out by utilizing decision tree regression, random forest regression algorithm or neural network deep learning, so that a conversion model from the pixel point coordinate to the GPS coordinate is obtained.
In the intelligent network connection road side subsystem, firstly, the video image of a road side camera is processed by a road side MEC, for example, yolov5 and Deepsort extract characteristic information related to the state of a target object, wherein the characteristic information not only comprises the category but also comprises position information, then JSON is used for packaging into RSM information, and the RSU is pushed to a vehicle OBU through a PC5 air interface.
The intelligent network connection vehicle-mounted subsystem is characterized in that a vehicle-mounted industrial personal computer is used for collecting RTK data, characteristic extraction is carried out on a video image of a vehicle-mounted camera, and similarly, the state characteristic of a target object comprises position information. And simultaneously, the vehicle-mounted OBU analyzes the received RSM information, and then the vehicle-mounted industrial personal computer performs fusion processing on the information acquired by the vehicle and the information transmitted from the RSU. Data association can be carried out on vehicle-mounted information and road-side information by adopting Hungary matching based on the Mahalanobis distance, and a target is updated by utilizing Kalman filtering to obtain an updated own vehicle state and a surrounding target object state. The target matching and filtering methods herein are not limited to the Hungarian matching and Kalman filtering mentioned herein.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A vehicle-road cooperation realization method based on cameras is characterized in that aiming at a plurality of vehicle-mounted cameras in an intelligent network connection road-side subsystem and a plurality of road-side cameras in the intelligent network connection vehicle-mounted subsystem, a unified coordinate system is constructed, the vehicle-mounted cameras and the road-side cameras are effectively fused, and vehicle-road sensing cooperation based on the cameras is realized, and the method comprises the following steps:
s1: the vehicle-mounted camera and the roadside camera realize time synchronization through GNSS time service;
s2: the intelligent network connection side subsystem realizes the spatial alignment of each roadside camera through the conversion from pixel coordinates to WGS84 coordinates;
s3: the intelligent network on-board subsystem realizes the spatial alignment of each on-board camera through the conversion from pixel coordinates to WGS84 coordinates;
s4: the intelligent network connection side subsystem pushes a detection result to the intelligent network connection vehicle-mounted subsystem through V2X;
s5: the intelligent network connection vehicle-mounted subsystem and the intelligent network connection side subsystem realize information fusion through a unified WGS84 coordinate system, and therefore vehicle-road sensing cooperation based on a camera is achieved.
2. The method for realizing vehicle-road cooperation based on the camera of claim 1, wherein in the step S2, the conversion from the roadside camera pixel point coordinates to the WGS84 coordinates is realized by establishing a conversion model, and the conversion model from the roadside camera pixel point coordinates to the WGS84 coordinates comprises the following steps:
s21: installing an RTK on a test vehicle to obtain real-time WGS84 coordinates of the test vehicle, simultaneously recording a running video of the test vehicle in a field of view area of a roadside camera, and identifying the position of the test vehicle in real time through a target detection algorithm, so as to obtain pixel point coordinates of the test vehicle, and simultaneously obtaining more corresponding pixel point coordinate pairs after time synchronization;
s22: and dividing the acquired data into a training set and a test set, and training by using a decision tree regression algorithm, a random forest regression algorithm or a neural network deep learning algorithm to finally obtain a conversion model from the pixel point coordinates of the roadside camera to the WGS84 coordinates.
3. The method for realizing vehicle-road cooperation based on the camera according to claim 1, wherein in the step S3, the vehicle-mounted camera directly realizes the conversion from the pixel coordinate to the WGS84 coordinate by establishing a homography transformation matrix, and the specific conversion process includes the following steps:
s31: a camera is arranged on the vehicle body, and a plurality of paper sheets with bright colors are placed in the visual field range of the camera to be used as marks;
s32: acquiring marked GPS coordinates by using a high-precision GPS handset;
s33: acquiring any one frame of detection picture of a camera, and acquiring pixel point coordinates of a marking paper in a picture;
s34: utilizing corresponding pixel point coordinate pairs, solving homography transformation of a sensor coordinate system and a GPS coordinate system through a least square method, projecting camera coordinates to GPS coordinates, defining the coordinates of a P point under the pixel coordinate system Our and the GPS coordinate system OXY to be (u, v) and (x, y) respectively when the sensor and the GPS simultaneously observe the P point, and then expressing the conversion relation between Our and OXY as a formula I, wherein the formula I is as follows:
projecting the camera coordinates of the detection target to a GPS coordinate system through matrix transformation, and giving N groups of space corresponding sets (u, v) and (X) i ,Y i ) (i =1,2,. Ann.., n), determining a formula two to a formula five according to formula one, wherein formula two is expressed as:
the third public representation is:
the formula four is expressed as:
the formula five is expressed as:
defining a formula six and a formula seven, wherein the formula six is expressed as:
the formula seven is expressed as:
the least squares solution of the transformation matrix N yields equation eight, which is expressed as:
mapping of pixel coordinates of the camera to a WGS84 coordinate system is achieved through the solved transformation matrix.
4. The camera-based vehicle-road cooperative implementation method according to claim 1, wherein in S4, the V2X includes a V2XPC5 air interface and a V2XUu air interface, and the intelligent network-link-side subsystem pushes a detection result to the intelligent network-link-vehicle-mounted subsystem through the V2XPC5 air interface or the V2XUu air interface.
5. The method for realizing vehicle-road cooperation based on camera according to claim 1, wherein in step S5, the vehicle information and the obstacle information around the vehicle are respectively fused with the information sensed by the intelligent network link-side subsystem, the vehicle information is position information provided by the vehicle-mounted RTK, and the obstacle information around the vehicle is sensing target information provided by the vehicle-mounted camera.
6. The camera-based vehicle-road cooperative implementation method is characterized in that information fusion is sequentially performed through a target matching and filtering algorithm, wherein the target matching adopts one or more combinations of global nearest neighbor, joint probability data association and multi-hypothesis tracking methods; the filtering algorithm adopts one or more of Kalman filtering, extended Kalman filtering, lossless Kalman filtering or particle filtering algorithm.
7. A camera-based vehicle-road cooperation realization system is used for realizing the camera-based vehicle-road cooperation realization method as claimed in any one of claims 1 to 6, and is characterized by comprising an intelligent network-connected vehicle-mounted subsystem arranged on a vehicle and an intelligent network-connected road-side subsystem arranged on a road test, wherein the intelligent network-connected vehicle-mounted subsystem comprises an RTK, a vehicle-mounted camera, a vehicle-mounted computing device and a V2X device which are integrated into a whole through an exchanger; the intelligent network connection road side subsystem comprises a road side camera, road side computing equipment and V2X equipment which are integrated through an exchanger.
8. The camera-based vehicle-road cooperative implementation method according to claim 7, wherein the RTK is a Beidou or GPS differential system, the vehicle-mounted camera comprises a forward-looking camera, a backward-looking camera and a look-around camera, the vehicle-mounted computing device comprises an embedded controller and an industrial personal computer, and the V2X device comprises a 5G/4GCPE and a V2XOBU.
9. The camera-based vehicle-road cooperative implementation method according to claim 7, wherein the roadside camera includes an industrial camera and a network camera, the roadside computing device includes an MEC server, an industrial personal computer and a computer, and the V2X device includes a 5G/4GCPE and a V2XRSU.
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