Network-connected vehicle-road cooperation test system and test method thereof

Abstract

The invention discloses a network-connected vehicle-road cooperation test system and a test method thereof, and the method comprises the steps: carrying out the road end positioning of a reduced-scale road scene and a reduced-scale intelligent vehicle through a high-precision indoor positioning system, and obtaining the road end sensing data; regional centralized scheduling control calculation is carried out through the cooperation server according to road end sensing data and vehicle state information reported by the reduced-scale intelligent vehicles, a multi-vehicle cooperation control instruction is obtained, and human control analog signals, automatic driving setting information or the multi-vehicle cooperation control instruction are sent to the reduced-scale intelligent vehicles through the intelligent vehicle control unit. The intelligent vehicle is simulated to run in the reduced-scale road scene through the reduced-scale intelligent vehicle, and simulation data reproduction is performed on the reduced-scale road scene and the reduced-scale intelligent vehicle through the digital twin unit according to the scene data of the reduced-scale road scene, the road end sensing data and the vehicle state information. According to the invention, the efficiency and reliability of the network-connected vehicle-road collaborative simulation test are improved, and the method can be widely applied to the technical field of intelligent traffic.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation, and particularly to an Internet-connected vehicle-road collaborative test system and a test method thereof. Background Art

[0002] Road traffic plays an important role in the modern transportation system due to its high flexibility and wide applicability. As the main participants in road traffic, vehicles have achieved a large number of research results in intelligentization and have been widely applied. Currently, with the increasing maturity of communication, control, and artificial intelligence technologies, Internet-connected vehicle-road collaborative technology has become a driving force for further improving traffic efficiency and safety.

[0003] However, compared with single-vehicle intelligent research, Internet-connected vehicle-road collaborative research involves more objects and a wider range, so there are still bottlenecks in test implementation. On the one hand, numerical simulation methods are widely used today. However, the confidence of this method in vehicle motion modeling, simulating communication processes, simulating device computing power constraints, etc. is doubtful, and there is still a huge gap from technology implementation. On the other hand, in some pilot areas, many units have carried out in-vehicle tests in closed scenarios, but this test method has limitations such as high operation difficulty, high cost, low safety, and poor flexibility.

[0004] Based on the above background, relevant research has put forward higher requirements for the test methods of Internet-connected vehicle-road collaborative technology. There is an urgent need for a convenient, efficient, and safe test technology. The scaled experimental platform is a product of the new generation of electronic and electrical technologies. By constructing an Internet-connected vehicle-road collaborative system with this framework and connecting physical objects in aspects such as vehicle motion, communication, perception, and computing, it can effectively improve the test credibility, and at the same time ensure easy operation, low cost, safety, and strong flexibility of the test. Internet-connected vehicle-road collaborative research and testing should include logical aspects such as autonomous reporting of vehicle status, autonomous calculation of driving status by vehicles, autonomous decision-making of single vehicles, edge offloading of computing power, and server fusion of perception information.

[0005] However, existing related solutions are difficult to adapt to the perception, communication, and computing logics in the real vehicle-road collaboration, and it is difficult to support high-credibility Internet-connected vehicle-road collaborative tests. Summary of the Invention

[0006] In order to solve the above technical problems, the purpose of the present invention is to provide an Internet-connected vehicle-road collaborative test system and a test method thereof, which improve the efficiency and reliability of Internet-connected vehicle-road collaborative simulation tests.

[0007] The first technical solution adopted by the present invention is:

[0008] An Internet-connected vehicle-road collaborative test system includes a scaled road scenario, a high-precision indoor positioning system, multiple scaled intelligent vehicles, an intelligent vehicle control unit, a collaborative server, a digital twin unit, and a communication unit, wherein:

[0009] The scaled - down road scenario is used to simulate urban road scenarios and highway scenarios according to a preset ratio;

[0010] The high - precision indoor positioning system is used to simulate the road - side positioning process in vehicle - road collaboration and simulate that GPS provides corresponding geodetic coordinate positioning data for the scaled - down intelligent vehicle;

[0011] The scaled - down intelligent vehicle is used to simulate the driving of intelligent vehicles in the scaled - down road scenario;

[0012] The intelligent vehicle control unit is used to control the driving of the scaled - down intelligent vehicle;

[0013] The collaborative server is used to simulate the section dispatching equipment and communication base stations in a real - traffic scenario to achieve information collection, information forwarding, and collaborative control calculation;

[0014] The digital twin unit is used to reproduce simulation data for the scaled - down road scenario and the scaled - down intelligent vehicle;

[0015] The communication unit is used to simulate the real vehicle - to - everything (V2X) communication between the high - precision indoor positioning system, the scaled - down intelligent vehicle, the intelligent vehicle control unit, the collaborative server, and the digital twin unit.

[0016] Furthermore, a scaled - down road network, obstacles, traffic lights, and road traffic signs are set in the scaled - down road scenario.

[0017] Furthermore, the high - precision indoor positioning system includes a motion - capture camera and a motion - capture server. The motion - capture camera is used to capture the pose data of obstacles, traffic lights, road traffic signs, and the scaled - down intelligent vehicle in the scaled - down road scenario. The motion - capture server is used to perform road - side positioning on the obstacles, traffic lights, road traffic signs, and the scaled - down intelligent vehicle based on the pose data to obtain road - side perception data.

[0018] Furthermore, the scaled - down intelligent vehicle is internally equipped with a battery energy mechanism, a power mechanism, a vehicle - end computing power module, a global positioning and perception module, and vehicle - end perception devices, where:

[0019] The battery energy mechanism is used to supply energy to the power mechanism, the vehicle - end computing power module, the global positioning and perception module, and the vehicle - end perception devices through a direct - current output device;

[0020] The power mechanism includes a motor and a servo. The power mechanism is used to control the motor to drive the scaled - down intelligent vehicle to move forward and backward and / or control the servo to drive the scaled - down intelligent vehicle to turn left and right according to the drive control signal output by the vehicle - end computing power module;

[0021] The vehicle-end computing power module includes a main board, a high-computing-power core board, and an embedded system microcontroller mounted on the main board;

[0022] The global positioning and perception module is used to simulate the earth coordinate positioning of the vehicle and the perception of the vehicle by the roadside unit in vehicle-road collaborative research;

[0023] The vehicle-end perception device is used to simulate the perception of the vehicle's surrounding environment.

[0024] Furthermore, the vehicle-end perception device includes a monocular camera, a binocular camera, a lidar, an inertial sensor, an odometer, and a steering angle feedback device.

[0025] Furthermore, the intelligent vehicle control unit includes a remote control / driving simulation device and an autonomous driving control device. The remote control / driving simulation device is used to simulate the human driving control of the scaled intelligent vehicle, and the autonomous driving control device is used to simulate the autonomous driving control and regional centralized scheduling control of the scaled intelligent vehicle, where:

[0026] When simulating human driving control, the scaled intelligent vehicle receives the human control simulation signal sent by the remote control / driving simulation device, and based on the human control simulation signal, simulates the physical steering wheel state and throttle / brake state of the real vehicle to obtain a first reference control quantity, and then drives the power mechanism to perform corresponding actions through the first reference control quantity;

[0027] When simulating autonomous driving control, the scaled intelligent vehicle receives the autonomous driving setting information sent by the autonomous driving control device. The vehicle-end computing power module performs autonomous driving intelligent decision-making calculations, intelligent path planning calculations, and reference control quantity calculations based on the autonomous driving setting information, the global positioning and perception data of the scaled intelligent vehicle, and the vehicle-end perception data, and drives the power mechanism to perform corresponding actions according to the obtained second reference control quantity;

[0028] When simulating regional centralized scheduling control, the scaled intelligent vehicle receives the multi-vehicle collaborative control instruction sent by the autonomous driving control device, generates a third reference control quantity according to the multi-vehicle collaborative control instruction, and then drives the power mechanism to perform corresponding actions according to the third reference control quantity.

[0029] Furthermore, when simulating the roadside scheduling device, the collaborative server is used to collect the roadside perception data of the high-precision indoor positioning system and the vehicle state information of each scaled intelligent vehicle, perform regional centralized scheduling control calculations based on the roadside perception data and the vehicle state information, obtain the multi-vehicle collaborative control instructions for each scaled intelligent vehicle, and send the multi-vehicle collaborative control instructions to the corresponding scaled intelligent vehicle;

[0030] When simulating a communication base station, the collaborative server is used to forward the road-end perception data and the multi-vehicle collaborative control instructions to the scaled intelligent vehicle, and is also used to forward the road-end perception data and the vehicle status information to the digital twin unit.

[0031] Furthermore, the digital twin unit is used to construct a digital twin simulation model based on the scene data of the scaled road scene, the road-end perception data of the high-precision indoor positioning system, and the vehicle status information of each scaled intelligent vehicle, and display the digital twin simulation model.

[0032] Furthermore, the communication unit includes a wireless communication network and a wired communication network. The scaled intelligent vehicle, the intelligent vehicle control unit, and the collaborative server are connected through the wireless communication network, and the high-precision indoor positioning system, the intelligent vehicle control unit, the collaborative server, and the digital twin unit are all connected to the wired communication network.

[0033] The second technical solution adopted by the present invention is:

[0034] A test method for a networked vehicle-road collaborative test system, which is used to be executed by the above-mentioned networked vehicle-road collaborative test system, includes the following steps:

[0035] Perform road-end positioning on the scaled road scene and the scaled intelligent vehicle through the high-precision indoor positioning system to obtain road-end perception data;

[0036] Through the collaborative server, perform regional centralized scheduling control calculation according to the road-end perception data and the vehicle status information reported by each scaled intelligent vehicle to obtain multi-vehicle collaborative control instructions;

[0037] Send a human control simulation signal, an autonomous driving setting information, or the multi-vehicle collaborative control instructions to the scaled intelligent vehicle through the intelligent vehicle control unit;

[0038] Through the scaled intelligent vehicle, simulate the driving of an intelligent vehicle in the scaled road scene according to the human control simulation signal, the autonomous driving setting information, or the multi-vehicle collaborative control instructions;

[0039] Through the digital twin unit, reproduce the simulation data of the scaled road scene and the scaled intelligent vehicle according to the scene data of the scaled road scene, the road-end perception data, and the vehicle status information.

[0040] The beneficial effects of the present invention are as follows: The present invention provides a networked vehicle-road collaborative test system and its test method. The networked vehicle-road collaborative test system includes a scaled road scene, a high-precision indoor positioning system, multiple scaled intelligent vehicles, an intelligent vehicle control unit, a collaborative server, a digital twin unit, and a communication unit. The high-precision indoor positioning system is used to perform road-end positioning on the scaled road scene and the scaled intelligent vehicles to obtain road-end perception data. The collaborative server performs regional centralized scheduling control calculations based on the road-end perception data and the vehicle status information reported by each scaled intelligent vehicle to obtain multi-vehicle collaborative control instructions. The intelligent vehicle control unit sends a human control simulation signal, an autonomous driving setting information, or a multi-vehicle collaborative control instruction to the scaled intelligent vehicles. The scaled intelligent vehicles simulate the driving of intelligent vehicles in the scaled road scene according to the human control simulation signal, the autonomous driving setting information, or the multi-vehicle collaborative control instruction. The digital twin unit reproduces the simulation data of the scaled road scene and the scaled intelligent vehicles based on the scene data, the road-end perception data, and the vehicle status information of the scaled road scene. The present invention restores the functions of road-end perception, vehicle-end perception, collaborative control, single-vehicle intelligence, vehicle networking communication, etc. in the real vehicle-road collaborative scenario. The information flow direction conforms to the actual logic, realizing real-time simulation, real-time digital twin, and semi-physical display. The experimental effect display is strong and the data is reproducible, ensuring that the supported networked vehicle-road collaborative test has strong operability, high safety, and controllable cost, guaranteeing the credibility of the networked vehicle-road collaborative simulation test, and thus improving the efficiency and reliability of the networked vehicle-road collaborative simulation test. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a block diagram of a networked vehicle-road collaborative test system provided by an embodiment of the present invention;

[0042] Figure 2 It is a schematic diagram of the wireless serial port message format provided by an embodiment of the present invention;

[0043] Figure 3 It is a schematic diagram of the control interface of the intelligent vehicle control unit provided by an embodiment of the present invention;

[0044] Figure 4 It is a schematic diagram of the digital twin interface of the digital twin unit provided by an embodiment of the present invention;

[0045] Figure 5 It is a top view of the scaled road scene provided by an embodiment of the present invention;

[0046] Figure 6 It is a schematic diagram of the positioning signal flow direction provided by an embodiment of the present invention;

[0047] Figure 7 It is a schematic diagram of the control information flow direction provided by an embodiment of the present invention;

[0048] Figure 8 The flowchart of the steps of a test method for an Internet-connected vehicle-road collaborative test system provided by an embodiment of the present invention. Detailed implementation manners

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. For the step numbers in the following embodiments, they are only set for the convenience of explanation and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0050] In the description of the present invention, the meaning of "a plurality" is more than two. If the first and second are described, it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this invention belongs. The terms used in the specification of this application are only for describing specific embodiments and are not intended to limit the present invention.

[0051] Referring to Figure 1 , the present invention provides an Internet-connected vehicle-road collaborative test system, including a scaled road scene, a high-precision indoor positioning system, a plurality of scaled intelligent vehicles, an intelligent vehicle control unit, a collaborative server, a digital twin unit, and a communication unit, wherein:

[0052] The scaled road scene is used to simulate urban road scenes and highway scenes according to a preset ratio;

[0053] The high-precision indoor positioning system is used to simulate the road-end positioning process in vehicle-road collaboration and simulate GPS to provide corresponding geodetic coordinate positioning data for the scaled intelligent vehicles;

[0054] The scaled intelligent vehicles are used to simulate the driving of intelligent vehicles in the scaled road scene;

[0055] The intelligent vehicle control unit is used to control the driving of the scaled intelligent vehicles;

[0056] The collaborative server is used to simulate the section dispatching equipment and communication base stations in a real traffic scene to achieve information collection, information forwarding, and collaborative control calculation;

[0057] The digital twin unit is used to reproduce the simulation data of the scaled road scene and the scaled intelligent vehicles;

[0058] The communication unit is used to simulate the real vehicle networking communication between the high-precision indoor positioning system, the scaled intelligent vehicles, the intelligent vehicle control unit, the collaborative server, and the digital twin unit.

[0059] As a further optional implementation, a scaled road scenario is provided with a scaled road network, obstacles, traffic lights, and road traffic signs.

[0060] Specifically, the scaled road scenario includes an urban road scenario and an expressway scenario. Based on the real road dimensions and corner standards, unified scale mapping is carried out, and traffic indication signals and traffic rule signs can be added. The above scenario map is fixed on the indoor ground, and the display forms include, but are not limited to, one or more of floor painting, customized floor, and programmable screen. The indoor display and installation methods of the scenario need to consider factors such as material deformation, surface friction coefficient, and color contrast between the passable area and the obstacle area.

[0061] As a further optional implementation, the high-precision indoor positioning system includes motion capture cameras and a motion capture server. The motion capture cameras are used to capture the pose data of obstacles, traffic lights, road traffic signs, and scaled intelligent vehicles in the scaled road scenario, and the motion capture server is used to perform road-end positioning on the obstacles, traffic lights, road traffic signs, and scaled intelligent vehicles based on the pose data to obtain road-end perception data.

[0062] Specifically, the high-precision indoor positioning system is used to simulate the road-end positioning method in vehicle-road collaboration, and at the same time, it can simulate GPS to provide the vehicle with its own geodetic coordinate positioning. The above road-end positioning is global and can provide a correct benchmark for the fusion effect of multi-vehicle perception data. The vehicle-end positioning accuracy should be higher than the GPS accuracy in the actual application scenario after scaling, with a margin to ensure that the real scenario can be simulated by reducing performance. The high-precision indoor positioning technology includes, but is not limited to, optical motion capture technology, computer vision positioning technology, ultra-wideband (UWB) positioning technology, and RFID-based positioning technology, etc.

[0063] As a further optional implementation, the shown scaled intelligent vehicle is built-in with a battery energy mechanism, a power mechanism, a vehicle-end computing power module, a global positioning and perception module, and vehicle-end perception devices, where:

[0064] The battery energy mechanism is used to supply energy to the power mechanism, the vehicle-end computing power module, the global positioning and perception module, and the vehicle-end perception devices through a direct current output device;

[0065] The power mechanism includes a motor and a steering gear. The power mechanism is used to control the motor to drive the scaled intelligent vehicle to move forward and backward and / or control the steering gear to drive the scaled intelligent vehicle to turn left and right according to the drive control signal output by the vehicle-end computing power module;

[0066] The vehicle-end computing power module includes a main board and a high-computing-power core board and an embedded system microcontroller mounted on the main board;

[0067] The global positioning perception module is used to simulate the geodetic coordinate positioning of vehicles and the perception of vehicles by roadside units in vehicle-road collaborative research;

[0068] The vehicle-end sensing device is used to simulate the perception of the vehicle's surrounding environment.

[0069] Specifically, the scaled intelligent vehicle should have driving capabilities, with built-in power mechanisms (such as motors, servos, etc.), battery energy mechanisms, transmission mechanisms, and shock-absorbing mechanisms, etc.; in addition, it should include vehicle-end computing power, which completes tasks such as receiving and sending information, control calculations, and positioning fusion calculations, and then sends the executable control amount to the power mechanism for execution.

[0070] The driving control of the scaled intelligent vehicle should conform to the logic of real driving scenarios, involving two existing types: human driving and in-vehicle assisted driving systems; human driving is simulated by a driving simulation device or remote control, and its signal is received by the on-vehicle receiver, directly controlling the power mechanism, with a higher priority than the control signal of the above-mentioned computing power device. In the real world, human takeover of driving also takes precedence over the logic of the assisted driving system; the computing power device simulates the in-vehicle autonomous driving computing power device in the real world, and its program startup, stop, constant speed cruise acceleration and deceleration, and mode switching and other functions are controlled by a single vehicle control computer deployed outside the site. At the same time, in this invention, the control computer can also be used as an edge offloading device for vehicle-end computing power.

[0071] Further as an optional implementation, the vehicle-end sensing device includes a monocular camera, a binocular camera, a lidar, an inertial sensor, an odometer, and a steering angle feedback device.

[0072] Specifically, the scaled intelligent vehicle should have vehicle-end information perception capabilities, involving sensing devices including but not limited to single and binocular cameras, lidar, odometers, inertial navigation, etc. The vehicle-end perception function includes the "geodetic coordinates" from automatic capture and the data of its own sensing devices, and multi-modal fusion is carried out using in-vehicle or edge computing power.

[0073] Further as an optional implementation, the intelligent vehicle control unit includes a remote control / driving simulation device and an autonomous driving control device. The remote control / driving simulation device is used to simulate the human driving control of the scaled intelligent vehicle, and the autonomous driving control device is used to simulate the autonomous driving control and regional centralized scheduling control of the scaled intelligent vehicle, where:

[0074] When simulating human driving control, the scaled intelligent vehicle receives the human control simulation signal sent by the remote control / driving simulation device, and based on the human control simulation signal, simulates the physical steering wheel state and throttle brake state of the real vehicle to obtain a first reference control amount, and then drives the power mechanism to perform corresponding actions through the first reference control amount;

[0075] When simulating autonomous driving control, the scaled intelligent vehicle receives the autonomous driving setting information sent by the autonomous driving control device. The vehicle-side computing power module performs autonomous driving intelligent decision-making calculations, intelligent path planning calculations, and reference control quantity calculations based on the autonomous driving setting information, the global positioning perception data of the scaled intelligent vehicle, and the vehicle-side perception data, and drives the power mechanism to perform corresponding actions according to the obtained second reference control quantity;

[0076] When simulating area centralized scheduling control, the scaled intelligent vehicle receives the multi-vehicle collaborative control instructions sent by the autonomous driving control device, generates a third reference control quantity according to the multi-vehicle collaborative control instructions, and then drives the power mechanism to perform corresponding actions according to the third reference control quantity.

[0077] Further, as an optional implementation manner, when simulating the roadside scheduling device, the collaborative server is used to collect the roadside perception data of the high-precision indoor positioning system and the vehicle state information of each scaled intelligent vehicle, and perform area centralized scheduling control calculations based on the roadside perception data and the vehicle state information to obtain the multi-vehicle collaborative control instructions for each scaled intelligent vehicle, and send the multi-vehicle collaborative control instructions to the corresponding scaled intelligent vehicle;

[0078] When simulating the communication base station, the collaborative server is used to forward the roadside perception data and the multi-vehicle collaborative control instructions to the scaled intelligent vehicle, and is also used to forward the roadside perception data and the vehicle state information to the digital twin unit.

[0079] Specifically, the collaborative server is deployed outside the road scene, has high computing power, and undertakes tasks such as information collection, information forwarding, and collaborative control calculation, simulating the roadside scheduling device and the communication base station in the real traffic scene;

[0080] Furthermore, for simulating the road section scheduling device, the collaborative server collects the vehicle state, which can be obtained through two ways: roadside perception and vehicle reporting. The centralized control calculation is completed inside the server, and the control instructions for each vehicle are sent to the corresponding vehicle for execution; for simulating the communication base station, the collaborative server completes information forwarding. One is to forward the high-precision indoor positioning system data to the intelligent vehicle and the digital twin unit, the second is to forward the control device instructions, which are packaged and broadcast to each vehicle together with the positioning data, and the third is to forward the collected vehicle states of each vehicle to the digital twin unit.

[0081] Further, as an optional implementation manner, the digital twin unit is used to construct a digital twin simulation model based on the scene data of the scaled road scene, the roadside perception data of the high-precision indoor positioning system, and the vehicle state information of each scaled intelligent vehicle, and display the digital twin simulation model.

[0082] Specifically, the digital twin unit is developed by a virtual engine, and the required data is transmitted by the collaborative server via a wired network, enabling functions such as virtual perspective, data storage, and data reproduction.

[0083] Further as an optional implementation, the communication unit includes a wireless communication network and a wired communication network. The scaled smart vehicle, the smart vehicle control unit, and the collaborative server are connected through the wireless communication network, and the high-precision indoor positioning system, the smart vehicle control unit, the collaborative server, and the digital twin unit are all connected to the wired communication network.

[0084] Specifically, the communication unit simulates real vehicle network communication. The scaled smart vehicle is connected to the control unit and the collaborative server through a wireless communication module, and the collaborative server, the control unit, and the digital twin unit are connected through a wired network. The communication unit undertakes functions such as issuing scheduling instructions, reporting vehicle-end perception information, issuing high-precision positioning information, issuing single-vehicle control instructions, and transmitting data required by the digital twin unit.

[0085] In some optional embodiments, the wireless communication technologies adopted include but are not limited to wireless local area network communication technology (Wi-Fi), wireless serial port communication technology, LoRa, Zigbee, etc. The wireless module uses one or a combination of them to complete wireless communication. During the process of issuing positioning information and control instructions, the performance of the wireless module adopted in terms of relevant indicators such as information delay and packet loss rate should be better than that of the real vehicle network, leaving a margin to ensure that the real scenario can be simulated by reducing performance.

[0086] The following further illustrates the vehicle-road collaborative test system of the present invention in conjunction with a specific embodiment.

[0087] The embodiments of the present invention include 24 motion capture cameras, 1 motion capture server, 20 (or more) smart vehicles, and each vehicle is equipped with a remote control, a digital twin display device, more than 1 smart vehicle control unit, 1 local area network router, and 1 collaborative server. In the embodiments of the present invention, the above devices are mainly connected through a wired network, a wireless network, and a wireless serial port.

[0088] The motion capture cameras and the motion capture server constitute the above-mentioned high-precision indoor positioning system. Motion capture positioning is a high-precision infrared optical positioning technology that captures the position information of the reflective marker points fixed on the surface of an object (such as a smart vehicle, a traffic signal, or an obstacle) from different angles through multiple optical lenses arranged in the venue, and captures its position, movement, and posture.

[0089] In the embodiments of the present invention, the NOKOV measurement technology solution is adopted. 24 motion capture cameras are fixedly installed on the surrounding walls of a 625-square-meter square indoor laboratory at a height of 5 meters, and the installation distribution is as uniform as possible. The motion capture cameras transmit perception information through a wired network and input it into the motion capture server through a switch.

[0090] In the embodiment of the present invention, the motion capture server is equipped with the Windows 11 operating system and configured with the special motion capture information processing software XINGYING. The software can complete operations such as site calibration, information unification, coordinate conversion, and sending. The motion capture server accesses the laboratory local area network through a wired network, and after packing the positioning information of all target rigid bodies, it sends it out through vrpn.

[0091] The collaboration server accesses the laboratory local area network through a wired network and receives the positioning information. In the embodiment of the present invention, the collaboration server installs the Ubuntu system. When parsing the motion capture vrpn data and completing the centralized collaborative control calculation, it adopts the ROS framework, while the information sending and forwarding service is developed using the GO language. The positioning information and automatic control instructions are packed in the collaboration server, broadcast using a wireless serial device, and then received by each intelligent vehicle.

[0092] As Figure 2 shown is a schematic diagram of the wireless serial port message format provided by the embodiment of the present invention. The message consists of 5 parts: a frame header, a data length, a data area, a check bit, and a frame tail. The occupied field lengths are 2 bytes, 2 bytes, (8 × number of units) bytes, 1 byte, and 1 byte respectively. The start frame is marked as 0×7E7E, the check bit is verified using the CRC-16 / XMODEM method, and the frame tail is marked as 0×7E7D.

[0093] The position size of the data area is determined by the number of test units (intelligent vehicles, signal lights, obstacles, etc.). The reserved length for a single unit is 8 bytes and is divided into five positions. Position 1 occupies 1 byte and represents the corresponding unit number, such as "Car: 1"; Position 2 occupies 2 bytes and represents the X coordinate of the unit; Position 3 occupies 2 bytes and represents the Y coordinate of the unit; Position 4 occupies 2 bytes and represents the yaw of the unit's orientation; Position 5 occupies 1 byte and represents the control information of the unit. Among them, BIT 0 is the start / stop control amount, BIT 1-4 are the speed control amounts, and the control form is in the form of acceleration. BIT 5-7 are the mode control amounts, such as the line-tracking mode, collaborative mode, following mode, etc.

[0094] In the embodiment of the present invention, the scaled-down intelligent vehicle uses an RK3399 six-core 64-bit (A72x2 + A53x4) processor as the computing power unit, with a main frequency of 1.8 GHz and supporting multiple network interfaces. The intelligent vehicle control system is built using the ROS (Robot Operating System) framework. Existing programs use C++ as the programming language, and ROS also supports Python compatibility.

[0095] The specific functions implemented by ROS are as follows: receiving the information sent by the wireless serial port (positioning + control), performing operation tasks such as information decoding, positioning fusion, and automatic control algorithm calculation internally, and then transmitting the executable reference control amount to the chassis downward. At the same time, the vehicle status is reported upward through UDP.

[0096] The intelligent vehicle control unit is a Windows system computer, which accesses the local area network through a wired network. On the one hand, it receives information from the collaborative server, including vehicle-reported information and global positioning information of the motion capture system; on the other hand, it sends the system control information on the vehicle-mounted computing unit to the collaborative server, and the collaborative server sends it to each intelligent vehicle.

[0097] As Figure 3 Shown is the schematic diagram of the control interface of the intelligent vehicle control unit provided by the embodiment of the present invention. The left side of the interface is the vehicle selection area. The gray vehicle logo means that the vehicle is not online or the account does not have the control permission for the corresponding vehicle. If the vehicle logo is bright white, it means that the vehicle is online and the account has the control permission. The bright blue logo shown as Car74 means that the vehicle is selected as the current control object. The digital square below the vehicle logo represents the voltage status reported by the vehicle. If the filling color of the square changes from green to yellow (voltage less than 7V), attention should be paid to the battery power. The middle of the interface is the sand table scene display area, where all vehicle positions, orientations, and relative positions to obstacles in the sand table can be viewed in real time, and vehicle control can be assisted. The right side of the interface is the control area. The current control commands include: start, pause, mode selection (tracking / following), speed distribution, etc. At the same time, the real-time vehicle parameters reported by the intelligent vehicle are also displayed below.

[0098] As Figure 4 Shown is the schematic diagram of the digital twin interface of the digital twin unit provided by the embodiment of the present invention. This program is developed by the Unity 3D game engine. The device accesses the local area network through a wired network and receives information from the collaborative server, including vehicle-reported information and global positioning information of the motion capture system. The 3D digital twin interface can display the sand table scene in real time and can freely adjust the camera position and perspective, and can simulate the driver's perspective of a certain vehicle to a certain extent.

[0099] The local area network router adopts an enterprise-level AX5400 dual-band Wi-Fi6 wireless VPN router, model TL-XVR5400L EasyMesh Edition. In the test environment of the embodiment of the present invention, when 10 vehicles are running simultaneously, the delay of uploading information through Wifi is controlled below 30ms, and the average delay is 3.54ms. The packet loss rate is controlled below 1‰, meeting the control requirements of the real scenario.

[0100] Method for wireless serial port broadcast information. Under the test environment of the embodiments of the present invention, it is measured that the delay is positively correlated with the message volume. In the case of the message volume when 20 vehicles are running, it is measured that the delay of the sent information is controlled within 70 ms, and the fluctuation range is extremely small, which can meet the control requirements of the real scenario.

[0101] As Figure 5 Shown is a top view of the scaled - down road scene provided by the embodiments of the present invention. The scaled - down road floor is composed of 256 square plates of 1m×1m. The total length and width are both 16m, and the total area is 256m2. The coordinate origin is located at the upper right corner, the positive direction of the x - axis is horizontal to the left, and the positive direction of the y - axis is vertical downward. The scaled - down road floor includes the simulation of urban roads and expressways. Among them, the urban road is designed as a two - lane bidirectional road, including scenarios such as intersections, T - intersections, and roundabouts; the expressway is a six - lane bidirectional road.

[0102] As Figure 6 Shown is the flow chart of the positioning signal provided by the embodiments of the present invention. The global positioning information is generated by the motion capture positioning processing unit. The motion capture camera captures the positions of the reflective marker balls in the field, and this information is generated corresponding to the corresponding rigid bodies in the motion capture server. Then the information is transmitted to the collaborative server through vrpn via a wired network, and the vrpn is received and parsed within the collaborative server. On the one hand, the parsed information is transmitted to the digital twin server through the wired network for display; on the other hand, it remains inside the server, which can be supplied to the collaborative algorithm for use or directly packaged and broadcast information through the wireless serial port. Each intelligent vehicle receives this information through the wireless serial port and gives it to the motion control algorithm for use after parsing.

[0103] As Figure 7 Shown is the flow chart of the control information provided by the embodiments of the present invention. The vehicle control information is generated through three channels. Firstly, the intelligent vehicle control unit issues control instructions, which are given to the collaborative server via the wired network, and are packaged together with the position information and sent to the corresponding vehicle for execution through the wireless serial port; secondly, the collaborative control algorithm receives the state information of all vehicles, generates the control quantities of each vehicle, and sends them to the corresponding vehicle for execution through the wireless serial port; both of the above - mentioned control quantities are sent from the intelligent vehicle core board to the main board STM32 for parsing into PWM waves, and are handed over to components such as the vehicle chassis motor and servo for execution; and for the third control information, that is, the remote control or the analog driving controller, the wireless control signal sent by the device is received by the receiver on the vehicle, given to the external Arduino board for processing into a control signal executable by the chassis, and then handed over to components such as the vehicle chassis relay, motor, and servo for execution.

[0104] The present invention restores functions such as roadside perception, vehicle-side perception, cooperative control, single-vehicle intelligence, and vehicle networking communication in a real vehicle-road cooperation scenario. The information flow direction conforms to the actual logic, realizing real-time simulation, real-time digital twin, and semi-physical display. The experimental effect has strong display and reproducible data, ensuring that the supported connected vehicle-road cooperation test has strong operability, high safety, and controllable cost, guaranteeing the credibility of the connected vehicle-road cooperation simulation test, thereby improving the efficiency and reliability of the connected vehicle-road cooperation simulation test.

[0105] Referring to Figure 8 , an embodiment of the present invention provides a test method for a connected vehicle-road cooperation test system, which is used to be executed by the above-mentioned connected vehicle-road cooperation test system, and includes the following steps:

[0106] S101. Perform roadside positioning on the scaled road scene and the scaled intelligent vehicle through a high-precision indoor positioning system to obtain roadside perception data;

[0107] S102. Through the cooperative server, perform regional centralized scheduling control calculation based on the roadside perception data and the vehicle status information reported by each scaled intelligent vehicle to obtain a multi-vehicle cooperative control instruction;

[0108] S103. Send a human control simulation signal, an autonomous driving setting information, or a multi-vehicle cooperative control instruction to the scaled intelligent vehicle through the intelligent vehicle control unit;

[0109] S104. Through the scaled intelligent vehicle, simulate the driving of an intelligent vehicle in the scaled road scene according to the human control simulation signal, the autonomous driving setting information, or the multi-vehicle cooperative control instruction;

[0110] S105. Through the digital twin unit, reproduce the simulation data of the scaled road scene and the scaled intelligent vehicle according to the scene data of the scaled road scene, the roadside perception data, and the vehicle status information.

[0111] The content in the above system embodiment is applicable to the method embodiment of the present invention. The functions specifically implemented by the method embodiment of the present invention are the same as those of the above system embodiment, and the beneficial effects achieved are also the same as those of the above system embodiment.

[0112] It should be recognized that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The above methods can be implemented in a computer program using standard programming techniques—including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes the computer to operate in a specific and predefined manner—in accordance with the methods and drawings described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, for this purpose the program is capable of running on a programmed application-specific integrated circuit.

[0113] In addition, the operations of the processes described herein can be performed in any suitable order, unless otherwise indicated herein or otherwise clearly contradicted by the context. The processes described herein (or variations and / or combinations thereof) can be executed under the control of one or more computer systems configured with executable instructions and can be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or by a combination thereof. The above computer programs include a plurality of instructions executable by one or more processors.

[0114] Further, the above methods can be implemented in any type of computing platform operably connected, including but not limited to personal computers, minicomputers, mainframes, workstations, network or distributed computing environments, separate or integrated computer platforms, or communicating with charged particle tools or other imaging devices, etc. Aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into the computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it can be read by a programmable computer and, when read by the storage medium or device, can be used to configure and operate the computer to perform the processes described herein. In addition, the machine-readable code, or portions thereof, can be transmitted via a wired or wireless network. When such media include instructions or programs that implement the above-described steps in conjunction with a microprocessor or other data processor, the inventions described herein include these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques described in the present invention, the present invention also includes the computer itself.

[0115] A computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents physical and tangible objects, including specific visual depictions of physical and tangible objects generated on the display.

[0116] As described above, only the preferred embodiments of the present invention are given, and the present invention is not limited to the above embodiments. As long as the same means are used to achieve the technical effects of the present invention, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention. Within the scope of protection of the present invention, its technical solutions and / or implementation manners can have various different modifications and changes.

Claims

1. A networked vehicle-road collaborative testing system, characterized in that: It includes scaled road scenes, high-precision indoor positioning systems, multiple scaled smart cars, smart car control units, collaborative servers, digital twin units, and communication units, among which: The scaled road scene is used to simulate urban road scenes and expressway scenes according to a preset ratio; The high-precision indoor positioning system is used to simulate the road-side positioning process in vehicle-road collaboration, and simulates GPS to provide corresponding geodetic coordinate positioning data for the scaled smart car; The scaled smart car is used to simulate the driving of a smart vehicle in the scaled road scene; The smart car control unit is used to control the driving of the scaled smart car; The collaborative server is used to simulate the road section dispatching equipment and communication base stations in real traffic scenes to realize information collection, information forwarding and collaborative control calculation; The digital twin unit is used to reproduce simulation data of the scaled road scene and the scaled smart car; The communication unit is used to simulate real Internet of Vehicles communication between the high-precision indoor positioning system, the scaled smart car, the smart car control unit, the collaborative server and the digital twin unit.

2. A connected vehicle-road collaborative testing system according to claim 1, characterized in that: The scaled road scene is provided with a scaled road network, obstacles, traffic lights and road traffic signs.

3. The connected vehicle-road collaborative testing system according to claim 1, characterized in that: The high-precision indoor positioning system includes a motion capture camera and a motion capture server. The motion capture camera is used to capture the position data of obstacles, traffic lights, road traffic signs and the scaled smart car in the scaled road scene. The motion capture server is used to perform road-end positioning of the obstacles, traffic lights, road traffic signs and the scaled smart car based on the position data to obtain road-end perception data.

4. The connected vehicle-road collaborative testing system according to claim 1, characterized in that: The scaled smart car shown has a built-in battery energy mechanism, a power mechanism, a vehicle-side computing power module, a global positioning perception module, and a vehicle-side perception device, among which: The battery energy mechanism is used to supply energy to the power mechanism, the vehicle-side computing power module, the global positioning perception module and the vehicle-side perception device through a direct current output device; The power mechanism includes a motor and a steering gear, and the power mechanism is used to control the motor to drive the scaled smart car to move forward and backward and / or control the steering gear to drive the scaled smart car to turn left and right according to the driving control signal output by the vehicle-side computing power module; The vehicle-side computing power module includes a mainboard, a high-computing power core board mounted on the mainboard, and an embedded system microcontroller; The global positioning perception module is used to simulate the geodetic coordinate positioning of the vehicle and the perception of the vehicle by the road end in the vehicle-road collaborative research; The vehicle-side sensing device is used to simulate the vehicle's perception of the surrounding environment.

5. A connected vehicle-road collaborative testing system according to claim 4, characterized in that: The vehicle-side sensing equipment includes a monocular camera, a binocular camera, a laser radar, an inertial sensor, an odometer and a steering angle feedback device.

6. A connected vehicle-road collaborative testing system according to claim 4, characterized in that: The smart car control unit includes a remote controller / driving simulation device and an automatic driving control device, wherein the remote controller / driving simulation device is used to simulate human driving control of the scaled smart car, and the automatic driving control device is used to simulate automatic driving control and regional centralized dispatching control of the scaled smart car, wherein: When simulating human driving control, the scaled smart car receives the human control simulation signal sent by the remote controller / driving simulation device, and simulates the physical steering wheel state and accelerator and brake state of the real car according to the human control simulation signal to obtain a first reference control amount, and then drives the power mechanism to perform a corresponding action through the first reference control amount; When simulating the automatic driving control, the automatic driving setting information sent by the automatic driving control device is received by the scaled smart car, and the automatic driving intelligent decision calculation, intelligent path planning calculation and reference control amount calculation are performed by the vehicle-side computing module according to the automatic driving setting information and the global positioning perception data and vehicle-side perception data of the scaled smart car, and the power mechanism is driven to perform corresponding actions according to the obtained second reference control amount; During the centralized dispatching control in the simulated area, the scaled intelligent vehicle receives the multi-vehicle collaborative control instruction sent by the automatic driving control device, generates a third reference control quantity according to the multi-vehicle collaborative control instruction, and then drives the power mechanism to perform corresponding actions according to the third reference control quantity.

7. The connected vehicle-road collaborative testing system according to claim 1, characterized in that: When simulating the road-side dispatching equipment, the collaborative server is used to collect the road-side sensing data of the high-precision indoor positioning system and the vehicle status information of each of the scaled smart cars, and perform regional centralized dispatching control calculations based on the road-side sensing data and the vehicle status information, obtain multi-vehicle collaborative control instructions for each of the scaled smart cars, and send the multi-vehicle collaborative control instructions to the corresponding scaled smart cars; When simulating a communication base station, the collaborative server is used to forward the road-side perception data and the multi-vehicle collaborative control instructions to the scaled smart car, and is also used to forward the road-side perception data and the vehicle status information to the digital twin unit.

8. The connected vehicle-road collaborative testing system according to claim 1, characterized in that: The digital twin unit is used to build a digital twin simulation model based on the scene data of the scaled road scene, the road-side perception data of the high-precision indoor positioning system, and the vehicle status information of each of the scaled smart cars, and display the digital twin simulation model.

9. A connected vehicle-road collaborative testing system according to any one of claims 1 to 8, characterized in that: The communication unit includes a wireless communication network and a wired communication network. The scaled smart car, the smart car control unit and the collaborative server are connected through the wireless communication network. The high-precision indoor positioning system, the smart car control unit, the collaborative server and the digital twin unit are all connected to the wired communication network.

10. A test method for a networked vehicle-road collaborative test system, configured to be executed by the networked vehicle-road collaborative test system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Performing road-side positioning of the scaled road scene and the scaled smart car by using the high-precision indoor positioning system to obtain road-side perception data; The collaborative server performs regional centralized dispatch control calculation according to the road-side sensing data and the vehicle status information reported by each of the scaled smart vehicles to obtain a multi-vehicle collaborative control instruction; Sending a human control simulation signal, automatic driving setting information or the multi-vehicle collaborative control instruction to the scaled smart car through the smart car control unit; The scaled smart car simulates driving of a smart vehicle in the scaled road scene according to the human control simulation signal, the automatic driving setting information or the multi-vehicle collaborative control instruction; The digital twin unit reproduces the scaled road scene and the scaled smart car through simulation data according to the scene data of the scaled road scene, the road-end perception data and the vehicle status information.

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