Connected vehicle road cooperation test system and test method thereof

Abstract

The application discloses a networked vehicle-road cooperation test system and a test method thereof. The networked vehicle-road cooperation test system is used for obtaining road end perception data by positioning a scale road scene and a scale intelligent vehicle at a road end through a high-precision indoor positioning system, obtaining multi-vehicle cooperation control instructions by performing regional centralized scheduling control calculation on the road end perception data and vehicle state information reported by each scale intelligent vehicle through a cooperation server, sending human control simulation signals, automatic driving setting information or the multi-vehicle cooperation control instructions to the scale intelligent vehicle through an intelligent vehicle control unit, simulating intelligent vehicle driving in the scale road scene through the scale intelligent vehicle, and reproducing simulation data of the scale road scene and the scale intelligent vehicle according to scene data of the scale road scene, the road end perception data and the vehicle state information through a digital twin unit. The application improves the efficiency and reliability of networked vehicle-road cooperation simulation test and can be widely applied to the technical field of intelligent transportation.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to a connected vehicle-road cooperative testing system and its testing method. Background Technology

[0002] Road traffic plays a vital role in modern transportation systems due to its high flexibility and wide applicability. Vehicles, as the main participants in road traffic, have seen significant progress in intelligent vehicle research and are widely applied. Currently, with the increasing maturity of communication, control, and artificial intelligence technologies, connected vehicle-road cooperative technology has become a driving force for further improving traffic efficiency and safety.

[0003] However, compared to research on single-vehicle intelligence, research on connected vehicle-road cooperation involves more objects and a wider scope, thus still facing bottlenecks in testing and implementation. On the one hand, numerical simulation methods are now widely used; however, the reliability of these methods in vehicle motion modeling, simulating communication processes, and simulating the computing power constraints of equipment is questionable, and there is still a significant gap between these methods and practical application. On the other hand, in some pilot areas, many organizations have conducted real-vehicle tests in closed scenarios, but this testing method suffers from limitations such as high operational difficulty, high cost, low safety, and poor flexibility.

[0004] Against this backdrop, related research has placed higher demands on testing methods for connected vehicle-road cooperative technologies, necessitating a convenient, efficient, and secure testing technique. Scaled-down experimental platforms, products of next-generation electronic and electrical technology, provide a framework for constructing connected vehicle-road cooperative systems. By integrating physical components into vehicle motion, communication, perception, and computation, the reliability of testing can be effectively improved, while ensuring ease of operation, low cost, safety, and high flexibility. Research and testing of connected vehicle-road cooperative systems should include logic such as autonomous vehicle status reporting, autonomous vehicle calculation of driving status, autonomous single-vehicle decision-making, edge computing offloading, and server-side fusion of perception information.

[0005] However, existing solutions are difficult to adapt to the perception, communication and computing logic in real vehicle-road cooperation, and are unable to support high-reliability connected vehicle-road cooperation testing. Summary of the Invention

[0006] To address the aforementioned technical problems, the present invention aims to provide a connected vehicle-road cooperative testing system and its testing method, thereby improving the efficiency and reliability of connected vehicle-road cooperative simulation testing.

[0007] The first technical solution adopted in this invention is:

[0008] A connected vehicle-road cooperative testing system includes a scaled-down road scene, a high-precision indoor positioning system, multiple scaled-down intelligent vehicles, an intelligent vehicle control unit, a cooperative server, a digital twin unit, and a communication unit, wherein:

[0009] The scaled-down road scene is used to simulate urban road scenes and expressway scenes according to a preset ratio;

[0010] The high-precision indoor positioning system is used to simulate the roadside positioning process in vehicle-road cooperation and to simulate GPS to provide the corresponding geodetic coordinate positioning data for the scaled-down intelligent vehicle.

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

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

[0013] The collaborative server is used to simulate road segment dispatching equipment and communication base stations in real traffic scenarios to realize information collection, information forwarding and collaborative control calculations.

[0014] The digital twin unit is used to reproduce the simulation data of the scaled-down road scene and the scaled-down smart vehicle;

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

[0016] Furthermore, the scaled-down road scene includes a scaled-down road network, obstacles, traffic lights, and road traffic signs.

[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-up intelligent vehicle in the scaled-up road scene. The motion capture server is used to perform roadside positioning of the obstacles, traffic lights, road traffic signs, and the scaled-up intelligent vehicle based on the pose data to obtain roadside perception data.

[0018] Furthermore, the scaled-down smart car shown incorporates a battery energy system, a power system, a vehicle-side computing module, a global positioning and perception module, and vehicle-side sensing equipment, among which:

[0019] The battery energy mechanism is used to supply power to the power mechanism, the vehicle-side computing module, the global positioning and perception module, and the vehicle-side perception device through a DC power output device.

[0020] The power mechanism includes a motor and a servo motor. 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 motor to drive the scaled-down intelligent vehicle to turn left and right according to the drive control signal output by the vehicle-end computing module.

[0021] The vehicle-side computing module includes a motherboard, a high-computing-power core board, and an embedded system microcontroller mounted on the motherboard.

[0022] The global positioning and perception module is used to simulate the geodetic coordinate positioning of vehicles and the perception of vehicles by the roadside in the vehicle-road cooperative research.

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

[0024] Furthermore, the vehicle-mounted sensing equipment 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 human driving control of the scaled-down intelligent vehicle, and the autonomous driving control device is used to simulate autonomous driving control and regional centralized scheduling control of the scaled-down intelligent vehicle, wherein:

[0026] When simulating human driving control, the scaled-down smart car receives human control simulation signals sent by the remote control / driving simulation device, and obtains a first reference control quantity by simulating the physical steering wheel state and accelerator and brake state of the real car based on the human control simulation signals. Then, the power mechanism is driven to perform corresponding actions through the first reference control quantity.

[0027] During simulated autonomous driving control, the scaled-down smart car receives autonomous driving setting information sent by the autonomous driving control device. The vehicle-side computing module performs autonomous driving intelligent decision calculation, intelligent path planning calculation, and reference control quantity calculation based on the autonomous driving setting information, the global positioning perception data of the scaled-down smart car, and the vehicle-side perception data. Based on the obtained second reference control quantity, the power mechanism is driven to perform corresponding actions.

[0028] During the simulated area centralized scheduling and control, the scaled-down intelligent vehicle receives multi-vehicle collaborative control instructions sent by the autonomous driving control device, generates a third reference control quantity based on the multi-vehicle collaborative control instructions, and then drives the power mechanism to perform corresponding actions based on the third reference control quantity.

[0029] Furthermore, when simulating roadside dispatching equipment, the collaborative server is used to collect roadside perception data of the high-precision indoor positioning system and vehicle status information of each scaled-down smart vehicle, and to perform regional centralized dispatching control calculations based on the roadside perception data and vehicle status information to obtain multi-vehicle collaborative control instructions for each scaled-down smart vehicle, and to send the multi-vehicle collaborative control instructions to the corresponding scaled-down smart vehicle.

[0030] When simulating a communication base station, the collaborative server is used to forward the roadside perception data and the multi-vehicle collaborative control commands to the scaled-down intelligent vehicle, and is also used to forward the roadside 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-down road scene, the roadside perception data of the high-precision indoor positioning system, and the vehicle status information of each scaled-down smart vehicle, and to display the digital twin simulation model.

[0032] Furthermore, the communication unit includes a wireless communication network and a wired communication network. The scaled-down 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.

[0033] The second technical solution adopted in this invention is:

[0034] A testing method for a connected vehicle-road cooperative testing system, used to perform tests through the aforementioned connected vehicle-road cooperative testing system, includes the following steps:

[0035] The high-precision indoor positioning system is used to perform roadside positioning of the scaled road scene and the scaled intelligent vehicle to obtain roadside perception data.

[0036] The collaborative server performs regional centralized scheduling and control calculations based on the roadside perception data and the vehicle status information reported by each scaled-down intelligent vehicle to obtain multi-vehicle collaborative control instructions.

[0037] The intelligent vehicle control unit sends human control simulation signals, autonomous driving setting information, or multi-vehicle collaborative control commands to the scaled-down intelligent vehicle.

[0038] The scaled-down intelligent vehicle simulates intelligent vehicle driving in the scaled-down road scene according to the human control simulation signal, the autonomous driving setting information, or the multi-vehicle cooperative control command.

[0039] The digital twin unit uses scene data of the scaled road scene, roadside perception data, and vehicle status information to simulate and reproduce the scaled road scene and the scaled intelligent vehicle.

[0040] The beneficial effects of this invention are as follows: This invention provides a connected vehicle-road cooperative testing system and its testing method. The connected vehicle-road cooperative testing system includes a scaled-down road scene, a high-precision indoor positioning system, multiple scaled-down intelligent vehicles, an intelligent vehicle control unit, a cooperative server, a digital twin unit, and a communication unit. The high-precision indoor positioning system performs roadside positioning on the scaled-down road scene and the scaled-down intelligent vehicles to obtain roadside perception data. The cooperative server performs regional centralized scheduling and control calculations based on the roadside perception data and the vehicle status information reported by each scaled-down intelligent vehicle to obtain multi-vehicle cooperative control commands. The intelligent vehicle control unit sends human control simulation signals, autonomous driving setting information, or multi-vehicle cooperative control commands to the scaled-down intelligent vehicles. The scaled-down intelligent vehicles simulate intelligent vehicle driving in the scaled-down road scene based on the human control simulation signals, autonomous driving setting information, or multi-vehicle cooperative control commands. The digital twin unit reproduces the simulation data of the scaled-down road scene and the scaled-down intelligent vehicles based on the scene data of the scaled-down road scene, the roadside perception data, and the vehicle status information. This invention recreates the functions of roadside perception, vehicle-side perception, collaborative control, single-vehicle intelligence, and vehicle-to-everything (V2X) communication in real-world vehicle-road cooperative scenarios. The information flow conforms to actual logic, enabling real-time simulation, real-time digital twin, and semi-physical demonstration. The experimental results are highly demonstrable and the data is reproducible, ensuring that the supported connected vehicle-road cooperative testing is highly operable, safe, and cost-controllable. This guarantees the credibility of connected vehicle-road cooperative simulation testing, thereby improving the efficiency and reliability of connected vehicle-road cooperative simulation testing. Attached Figure Description

[0041] Figure 1 A module block diagram of a connected vehicle-road cooperative testing system provided in an embodiment of the present invention;

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

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

[0044] Figure 4 A schematic diagram of the digital twin interface of the digital twin unit provided in an embodiment of the present invention;

[0045] Figure 5 This is a top view of a scaled-down road scene provided in an embodiment of the present invention;

[0046] Figure 6 This is a positioning signal flow diagram provided in an embodiment of the present invention;

[0047] Figure 7 The control information flow diagram provided in the embodiments of the present invention;

[0048] Figure 8 A flowchart illustrating the steps of a test method for a connected vehicle-road cooperative testing system provided in an embodiment of the present invention. Detailed Implementation

[0049] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

[0050] In the description of this invention, "multiple" means two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or the order in which the indicated technical features are presented. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing particular embodiments only and not for limiting the invention.

[0051] Reference Figure 1 This invention provides a connected vehicle-road cooperative testing system, including a scaled-down road scene, a high-precision indoor positioning system, multiple scaled-down intelligent vehicles, an intelligent vehicle control unit, a cooperative server, a digital twin unit, and a communication unit, wherein:

[0052] The scaled-down road scene is used to simulate urban road scenes and expressway scenes according to a preset scale;

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

[0054] The scaled-down intelligent vehicle is used to simulate the driving of intelligent vehicles in scaled-down road scenarios.

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

[0056] The collaborative server is used to simulate road segment dispatching equipment and communication base stations in real traffic scenarios to realize information collection, information forwarding, and collaborative control calculations.

[0057] Digital twin units are used to reproduce simulation data of scaled-down road scenes and scaled-down intelligent vehicles;

[0058] The communication unit is used to simulate real vehicle-to-everything (V2X) communication between a high-precision indoor positioning system, a scaled-down smart car, a smart car control unit, a collaborative server, and a digital twin unit.

[0059] As an optional implementation, the scaled-down road scene includes a scaled-down road network, obstacles, traffic lights, and road traffic signs.

[0060] Specifically, the scaled-down road scenes include urban road scenes and expressway scenes. Based on the actual road dimensions and turning angle standards, the maps are drawn to a uniform scale and can include traffic signs and traffic rule markings. The above scene maps are fixed on the indoor floor, and the display forms include, but are not limited to, one or more of the following: floor painting, custom flooring, and programmable screens. The indoor display and installation methods of the scenes need to take into account factors such as material deformation, surface friction coefficient, and color contrast between passable areas and obstacle areas.

[0061] As an optional implementation, 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 scaled-up intelligent vehicles in a scaled-down road scene. The motion capture server is used to perform roadside positioning of obstacles, traffic lights, road traffic signs, and scaled-up intelligent vehicles based on the pose data to obtain roadside perception data.

[0062] Specifically, the high-precision indoor positioning system is used to simulate roadside positioning methods in vehicle-road cooperation, and can also simulate GPS to provide the vehicle with its own geodetic coordinates. The aforementioned roadside positioning has globality and can provide a correct benchmark for the fusion effect of multi-vehicle perception data. The vehicle-side positioning accuracy should be higher than the GPS accuracy in the actual application scenario after scaling, leaving a margin to ensure that the performance can be reduced to simulate the real scenario. High-precision indoor positioning technologies include, but are not limited to, optical motion capture technology, computer vision positioning technology, ultra-wideband (UWB) positioning technology, and RFID-based positioning technology.

[0063] As a further optional implementation, the scaled-down smart car shown incorporates a battery energy system, a power system, a vehicle-side computing module, a global positioning and perception module, and vehicle-side sensing devices, wherein:

[0064] The battery power unit is used to supply power to the power unit, vehicle-side computing module, global positioning and sensing module, and vehicle-side sensing equipment through DC power output devices.

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

[0066] The vehicle-side computing module includes a motherboard, a high-performance computing core board mounted on the motherboard, and an embedded system microcontroller.

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

[0068] Vehicle-mounted sensing devices are used to simulate a vehicle's perception of its surrounding environment.

[0069] Specifically, a scaled-down intelligent vehicle should have driving capabilities and include a built-in power mechanism (such as a motor, servo motor, etc.), battery energy mechanism, transmission mechanism, and shock absorption mechanism; it should also include vehicle-side computing power, which completes tasks such as sending and receiving information, control calculation, and positioning fusion calculation, and then sends executable control quantities to the power mechanism for execution.

[0070] The driving control of the scaled-down intelligent vehicle should conform to the logic of real-world driving scenarios, involving both existing human driving and onboard driver assistance systems. Human driving is simulated by a driving simulator or remote control, with signals received by the onboard receiver and directly controlling the powertrain. This control takes priority over the control signals from the aforementioned computing devices, mirroring the logic in the real world where human intervention takes precedence over driver assistance systems. The computing devices simulate the computing power of real-world onboard autonomous driving systems, controlled by a single-vehicle control computer deployed off-site, managing functions such as program startup, shutdown, cruise control acceleration / deceleration, and mode switching. Furthermore, in this invention, the control computer can also serve as an edge offloading device for onboard computing power.

[0071] As an optional implementation, the vehicle-mounted sensing devices include a monocular camera, a binocular camera, a lidar, an inertial sensor, an odometer, and a steering angle feedback device.

[0072] Specifically, scaled-down smart cars should have vehicle-side information perception capabilities, involving sensing devices including but not limited to single and dual-lens cameras, LiDAR, odometers, inertial navigation, etc. Vehicle-side perception functions include automatically captured "geocentric coordinates" and data from its own sensing devices, utilizing onboard or edge computing power for multimodal fusion.

[0073] As a further 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 human driving control of the scaled-down intelligent vehicle, and the autonomous driving control device is used to simulate autonomous driving control and regional centralized scheduling control of the scaled-down intelligent vehicle, wherein:

[0074] When simulating human driving control, the scaled-down smart car receives human control simulation signals sent by the remote control / driving simulation device, and obtains the first reference control quantity by simulating the physical steering wheel state and accelerator and brake state of the real car based on the human control simulation signals. Then, the power mechanism is driven to perform corresponding actions through the first reference control quantity.

[0075] During simulated autonomous driving control, the scaled-down intelligent vehicle receives autonomous driving setting information sent by the autonomous driving control device. The vehicle-side computing module performs autonomous driving intelligent decision calculation, intelligent path planning calculation, and reference control quantity calculation based on the autonomous driving setting information, the global positioning perception data of the scaled-down intelligent vehicle, and the vehicle-side perception data. Based on the obtained second reference control quantity, the power mechanism is driven to perform corresponding actions.

[0076] During the centralized scheduling and control of the simulated area, the scaled-down intelligent vehicle receives multi-vehicle collaborative control commands sent by the autonomous driving control equipment, generates a third reference control quantity based on the multi-vehicle collaborative control commands, and then drives the power mechanism to perform corresponding actions based on the third reference control quantity.

[0077] As an optional implementation, when simulating roadside dispatching equipment, the collaborative server is used to collect roadside perception data from the high-precision indoor positioning system and vehicle status information of each scaled-down intelligent vehicle, and to perform regional centralized dispatching control calculations based on the roadside perception data and vehicle status information to obtain multi-vehicle collaborative control instructions for each scaled-down intelligent vehicle, and to send the multi-vehicle collaborative control instructions to the corresponding scaled-down intelligent vehicle.

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

[0079] Specifically, the collaborative server is deployed outside the road scenario, has high computing power, and undertakes tasks such as information collection, information forwarding, and collaborative control computing, simulating roadside dispatching equipment and communication base stations in real traffic scenarios;

[0080] Furthermore, for the simulated road segment dispatching equipment, the collaborative server collects vehicle status through two methods: roadside sensing and reporting from each vehicle. The server performs centralized control calculations and sends control commands to each vehicle for execution. For the simulated communication base station, the collaborative server forwards information in three ways: first, it forwards high-precision indoor positioning system data to the intelligent vehicle and digital twin unit; second, it forwards control equipment commands, packages them together with positioning data, and broadcasts them to each vehicle; and third, it forwards the collected vehicle status to the digital twin unit.

[0081] As an optional implementation, the digital twin unit is used to construct a digital twin simulation model based on scene data of the scaled-down road scene, roadside perception data of the high-precision indoor positioning system, and vehicle status information of each scaled-down intelligent vehicle, and to 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 a collaborative server via a wired network, enabling functions such as virtual perspective, data storage, and data reproduction.

[0083] As an optional implementation, the communication unit includes a wireless communication network and a wired communication network. The scaled-down smart car, the smart car control unit, and the collaborative server are connected via the wireless communication network, while 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.

[0084] Specifically, the communication unit simulates real vehicle-to-everything (V2X) communication. The scaled-down intelligent vehicle is connected to the control unit and the collaborative server via a wireless communication module, while the collaborative server, control unit, and digital twin unit are connected via a wired network. The communication unit is responsible for functions such as issuing scheduling instructions, reporting vehicle-side 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 used include, but are not limited to, wireless local area network communication technology (Wi-Fi), wireless serial communication technology, LoRa, Zigbee, etc. The wireless module uses one or more of these technologies in combination to complete wireless communication. During the transmission of positioning information and control commands, the wireless module used must outperform real vehicle networks in terms of information latency, packet loss rate, and other related indicators, with a margin to ensure that real-world scenarios can be simulated by reducing performance.

[0086] The connected vehicle-road cooperative testing system of the present invention will be further described below with reference to a specific embodiment.

[0087] This invention includes 24 motion capture cameras, 1 motion capture server, 20 (or more) intelligent vehicles, and each vehicle is equipped with a remote control, a digital twin display device, at least one intelligent vehicle control unit, 1 local area network router, and 1 collaborative server. In this invention, the above-mentioned devices are mainly connected via wired network, wireless network, and wireless serial port.

[0088] The motion capture camera and motion capture server constitute the aforementioned high-precision indoor positioning system. Motion capture positioning is a high-precision infrared optical positioning technology that uses multiple optical lenses arranged in the venue to capture the position information of reflective markers fixed on the surface of objects (smart cars, traffic lights, or obstacles, etc.) from different angles, capturing their position, movement, and posture.

[0089] In this embodiment of the invention, the NOKOV measurement technology solution is used, with 24 motion capture cameras fixedly installed on the four walls of a 625-square-meter indoor laboratory at a height of 5 meters, and the installation is as evenly distributed as possible. The motion capture cameras transmit sensing information via a wired network, which is then input to the motion capture server via a switch.

[0090] In this embodiment of the invention, the motion capture server is equipped with a Windows 11 operating system and XINGYING, a dedicated motion capture information processing software. This software can perform operations such as site location determination, information unification, coordinate transformation, and transmission. The motion capture server connects to the laboratory's local area network via a wired network and packages the positioning information of all target rigid bodies before sending it via VPN.

[0091] The collaborative server connects to the laboratory's local area network via a wired network, and receives location information from the collaborative server. In this embodiment of the invention, the collaborative server is running an Ubuntu system. It uses the ROS framework to parse motion capture VRPN data and perform centralized collaborative control calculations, while the information sending and forwarding services are developed using the Go language. Location information and automatic control commands are packaged on the collaborative server, broadcast using a wireless serial port device, and then received by each intelligent vehicle.

[0092] like Figure 2 The diagram shows a wireless serial port message format provided in an embodiment of the present invention. The message consists of five parts: frame header, data length, data area, checksum, and frame trailer. The lengths of the fields occupied by these parts are 2 bytes, 2 bytes, (8 × unit quantity) bytes, 1 byte, and 1 byte, respectively. The start frame is marked as 0×7E7E, the checksum is verified using the CRC-16 / XMODEM method, and the frame trailer is marked as 0×7E7D.

[0093] The size of the data area is determined by the number of test units (intelligent vehicles, traffic lights, or obstacles, etc.). Each unit has a reserved length of 8 bytes, divided into five positions. Position 1 occupies 1 byte, representing the corresponding unit number, such as "Car:1"; Position 2 occupies 2 bytes, representing the unit's X coordinate; Position 3 occupies 2 bytes, representing the unit's Y coordinate; Position 4 occupies 2 bytes, representing the unit's orientation (yaw); Position 5 occupies 1 byte, representing the unit's control information. Bit 0 is the start / stop control value, Bits 1-4 are speed control values ​​(acceleration type), and Bits 5-7 are mode control values, such as line-following mode, cooperative mode, and follow mode.

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

[0095] The specific functions implemented by ROS are: receiving information (positioning + control) sent by the wireless serial port, performing information decoding, positioning fusion, automatic control algorithm calculation and other computational tasks internally, and then transmitting executable reference control quantities to the chassis, while simultaneously reporting the vehicle status to the chassis via UDP.

[0096] The intelligent vehicle control unit is a Windows system computer. This computer is connected to the local area network via 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 system control information from the on-board computing unit to the collaborative server, which then sends it to each intelligent vehicle.

[0097] like Figure 3 The diagram shows the control interface of the intelligent vehicle control unit provided in this embodiment of the invention. The left side of the interface is the vehicle selection area. A gray vehicle icon indicates that the vehicle is not online or the account does not have control permissions for the corresponding vehicle. A bright white vehicle icon indicates that the vehicle is online and the account has control permissions. A bright blue icon, as shown in Car74, indicates that the vehicle has been selected as the current control object. The numbered squares below the vehicle icons represent the voltage status reported by the vehicle. If the square fill color changes from green to yellow (voltage less than 7V), the battery level needs to be monitored. The middle of the interface is the sandbox scene display area. In this area, the position, orientation, and relative position to obstacles of all vehicles in the sandbox can be viewed in real time, assisting in vehicle control. The right side of the interface is the control area. Current control commands include: start, pause, mode selection (tracking / following), speed issuance, etc. The real-time vehicle parameters reported by the intelligent vehicle are also displayed below.

[0098] like Figure 4 The diagram shown illustrates the digital twin interface of the digital twin unit provided in this embodiment of the invention. Developed using the Unity 3D game engine, the device connects to a local area network via a wired connection and receives information from a collaborative server, including vehicle-reported information and global positioning information from the motion capture system. The 3D digital twin interface can display a sandbox scene in real time and allows for free adjustment of the camera position and viewing angle, thus simulating the driver's perspective of a vehicle to a certain extent.

[0099] The local area network router uses an enterprise-grade AX5400 dual-band Wi-Fi 6 wireless VPN router, model TL-XVR5400L Easy-to-Display Edition. In the test environment of this embodiment, with 10 vehicles running simultaneously, the latency for uploading information via Wi-Fi is controlled below 30ms, the average latency is 3.54ms, and the packet loss rate is controlled below 1‰, meeting the control requirements of real-world scenarios.

[0100] The wireless serial port broadcast information method, tested under the conditions of this embodiment, showed a positive correlation between latency and message volume. With 20 vehicles operating and a message volume, the measured information transmission latency was controlled within 70ms, with minimal fluctuation, meeting the control requirements of real-world scenarios.

[0101] like Figure 5 The image shown is a top view of a scaled-down road scene provided in an embodiment of the present invention. The miniature road floor consists of 256 1m × 1m square panels, with a total length and width of 16m and a total area of ​​256m². The origin of the coordinate system is located in the upper right corner, with the positive x-axis pointing horizontally to the left and the positive y-axis pointing vertically downwards. The miniature road floor includes simulations of urban roads and expressways. The urban roads are designed as two-lane dual carriageways, including scenes such as intersections, T-junctions, and roundabouts; the expressways are six-lane dual carriageways.

[0102] like Figure 6 The diagram illustrates the positioning signal flow provided in this embodiment of the invention. Global positioning information is generated by the motion capture positioning processing unit. The motion capture camera captures the position of reflective marker balls within the field, and this information is generated in the motion capture server corresponding to the rigid body. The information is then transmitted via a wired network through a VPN to the collaboration server, where it is received and parsed. The parsed information is transmitted via a wired network to the digital twin server for display purposes; alternatively, it remains within the server, serving both the collaboration algorithm and the system, or it can be directly packaged and broadcast via a wireless serial port. Each intelligent vehicle receives this information via a wireless serial port, parses it, and then provides it to the motion control algorithm.

[0103] like Figure 7 The diagram shown illustrates the control information flow provided in this embodiment of the invention. Vehicle control information is generated through three pathways: First, the intelligent vehicle control unit issues control commands, which are transmitted to the collaborative server via a wired network and packaged together with location information, then sent to the corresponding vehicle for execution via a wireless serial port. Second, the collaborative control algorithm receives the status information of all vehicles, generates control quantities for each vehicle, and sends them to the corresponding vehicle for execution via a wireless serial port. Both of these control quantities are sent by the intelligent vehicle core board to the STM32 motherboard, where they are parsed into PWM waves and then executed by components such as the vehicle chassis motors and servos. Third, the control information is transmitted via a remote control or driving simulator. The wireless control signals sent by these devices are received by the receiver on the vehicle and processed by an external Arduino board into control signals executable by the chassis, which are then executed by components such as the vehicle chassis relays, motors, and servos.

[0104] This invention recreates the functions of roadside perception, vehicle-side perception, collaborative control, single-vehicle intelligence, and vehicle-to-everything (V2X) communication in real-world vehicle-road cooperative scenarios. The information flow conforms to actual logic, enabling real-time simulation, real-time digital twin, and semi-physical demonstration. The experimental results are highly demonstrable and the data is reproducible, ensuring that the supported connected vehicle-road cooperative testing is highly operable, safe, and cost-controllable. This guarantees the credibility of connected vehicle-road cooperative simulation testing, thereby improving the efficiency and reliability of connected vehicle-road cooperative simulation testing.

[0105] Reference Figure 8 This invention provides a testing method for a connected vehicle-road cooperative testing system, which is used to perform tests through the aforementioned connected vehicle-road cooperative testing system, and includes the following steps:

[0106] S101. Use a high-precision indoor positioning system to perform roadside positioning on scaled-down road scenes and scaled-down intelligent vehicles to obtain roadside perception data.

[0107] S102. The collaborative server performs regional centralized scheduling and control calculations based on roadside perception data and vehicle status information reported by each scaled-down intelligent vehicle to obtain multi-vehicle collaborative control instructions.

[0108] S103. Send human control simulation signals, autonomous driving setting information or multi-vehicle collaborative control commands to the scaled-down intelligent vehicle through the intelligent vehicle control unit.

[0109] S104. The scaled-down intelligent vehicle simulates intelligent vehicle driving in a scaled-down road scene based on human control simulation signals, autonomous driving setting information or multi-vehicle cooperative control instructions.

[0110] S105. Using a digital twin unit, the scaled-down road scene and the scaled-down intelligent vehicle are simulated and reproduced based on scene data, roadside perception data, and vehicle status information.

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

[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 storage medium. The methods described above can be implemented using standard programming techniques—including implementation in a computer program on a non-transitory computer-readable storage medium configured to allow the computer to operate in a specific and predefined manner—according to 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 the 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. Furthermore, for this purpose, the program can run on a programmed application-specific integrated circuit (ASIC).

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

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

[0115] A computer program can be applied to input data to perform the functions described herein, thereby transforming the input data to generate output data stored in 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 invention, the transformed data represents physical and tangible objects, including a specific visual depiction of physical and tangible objects generated on the display.

[0116] The above description is merely a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention, as long as they achieve the technical effects of the present invention by the same means, should be included within the scope of protection of the present invention. Within the scope of protection of the present invention, the technical solutions and / or implementation methods can have various modifications and variations.

Claims

1. A connected vehicle-road cooperative testing system, characterized in that, This includes scaled-down road scenes, a high-precision indoor positioning system, multiple scaled-down smart vehicles, a smart vehicle control unit, a collaborative server, a digital twin unit, and a communication unit, among which: The scaled-down 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 roadside positioning process in vehicle-road cooperation and to simulate GPS to provide the corresponding geodetic coordinate positioning data for the scaled-down intelligent vehicle. The scaled-down intelligent vehicle is used to simulate the driving of an intelligent vehicle in the scaled-down road scenario. The intelligent vehicle control unit is used for driving control of the scaled-down intelligent vehicle; The collaborative server is used to simulate road segment dispatching equipment and communication base stations in real traffic scenarios to realize information collection, information forwarding, and collaborative control calculation. When simulating road segment dispatching equipment, the collaborative server is used to collect road segment perception data of the high-precision indoor positioning system and vehicle status information of each scaled-down intelligent vehicle, and perform regional centralized dispatching control calculation based on the road segment perception data and vehicle status information to obtain multi-vehicle collaborative control instructions for each scaled-down intelligent vehicle, and send the multi-vehicle collaborative control instructions to the corresponding scaled-down intelligent vehicle. When simulating communication base stations, the collaborative server is used to forward the road segment perception data and the multi-vehicle collaborative control instructions to the scaled-down intelligent vehicle, and also to forward the road segment perception data and vehicle status information to the digital twin unit. The digital twin unit is used to reproduce the simulation data of the scaled-down road scene and the scaled-down smart vehicle; The communication unit is used to simulate real vehicle-to-everything (V2X) communication between the high-precision indoor positioning system, the scaled-down smart vehicle, the smart vehicle control unit, the collaborative server, and the digital twin unit. The scaled-down smart car incorporates a battery energy system, a power system, a vehicle-side computing module, a global positioning and perception module, and vehicle-side sensing devices, among which: The battery energy mechanism is used to supply power to the power mechanism, the vehicle-side computing module, the global positioning and perception module, and the vehicle-side perception device through a DC power output device. The power mechanism includes a motor and a servo motor. 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 motor to drive the scaled-down intelligent vehicle to turn left and right according to the drive control signal output by the vehicle-end computing module. The vehicle-side computing module includes a motherboard, a high-computing-power core board, and an embedded system microcontroller mounted on the motherboard. The global positioning and perception module is used to simulate the geodetic coordinate positioning of vehicles and the perception of vehicles by the roadside in the vehicle-road cooperative research. The vehicle-mounted sensing device is used to simulate the vehicle's perception of its surrounding environment. 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 human driving control of the scaled-down intelligent vehicle, and the autonomous driving control device is used to simulate autonomous driving control and regional centralized dispatch control of the scaled-down intelligent vehicle, wherein: When simulating human driving control, the scaled-down smart car receives human control simulation signals sent by the remote control / driving simulation device, and obtains a first reference control quantity by simulating the physical steering wheel state and accelerator and brake state of the real car based on the human control simulation signals. Then, the power mechanism is driven to perform corresponding actions through the first reference control quantity. During simulated autonomous driving control, the scaled-down smart car receives autonomous driving setting information sent by the autonomous driving control device. The vehicle-side computing module performs autonomous driving intelligent decision calculation, intelligent path planning calculation, and reference control quantity calculation based on the autonomous driving setting information, the global positioning perception data of the scaled-down smart car, and the vehicle-side perception data. Based on the obtained second reference control quantity, the power mechanism is driven to perform corresponding actions. During the simulated area centralized scheduling and control, the scaled-down intelligent vehicle receives multi-vehicle collaborative control instructions sent by the autonomous driving control device, generates a third reference control quantity based on the multi-vehicle collaborative control instructions, and then drives the power mechanism to perform corresponding actions based on the third reference control quantity.

2. The connected vehicle-road cooperative testing system according to claim 1, characterized in that: The scaled-down road scene includes a scaled-down road network, obstacles, traffic lights, and road traffic signs.

3. The connected vehicle-road cooperative 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 pose data of obstacles, traffic lights, road traffic signs, and the scaled-up intelligent vehicle in the scaled-up road scene. The motion capture server is used to perform roadside positioning of the obstacles, traffic lights, road traffic signs, and the scaled-up intelligent vehicle based on the pose data to obtain roadside perception data.

4. The connected vehicle-road cooperative testing system according to claim 1, characterized in that: The vehicle-mounted sensing equipment includes a monocular camera, a binocular camera, a lidar, an inertial sensor, an odometer, and a steering angle feedback device.

5. The connected vehicle-road cooperative testing system according to claim 1, characterized in that: The digital twin unit is used to construct a digital twin simulation model based on the scene data of the scaled-down road scene, the roadside perception data of the high-precision indoor positioning system, and the vehicle status information of each scaled-down intelligent vehicle, and to display the digital twin simulation model.

6. A connected vehicle-road cooperative testing system according to any one of claims 1 to 5, characterized in that: The communication unit includes a wireless communication network and a wired communication network. The scaled-down 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.

7. A testing method for a connected vehicle-road cooperative testing system, used to execute the test using the connected vehicle-road cooperative testing system as described in any one of claims 1 to 6, characterized in that, Includes the following steps: The high-precision indoor positioning system is used to perform roadside positioning of the scaled road scene and the scaled intelligent vehicle to obtain roadside perception data. The collaborative server performs regional centralized scheduling and control calculations based on the roadside perception data and the vehicle status information reported by each scaled-down intelligent vehicle to obtain multi-vehicle collaborative control instructions. The intelligent vehicle control unit sends human control simulation signals, autonomous driving setting information, or multi-vehicle collaborative control commands to the scaled-down intelligent vehicle. The scaled-down intelligent vehicle simulates intelligent vehicle driving in the scaled-down road scene according to the human control simulation signal, the autonomous driving setting information, or the multi-vehicle cooperative control command. The digital twin unit uses the scene data of the scaled-down road scene, the roadside perception data, and the vehicle status information to simulate and reproduce the scaled-down road scene and the scaled-down intelligent vehicle.

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