Actual road driving simulation test system, method, device, equipment and medium

Abstract

The invention relates to an actual road driving simulation test system, method, device and equipment and a medium. The system comprises an actual data acquisition device, a chassis dynamometer, a panoramic display and a control device. The actual data acquisition device is used for acquiring route data of an actually measured road section; the chassis dynamometer is used for simulating the driving state of the tested vehicle during driving based on the route data; the panoramic display is used for dynamically displaying a road scene when the tested vehicle runs; and the control device is used for acquiring target environment information matched with the driving road mileage of the tested vehicle based on the real-time vehicle speed, generating target simulation information corresponding to the background vehicle based on the driving state and the target environment information, and generating a scene updating instruction based on the target environment information and the target simulation information. According to the invention, the physical condition and the dynamic traffic environment of the actually measured road can be accurately reproduced in a laboratory, and the full-working-condition test of the vehicle in a complex scene is realized.

Description

Technical Field

[0001] This application relates to the technical field of vehicle testing and simulation, and in particular to a real-road driving simulation testing system, method, apparatus, equipment and medium. Background Technology

[0002] With the rapid development of the automotive industry and the continuous innovation of intelligent and electric technologies, the demand for vehicle performance testing is becoming increasingly complex and diverse. Traditional road testing, as the core means of vehicle performance verification, can directly reflect the vehicle's performance in real driving environments, but its shortcomings, such as long testing cycles, high costs, low testing efficiency, and high safety risks, are becoming increasingly prominent.

[0003] Existing chassis-based dynamometer-based laboratory testing systems primarily focus on single-machine simulations of vehicle dynamics, such as driving resistance and inertia, while neglecting real-world traffic flow dynamics, complex road conditions, and unexpected scenarios. This results in a significant disconnect between the test scenarios and real-world driving environments, failing to comprehensively reflect vehicle performance on actual roads. Furthermore, existing laboratory testing systems largely rely on pre-set fixed test cycles, such as NEDC and WLTC, and cannot dynamically generate customized test scenarios based on actual road data. This makes it difficult for these systems to meet emerging demands such as ADAS (Advanced Driver Assistance Systems) verification and new energy vehicle energy consumption analysis, limiting the expansion of their application scenarios.

[0004] In summary, there is an urgent need for a highly integrated driving simulation testing technology that can accurately reproduce the physical conditions of actual roads (such as driving resistance and altitude changes), dynamic traffic environments (such as traffic flow and traffic lights), and unexpected scenarios in the laboratory, and achieve efficient collection and analysis of multi-source data to meet the diversified needs of vehicle performance testing, intelligent driving verification, and environmental compliance assessment, thereby promoting the continuous development and innovation of the automotive industry. Summary of the Invention

[0005] In order to accurately reproduce the physical conditions and unexpected scenarios of actual roads in the laboratory and achieve efficient collection and analysis of multi-source data to meet the diversified needs of vehicle performance testing, intelligent driving verification and environmental compliance assessment, this application provides a real road driving simulation test system, method, device, equipment and medium.

[0006] In a first aspect, this application provides a real-road driving simulation test system, including a real data acquisition device, a chassis dynamometer, a panoramic display, and a control device; The actual data acquisition device is used to acquire route data of the measured road section. The route data includes road mileage, elevation changes and environmental information. The environmental information includes road signs, traffic light locations, intersection distribution and roadside scenes. The chassis dynamometer is used to simulate the driving state of the vehicle under test based on the route data. The driving state includes driving resistance, vehicle inertia and real-time vehicle speed. The panoramic display is used to dynamically display the road scene when the vehicle under test is driving, and the road scene includes the environmental information and background vehicles; The control device is used to acquire target environment information matching the road mileage traveled by the tested vehicle based on the real-time vehicle speed; and to dynamically generate target simulation information corresponding to the background vehicle based on the driving state and the target environment information, wherein the target simulation information includes driving mode, vehicle type and number of vehicles. The control device is further configured to generate a scene update command based on the target environment information and the target simulation information, so that the panoramic display updates the current road scene to the target road scene.

[0007] The beneficial effects of this application are as follows: the control device dynamically retrieves environmental information of the target road segment based on real-time vehicle speed and road mileage, and generates simulated behavior of the background vehicle. Through the coordinated control of the chassis dynamometer, the control device, and the panoramic display, the physical conditions and dynamic traffic environment of the actual test road can be accurately replicated in the laboratory, enabling full-condition testing of vehicles in complex scenarios. It achieves efficient acquisition and analysis of multi-source data, significantly improving testing efficiency and safety, meeting the diversified needs of vehicle performance testing, and promoting the continuous development and innovation of the automotive industry.

[0008] Furthermore, the simulation testing system also includes a data acquisition and processing module; The acquisition and processing module is used to acquire vehicle operation information in real time and perform in-depth analysis and processing on the vehicle operation information to obtain target information. The vehicle operation information includes the driving status, the output data of the chassis dynamometer, and the road scene. The output data includes torque, speed, and power. The target information includes the energy consumption, environmental performance, acceleration performance, or braking performance of the vehicle under test. The data acquisition and processing module is also used to generate a performance test analysis report of the vehicle under test based on the target information.

[0009] The beneficial effects of adopting the above-mentioned further solution are: the data acquisition and processing module performs in-depth analysis and processing of the collected vehicle operation information to extract valuable target information. The analyzed and processed target information can be presented to the driver in the form of a quantitative report. The performance test analysis report can intuitively show the performance of the tested vehicle in a simulated environment, providing strong support for the performance optimization of the tested vehicle and the verification of the intelligent driving system.

[0010] Furthermore, the data acquisition and processing module has a built-in edge computing unit; The edge computing unit is used to identify the vehicle operation information based on a machine learning model, obtain the driving behavior pattern of the driver corresponding to the tested vehicle, and obtain the safety risk assessment information corresponding to the tested vehicle based on the driving behavior pattern. The driving behavior pattern includes driving habits or driving style. The edge computing unit is also used to generate a multi-dimensional test report based on the driving behavior pattern, the safety risk assessment information, and the performance test analysis report.

[0011] The beneficial effects of adopting the above-mentioned further solutions are: the generation of multi-dimensional test reports also provides drivers with a more comprehensive and in-depth analysis of test results, avoids the upload delay of massive amounts of raw data, and quickly outputs quantifiable test conclusions.

[0012] Furthermore, the control device includes a traffic flow simulation module; The traffic flow simulation module is used to obtain a traffic flow model based on the actual test requirements corresponding to the vehicle under test, so that the traffic flow model generates initial background vehicle behavior based on the traffic flow data of the measured road segment; it is also used to adjust the initial background vehicle behavior in real time based on the driving state and the target environment information to obtain the target simulation information.

[0013] The beneficial effects of adopting the above-mentioned further solutions are: by generating initial background vehicle behavior, it is possible to more accurately simulate traffic flow under real road conditions, thereby improving the realism and credibility of the tested road scenarios. Through a data-driven traffic flow simulation module, it is ensured that the tested road scenarios are highly consistent with actual road conditions, further enhancing the realism and credibility of the test results.

[0014] Furthermore, the control device also includes an unexpected scenario generation module; The unexpected scene generation module is used to generate unexpected scenes based on the scene triggering mode and update the unexpected scenes to the target road scene. The scene triggering mode is a random triggering mode or a controllable triggering mode. The unexpected scenes include pedestrians crossing the road, vehicles in front braking suddenly, vehicles breaking down and stopping, traffic accidents, or severe weather. If the scene triggering mode is a random triggering mode, then the unexpected scene generation module is used to randomly generate the unexpected scene based on a preset probability; If the scene triggering mode is a controllable triggering mode, then the unexpected scene generation module is used to generate the preset unexpected scene when the preset triggering conditions are met.

[0015] The beneficial effects of adopting the above-mentioned further solutions are: the actual road driving simulation test system can more flexibly simulate various sudden risk situations, providing stronger support for the verification of intelligent driving systems. Through a flexible scenario generation mechanism, it can simulate unpredictable sudden risks and also perform repeated verification for specific functions, thus balancing the comprehensiveness and specificity of the test.

[0016] Furthermore, the panoramic display includes a surround LED display array, a viewpoint tracking sensor, and a scene rendering engine; The surround LED display array is composed of multiple LED screens spliced ​​together to form the visual display area corresponding to the panoramic display. The viewpoint tracking sensor is used to track the driver's head position and direction in real time in order to adjust the viewing angle and display range of the panoramic display. The scene rendering engine is used to render the target road scene in real time.

[0017] The beneficial effects of adopting the above-mentioned further solutions are: panoramic displays can provide more immersive and realistic visual feedback, giving drivers a sensory experience consistent with real driving. This eliminates the image delay and perspective distortion problems of traditional simulators, allowing drivers to obtain a sensory experience consistent with real driving.

[0018] Secondly, this application provides a method for simulating driving on a real road, applied to a control device, comprising: Based on the real-time speed of the vehicle under test, target environment information matching the road mileage traveled by the vehicle under test is obtained. The target environment information includes road signs, traffic light locations, intersection distribution, and roadside scenes. Based on the driving status and the target environment information, target simulation information corresponding to the background vehicle is dynamically generated. The target simulation information includes driving mode, vehicle type and number of vehicles. The driving status includes driving resistance, vehicle inertia and real-time vehicle speed simulated by the chassis dynamometer based on route data. Based on the target environment information and the target simulation information, a scene update command is generated and sent to the panoramic display so that the panoramic display updates the current road scene to the target road scene.

[0019] Thirdly, this application provides a real-road driving simulation testing device, comprising: The environment acquisition module is used to acquire target environment information that matches the road mileage traveled by the test vehicle based on the real-time vehicle speed. The target environment information includes road signs, traffic light locations, intersection distribution, and roadside scenes. The background vehicle simulation module is used to dynamically generate target simulation information corresponding to the background vehicle based on the driving status and the target environment information. The target simulation information includes driving mode, vehicle type and number of vehicles. The driving status includes driving resistance, vehicle inertia and real-time vehicle speed simulated by the chassis dynamometer based on route data. The scene update module is used to generate a scene update command based on the target environment information and the target simulation information, and send it to the panoramic display so that the panoramic display updates the current road scene to the target road scene.

[0020] Fourthly, this application provides an electronic device, including a processor and a memory, wherein the processor is coupled to the memory; The processor is configured to execute a computer program stored in the memory, so that the electronic device performs the method as described in the second aspect.

[0021] Fifthly, this application provides a computer-readable storage medium including a computer program or instructions that, when executed on a computer, cause the computer to perform the method described in the second aspect. Attached Figure Description

[0022] Figure 1 This is a structural block diagram of the actual road driving simulation test system according to an embodiment of this application; Figure 2 This is a flowchart illustrating the actual road driving simulation test method according to an embodiment of this application; Figure 3 This is a structural block diagram of the actual road driving simulation test device according to an embodiment of this application; Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application.

[0023] The attached diagram lists the components represented by each number as follows: 1. Actual data acquisition device; 2. Chassis dynamometer; 3. Panoramic display; 4. Control device. Detailed Implementation

[0024] The present application will be further described in detail below with reference to the accompanying drawings.

[0025] like Figure 1 As shown in the figure, this application provides a real-road driving simulation test system, including a real data acquisition device 1, a chassis dynamometer 2, a panoramic display 3, and a control device 4.

[0026] The actual data acquisition device 1 is used to acquire route data of the measured road section. The route data includes road mileage, altitude changes and environmental information. The environmental information includes road signs, traffic light positions, intersection distribution and roadside scenes.

[0027] In this embodiment, the actual data acquisition device 1 can employ multimodal sensor fusion, specifically including: LiDAR (Light Detection and Ranging) is used to scan the geometry of measured roads (such as slope and curvature) and the location of obstacles, generating high-precision point clouds (accuracy ±2cm). Multiple frames of LiDAR data can be fused into a 3D road model using the SLAM (Simplified Language Acoustics) algorithm, and key features such as intersection boundaries can be annotated to obtain the intersection distribution. A binocular camera is used to capture images of roadside scenes and extract the color and position of road signs and traffic lights through stereo vision algorithms. High-precision GPS / IMU is used to record road mileage and altitude changes (GPS positioning accuracy 0.1m, IMU sampling rate 200Hz). It can combine GPS trajectory and IMU data, employ Kalman filtering to eliminate positioning drift, and generate a table mapping altitude to mileage.

[0028] The chassis dynamometer 2 is used to simulate the driving state of the vehicle under test based on the route data. The driving state includes driving resistance, vehicle inertia and real-time vehicle speed.

[0029] In this embodiment, the chassis dynamometer 2 is the core equipment of the actual road driving simulation testing system. Its core function is to provide the tested vehicle with driving resistance, vehicle inertia, and speed control consistent with the actual test road section by accurately simulating the dynamic characteristics of the vehicle under real road conditions. Specific functions may include: Simulation of driving resistance is used to reproduce the rolling resistance, air resistance, and gradient resistance of the tested vehicle on the actual test road section; The simulation of vehicle inertia is used to dynamically adjust the rotational inertia to match the inertial effects during vehicle acceleration / deceleration. Real-time vehicle speed simulation is used to achieve millimeter-level precision synchronization of vehicle speed through a closed-loop feedback mechanism.

[0030] In this embodiment, based on the vehicle dynamics model, the driving resistance... It can be calculated using the following formula: in, This indicates the mass (kg) of the vehicle being tested. This represents the acceleration due to gravity (9.81 m / s²). Indicates the road slope angle. Indicates air density, It represents the air drag coefficient multiplied by the frontal area (m²). This indicates the real-time vehicle speed (m / s). This represents the rolling resistance coefficient (0.01-0.015).

[0031] The chassis dynamometer 2 includes a servo motor module, which employs a high dynamic response permanent magnet synchronous motor (rated torque up to 2000 Nm, overload capacity up to 300%). The permanent magnet synchronous motor directly drives the test roller via a coupling; the test roller can be a 48-inch diameter roller. During testing, the drive wheels of the vehicle under test must be strictly aligned with the test roller, and the permanent magnet synchronous motor drives the test roller to rotate, simulating vehicle driving conditions. For heavy vehicle testing needs, the chassis dynamometer 2 can also include a hydraulic loading module. This module, through parallel hydraulic brakes, can further expand the load capacity range, thereby achieving a wider range of dynamic load simulation.

[0032] The chassis dynamometer 2 can convert the collected route data into a time-resistance curve and send torque commands to the servo motor module via the CAN bus (the control cycle can be 1ms). Based on the current feedback of the servo motor module and the vehicle wheel speed signal, a PID algorithm is used to compensate for the loss of the transmission system and achieve dynamic compensation.

[0033] For example, when the vehicle under test travels at 80 km / h on an asphalt road with a 5% gradient, the driving resistance is 3620 N, and the chassis dynamometer 2 needs to apply an equivalent torque of 1086 Nm.

[0034] The chassis dynamometer 2 can accurately calculate the driving resistance of the tested vehicle at different speeds based on route data and vehicle parameters (such as mass, tire specifications, etc.), and apply corresponding loads through motors or hydraulic devices to make the tested vehicle feel resistance changes similar to those on real roads.

[0035] In this embodiment, the vehicle's inertia can be calculated based on the equivalent formula for rotational inertia. The simulation. The equivalent formula for the moment of inertia can be expressed as: in, This indicates the mass (kg) of the vehicle being tested. This indicates the tire radius of the vehicle being tested.

[0036] The chassis dynamometer 2 can achieve equivalent simulation of vehicle inertia through dynamic torque control. Based on the equivalent formula of rotational inertia, the servo motor module outputs dynamic driving torque in real time, and combined with a closed-loop control algorithm, it performs high-precision compensation for the inertial force during vehicle acceleration / deceleration. For example, when simulating complex road conditions such as uphill and downhill slopes, by adjusting the amplitude and direction of the output torque of the servo motor, the longitudinal dynamic characteristics of the vehicle under test can be accurately reproduced, ensuring that the dynamic response of the vehicle under test in the simulated environment based on the test roller is consistent with the driving characteristics on real roads.

[0037] The chassis dynamometer 2 includes a dual closed-loop control architecture consisting of an inner loop and an outer loop. The inner loop (torque control) achieves accurate torque tracking through motor current feedback, while the outer loop (vehicle speed control) adjusts the target torque based on the vehicle wheel speed sensor signal using a fuzzy PID algorithm.

[0038] Through hardware-level triggering (PTP protocol), the speed of the chassis dynamometer 2 can be synchronized with the speed of the vehicle in the virtual scene, regardless of whether the vehicle under test is traveling at a constant speed or accelerating or decelerating. When wheel slippage is detected (such as a sudden change in wheel speed >20%), it automatically switches to slip ratio control mode.

[0039] The panoramic display 3 is used to dynamically display the road scene when the vehicle under test is driving, and the road scene includes the environmental information and background vehicles.

[0040] The panoramic display 3 can simulate various types of real-world road scenarios, including urban roads, highways, and rural paths. It can also display real-time traffic signal changes and the driving status of background vehicles, making the tested road scenarios more realistic. Through high-precision 3D modeling and texture mapping technology, the simulated road scenarios are highly similar to the real-world environments corresponding to the tested roads.

[0041] The control device 4 is used to acquire target environment information that matches the road mileage traveled by the tested vehicle based on the real-time vehicle speed; and to dynamically generate target simulation information corresponding to the background vehicle based on the driving state and the target environment information, wherein the target simulation information includes driving mode, vehicle type and number of vehicles.

[0042] The control device 4 is also used to generate a scene update command based on the target environment information and the target simulation information, so that the panoramic display 3 updates the current road scene to the target road scene.

[0043] The actual data acquisition device 1, through the fusion of LiDAR, binocular cameras, and high-precision GPS / IMU, can construct digital twin route data with centimeter-level accuracy, covering real road features. The control device 4, based on real-time vehicle speed and road mileage, dynamically calls up environmental information of the target road segment and generates simulated behaviors of background vehicles (such as lane changing and sudden braking).

[0044] Through the coordinated control of the chassis dynamometer 2, control device 4, and panoramic display 3, the physical conditions and dynamic traffic environment of the actual test road can be accurately replicated in the laboratory, enabling full-condition testing of vehicles in complex scenarios. This achieves efficient acquisition and analysis of multi-source data, significantly improving testing efficiency and safety, meeting the diversified needs of vehicle performance testing, and promoting the continuous development and innovation of the automotive industry.

[0045] In this embodiment, the simulation test system further includes a data acquisition and processing module; the acquisition and processing module is used to acquire vehicle operation information in real time and perform in-depth analysis and processing on the vehicle operation information to obtain target information. The vehicle operation information includes the driving status, the output data of the chassis dynamometer 2, and the road scene. The output data includes torque, speed, and power. The target information includes the energy consumption, environmental performance, acceleration performance, or braking performance of the vehicle under test. The acquisition and processing module is also used to generate a performance test analysis report of the vehicle under test based on the target information.

[0046] The data acquisition and processing module collects the driving status of the vehicle under test in real time via the vehicle's CAN bus. This driving status can also include acceleration, gear position, and accelerator / brake pedal opening. The chassis dynamometer 2 can output torque, speed, and power to the data acquisition and processing module via an Ethernet / PXI interface. The data acquisition and processing module receives the road scene from the panoramic display 3, including environmental information and background vehicles.

[0047] In the testing using Chassis Dynamometer 2, torque refers to the simulated resistance torque applied by the dynamometer to the vehicle's drive wheels, used to reproduce the resistance experienced by the vehicle when driving on real roads (such as rolling resistance, air resistance, and gradient resistance). Closed-loop feedback control of the motor's output torque in Chassis Dynamometer 2 ensures that the vehicle's dynamic characteristics are consistent with real road conditions. In the testing using Chassis Dynamometer 2, rotational speed reflects the real-time rotational state of the vehicle's drive wheels and is directly correlated to vehicle speed simulation. Power refers to the mechanical power absorbed by Chassis Dynamometer 2, used to evaluate the vehicle's power output or energy consumption.

[0048] The data acquisition and processing module can be a high-concurrency processing architecture. The high-concurrency processing architecture adopts the Time-Sensitive Networking (TSN) protocol to ensure the time synchronization of multiple data streams (e.g., jitter <1ms) and uses a circular buffer (capacity can be 1GB) to cope with data peaks.

[0049] The data acquisition and processing module performs in-depth analysis of the collected vehicle operation information to extract valuable target information. The analyzed target information can be presented to the driver in the form of a quantitative report. The performance test analysis report can intuitively show the performance of the tested vehicle in a simulated environment, providing strong support for the performance optimization of the tested vehicle and the verification of the intelligent driving system.

[0050] In this embodiment, the vehicle operation information is subjected to in-depth analysis and processing to obtain target information, which may specifically include: The initial vehicle operation information is spatiotemporally aligned using a Generative Adversarial Network (GAN) to align the timestamps and spatial coordinates of multimodal data, addressing the issue of frame rate discrepancies among various sensors. For example, a 10Hz point cloud from a LiDAR sensor is aligned with a 30Hz image from a camera to generate synchronized multimodal vehicle operation information. Based on this synchronized multimodal vehicle operation information, key features and performance indicators are calculated. Key features may include instantaneous power, total energy consumption integral, and environmental indicators. Environmental indicators can be calculated for CO2 emissions from fuel-powered vehicles. Performance indicators include acceleration performance and braking performance. Acceleration performance is calculated using the integral acceleration curve to measure the time from 0 to 100 km / h, and braking performance is calculated using the integral deceleration curve to measure the braking distance from 100 to 0 km / h. Based on these key features and performance indicators, target information is obtained.

[0051] For example, the total energy consumption credit can be used to assess the fuel economy or electricity consumption of the tested vehicle; environmental indicators can be used to assess the environmental performance of the tested vehicle; and performance indicators can be used to assess the acceleration performance, braking performance, etc. of the tested vehicle.

[0052] In this embodiment, the data acquisition and processing module has a built-in edge computing unit. The edge computing unit is used to identify the vehicle operation information based on a machine learning model, obtain the driving behavior pattern of the driver corresponding to the tested vehicle, and obtain the safety risk assessment information corresponding to the tested vehicle based on the driving behavior pattern. The driving behavior pattern includes driving habits or driving style. The edge computing unit is also used to generate a multi-dimensional test report based on the driving behavior pattern, the safety risk assessment information, and the performance test analysis report.

[0053] Edge computing units can extract driving behavior features from vehicle operation information based on machine learning models, and obtain driving behavior patterns based on these features. Driving behavior features can include rapid acceleration / deceleration, steering characteristics, and following habits. Rapid acceleration / deceleration is used to count the percentage of time that acceleration exceeds a threshold to capture aggressive driving habits. Steering characteristics are used to calculate the fluctuation frequency (Hz) and maximum angular rate of the steering wheel angle to identify frequent lane changes or emergency avoidance behaviors. Following habits are used to determine whether the driver is maintaining a safe distance by using the time to head-over-the-vehicle distance (THW) and minimum following distance.

[0054] Machine learning models can be either K-means clustering models or random forest classification models. K-means clustering is an unsupervised learning method that can categorize drivers into three driving behavior patterns: conservative, moderate, or aggressive. Random forest classification models are supervised models trained based on clustering results, annotating driving styles in real time and outputting driving behavior patterns (e.g., "aggressive") for subsequent risk assessment.

[0055] The edge computing unit also includes a risk assessment model. Driving behavior patterns directly influence the weighting and risk thresholds of this model. The risk assessment model can include rapid acceleration / deceleration, steering fluctuations, and following distance. Different driving behavior patterns correspond to different risk coefficient weights, and a risk score R can be calculated based on these weights. For example, in an aggressive driving behavior pattern, the corresponding weight for rapid acceleration / deceleration could be 0.5, the weight for steering fluctuations could be 0.3, and the weight for following distance could be 0.2; in a conservative driving behavior pattern, the corresponding weights for rapid acceleration / deceleration could be 0.3, steering fluctuations could be 0.2, and following distance could be 0.5.

[0056] Different driving behavior patterns can trigger different levels of risk warnings. For example, when the driving behavior pattern is aggressive, the corresponding high-risk threshold is R greater than 60, and the corresponding medium-risk threshold is R greater than 40 and not greater than 60; when the driving behavior pattern is conservative, the corresponding high-risk threshold is R greater than 75, and the corresponding medium-risk threshold is R greater than 50 and not greater than 75.

[0057] Safety risk assessment information is a comparison between the risk score and the corresponding risk thresholds for different levels. The multidimensional test report can include performance analysis content from the performance test analysis report, driving style radar charts, and risk assessments. The output formats of the multidimensional test report can include interactive PDFs and API interfaces. Interactive PDFs allow users to click on charts to view raw data, while API interfaces can push JSON data to the cloud for big data analysis.

[0058] The data acquisition and processing module can output quantifiable test conclusions more quickly, providing immediate feedback for vehicle optimization. Meanwhile, the generation of multi-dimensional test reports provides drivers with a more comprehensive and in-depth analysis of test results, avoiding the upload delays of massive amounts of raw data and rapidly outputting quantifiable test conclusions.

[0059] The edge computing unit can also achieve millisecond-level time synchronization of vehicle operation information, ensuring that the collected vehicle operation information is highly consistent with the actual driving status of the vehicle under test, which facilitates vehicle performance analysis and intelligent driving system verification.

[0060] In this embodiment, the panoramic display 3 includes a surround LED display array, a viewpoint tracking sensor, and a scene rendering engine; the surround LED display array is composed of multiple LED screens spliced ​​together to form the visual display area corresponding to the panoramic display 3; the viewpoint tracking sensor is used to track the driver's head position and direction in real time to adjust the display angle and display range of the panoramic display 3; the scene rendering engine is used to render the target road scene in real time.

[0061] The panoramic display 3 uses multi-screen splicing and viewpoint tracking technology to provide drivers with immersive visual feedback and precise matching of physical motion.

[0062] The panoramic display 3 uses a surround LED display array, which is made up of multiple LED screens spliced ​​together to form a large visual display area. It can eliminate the screen splicing traces of traditional simulators and provide a more coherent and realistic visual experience.

[0063] To keep pace with the driver's head movements, the Panoramic Display 3 is also equipped with a viewpoint tracking sensor. This sensor tracks the driver's head position and direction in real time, adjusting the viewing angle and range of the displayed image accordingly to ensure the driver always sees the correct content. The Panoramic Display 3 also features a built-in high-efficiency scene rendering engine, capable of rendering high-quality images in real time, allowing the target road scene to be presented realistically.

[0064] The panoramic display 3 provides a more immersive and realistic visual feedback, giving drivers a sensory experience consistent with real driving. It eliminates the image lag and perspective distortion problems of traditional simulators, allowing drivers to have a sensory experience consistent with real driving.

[0065] The viewpoint tracking sensor can be an infrared camera array, which can be deployed on the top of the cockpit or the edge of the display screen. It actively emits infrared light and receives reflected signals to capture facial feature points of the driver (such as eyelids, bridge of the nose, and auricles). By fusing the driver's position and orientation data using a Kalman filter algorithm, the system can output 6 degrees of freedom (6DoF) parameters for the head. These parameters include the head's coordinates (x, y, z) in three-dimensional space and the head's rotation angles (pitch, yaw, and roll).

[0066] For example, the horizontal viewing angle can be adjusted based on the head's yaw angle (e.g., expanding from ±60° to ±120°). The vertical viewing angle can be adjusted based on the head's pitch angle (e.g., displaying more sky when looking up, and more dashboard when looking down).

[0067] In this embodiment, the panoramic display 3 can support dynamic switching of multiple perspectives, including front view, rear view and side view, and adjust the scene rendering accuracy and refresh rate in real time according to the driving status of the vehicle under test to ensure that the road scene and vehicle movement are highly synchronized.

[0068] The surround LED display array can be composed of 6 curved LED screens (front, left front, right front, left rear, right rear, and rear), with a curvature radius of 5m, a horizontal field of view of 220°, and a vertical field of view of 60°.

[0069] For example, the forward-looking view is the default mode, covering a 140° field of view directly in front of the driver, used for scenarios such as driving straight and following other vehicles; the triggering conditions for the side-looking view (left / right) include: steering wheel angle >15° or yaw rate >10° / s (such as changing lanes or turning); the triggering conditions for the rear-looking view include: reversing signal activation.

[0070] In this embodiment, based on the vehicle's motion trend (such as the rate of change of steering angle), the 3D model resources of the target viewpoint can be preloaded into the video memory. When switching viewpoints, a 0.2-second gradual blending (Alpha blending) can be used to avoid abrupt screen transitions. For example, when the vehicle turns right, the right front screen drops from 8K to 4K, while the right rear screen increases from 4K to 8K, and the main viewpoint smoothly transitions to the right front.

[0071] There can be a mapping relationship between refresh rate and real-time vehicle speed. For example, when the real-time vehicle speed is 100 km / h, the refresh rate can be 120 Hz. The DisplayPort 2.1 Adaptive-Sync protocol ensures that the screen refresh is aligned with the vehicle's CAN bus signal timestamp.

[0072] In this embodiment, the chassis dynamometer 2 and the panoramic display 3 can achieve coordinated control through the hardware-in-the-loop (HIL) protocol, ensuring a high degree of consistency between the vehicle dynamics response and visual / scene feedback. This can avoid test deviations caused by system delays and improve data reliability under complex working conditions.

[0073] During simulated driving, the chassis dynamometer 2 and the panoramic display 3 can adjust and optimize their output signals in real time to adapt to the actual driving conditions of the vehicle. For example, when the vehicle accelerates, the chassis dynamometer 2 increases the torque output, while the panoramic display 3 updates the screen content to reflect the visual changes during acceleration.

[0074] The overall performance and testing accuracy of the real-road driving simulation testing system have been significantly improved, providing stronger support for vehicle performance optimization and intelligent driving system verification. It avoids testing deviations caused by system latency and enhances data reliability under complex operating conditions.

[0075] When the output torque of the chassis dynamometer 2 changes, the scene rendering engine simultaneously generates special effects such as tire slippage and vehicle pitch. When there is a sudden change in torque (such as rapid acceleration), a motion blur is triggered on the screen, and the blur intensity can be proportional to the acceleration.

[0076] In this embodiment, the control device 4 includes a traffic flow simulation module; the traffic flow simulation module is used to obtain a traffic flow model based on the actual test requirements corresponding to the vehicle under test, so that the traffic flow model generates initial background vehicle behavior based on the traffic flow data of the measured road segment; it is also used to adjust the initial background vehicle behavior in real time based on the driving state and the target environment information to obtain the target simulation information.

[0077] The control device 4 incorporates multiple traffic flow models, allowing users to select the appropriate model for simulation based on actual needs. These traffic flow models can generate initial background vehicle behavior that conforms to real-world statistical patterns, including background vehicle speed distribution, vehicle type ratios, and traffic density.

[0078] Specifically, the traffic flow model can generate a background vehicle speed distribution that conforms to real statistical laws based on the traffic flow data of the measured road segment. The background vehicle speed distribution follows a normal distribution, and the mean matches the vehicle speed in the traffic flow data of the measured road segment, ensuring that the vehicle speed changes in the simulated road scenario are consistent with the real road conditions. The traffic flow model can also adjust the proportion of vehicle types in the background based on the measured traffic flow data. For example, the proportion of different types of vehicles such as cars and trucks in the graph will be consistent with the measured traffic flow data, making the simulated road scene more realistic and credible. To simulate changes in road conditions at different times, the traffic flow model can also dynamically adjust traffic density according to peak / off-peak hours. For example, during peak hours, the number of background vehicles increases, and the traffic density increases; during off-peak hours, the number of background vehicles decreases, and the traffic density decreases.

[0079] By generating initial background vehicle behavior, traffic flow under real-world road conditions can be simulated more accurately, improving the realism and credibility of the tested road scenarios. The data-driven traffic flow simulation module ensures a high degree of consistency between the tested road scenarios and actual road conditions, further enhancing the realism and credibility of the test results.

[0080] Building upon the initial background vehicle behavior, control device 4 also possesses intelligent behavior generation capabilities. Control device 4 can adjust the initial background vehicle behavior in real time based on the driving status of the vehicle under test and the currently changed target environment information, making the tested road scenario more realistic and believable.

[0081] Road signs may include speed limit signs, construction signs, and lane line types (solid lines / dashed lines); traffic light positions and statuses may include traffic light countdowns and intersection turn signal statuses; intersection distribution may include intersection types (cross / T) and lane connection relationships; roadside scenes may include pedestrians crossing streets, parking areas, and obstacles (such as traffic cones).

[0082] In this embodiment, the initial background vehicle behavior is adjusted in real time based on the driving state and the target environment information, which may specifically include: Driving resistance affects the acceleration of the tested vehicle, and thus the safe following distance of the background vehicle is adjusted through the IDM model to avoid following too far; the generation frequency of the background vehicle is adjusted according to the real-time vehicle speed and vehicle inertia. When the red light countdown t < 5 seconds is detected, the vehicles in the lateral background initiate pre-braking; Based on lane connections, assign turning probabilities to background vehicles (30% for left turn, 50% for straight, and 20% for right turn).

[0083] Determine intersection traffic priority through game theory models (such as Nash equilibrium) to avoid deadlock; Adjust the average speed distribution of background vehicles to the speed limit value according to the speed limit sign; According to the construction signs, the lane is closed. Background vehicles should initiate lane changes (steering angle rate > 10° / s) 200 meters away and detour around the closed area. If there are traffic cones on the side of the road, vehicles in the background should be shifted laterally by 0.5 meters to maintain a safe distance.

[0084] In this embodiment, the control device 4 further includes an accident scene generation module; the accident scene generation module is used to generate an accident scene based on a scene triggering mode, and update the accident scene to the target road scene. The scene triggering mode is a random triggering mode or a controllable triggering mode. The accident scene includes pedestrians crossing the road, emergency braking of vehicles ahead, vehicle breakdown and stopping, traffic accidents, or severe weather. If the scene triggering mode is a random triggering mode, then the unexpected scene generation module is used to randomly generate the unexpected scene based on a preset probability; If the scene triggering mode is a controllable triggering mode, then the unexpected scene generation module is used to generate the preset unexpected scene when the preset triggering conditions are met.

[0085] The random trigger mode can generate unexpected scenarios randomly according to a preset probability; the controllable trigger mode generates a preset unexpected scenario when preset trigger conditions are met. For example, the preset trigger conditions can be a specific time point.

[0086] When the scene triggering mode is set to random triggering mode, unexpected scenarios such as pedestrians crossing the road or vehicles breaking down and stopping will suddenly occur. The generation of these unexpected scenarios can increase the challenge and realism of the driving simulation.

[0087] When the scene triggering mode is controlled, the target scene, i.e., the preset unexpected scene, is generated at a specific time point, such as simulating a specific type of traffic accident or emergency. This controlled triggering method allows for repeated verification of specific functions, improving the targeting of the test.

[0088] Real-world driving simulation testing systems can more flexibly simulate various unexpected risk situations, providing stronger support for the verification of intelligent driving systems. Through a flexible scenario generation mechanism, it can simulate unpredictable sudden risks and also perform repeated verification for specific functions, balancing the comprehensiveness and specificity of the test.

[0089] In this embodiment, the actual road driving simulation test system also includes a user-defined test module, which allows drivers to import actual road data, set virtual traffic flow parameters, and superimpose special environmental factors to generate customized test road scenarios.

[0090] The user-defined test module allows drivers to import actual road data, which may include route data such as road geometry, pavement material, and traffic signs.

[0091] The user-defined testing module also allows drivers to set virtual traffic flow parameters. These parameters include, but are not limited to, vehicle type, number of vehicles, speed distribution, traffic density, and vehicle behavior patterns. By flexibly adjusting these parameters, drivers can simulate various complex traffic scenarios, such as peak-hour congestion, traffic flow changes on highways, or pedestrian crossings on urban roads.

[0092] The user-defined testing module also has the function of generating customized test reports. After the test is completed, the user-defined testing module can automatically collect and analyze test data, including key indicators such as vehicle trajectory, speed change, acceleration, and braking distance.

[0093] Based on test data, the user-defined testing module can generate detailed test reports to help drivers fully understand the vehicle's test performance. The test reports not only include objective test data but can also incorporate subjective evaluations and analyses based on the driver's needs. For example, drivers can evaluate the vehicle's driving feel, comfort, and handling, and provide suggestions for future improvements.

[0094] The user-defined testing module provides robust support for vehicle testing and development through features such as importing real-world road data, setting virtual traffic flow parameters, and generating customized test reports. This allows drivers to recreate real-world traffic conditions in a virtual environment, enabling a comprehensive evaluation of the vehicle's driving system, safety performance, and fuel economy. Furthermore, by simulating different road scenarios, drivers can identify potential problems or deficiencies in the tested vehicle, providing strong support for subsequent improvements and optimizations. This meets the personalized needs of different vehicle models and testing objectives, expanding the system's applicability and technological value.

[0095] Based on the same technical concept, embodiments of this application provide a method for simulating driving on actual roads. This method can be executed by a device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet computer, desktop computer, etc., but is not limited to these.

[0096] like Figure 2 As shown, a method for simulating driving on actual roads, using electronic devices as the execution subject, is described in its main process flow as follows (steps S101 to S103): Step S101: Based on the real-time speed of the vehicle under test, obtain target environment information that matches the road mileage traveled by the vehicle under test. The target environment information includes road signs, traffic light locations, intersection distribution, and roadside scenes. Step S102: Based on the driving status and the target environment information, dynamically generate target simulation information corresponding to the background vehicle. The target simulation information includes driving mode, vehicle type and number of vehicles. The driving status includes driving resistance, vehicle inertia and real-time vehicle speed simulated by the chassis dynamometer based on route data. Step S103: Based on the target environment information and the target simulation information, generate a scene update command and send it to the panoramic display so that the panoramic display updates the current road scene to the target road scene.

[0097] In this embodiment, the method further includes: Based on the scene triggering mode, an unexpected scene is generated and the unexpected scene is updated to the target road scene. The scene triggering mode is either a random triggering mode or a controllable triggering mode. The unexpected scenes include pedestrians crossing the road, vehicles in front braking suddenly, vehicles breaking down and stopping, traffic accidents, or severe weather. If the scene triggering mode is random triggering mode, then unexpected scenes will be randomly generated based on preset probabilities; If the scene triggering mode is a controllable triggering mode, the unexpected scene generation module is used to generate a preset unexpected scene when the preset triggering conditions are met.

[0098] Based on the same technical concept, this application also provides a real-road driving simulation testing device, such as... Figure 3 As shown, the actual road driving simulation test device 200 mainly includes: The environment acquisition module 201 is used to acquire target environment information that matches the road mileage traveled by the test vehicle based on the real-time vehicle speed when the test vehicle is driving. The target environment information includes road signs, traffic light positions, intersection distribution and roadside scenes. Background vehicle simulation module 202 is used to dynamically generate target simulation information corresponding to the background vehicle based on the driving status and the target environment information. The target simulation information includes driving mode, vehicle type and number of vehicles. The driving status includes driving resistance, vehicle inertia and real-time vehicle speed simulated by the chassis dynamometer based on route data. The scene update module 203 is used to generate a scene update instruction based on the target environment information and the target simulation information, and send it to the panoramic display so that the panoramic display updates the current road scene to the target road scene.

[0099] Optionally, the device further includes: An accident generation module is used to generate accident scenarios based on a scene triggering mode and update the accident scenarios to the target road scene. The scene triggering mode is either a random triggering mode or a controllable triggering mode. Accident scenarios include pedestrians crossing the road, vehicles in front braking suddenly, vehicles breaking down and stopping, traffic accidents, or severe weather. If the scene triggering mode is a random triggering mode, the accident scenario is randomly generated based on a preset probability. If the scene triggering mode is a controllable triggering mode, the accident scenario generation module is used to generate a preset accident scenario when preset triggering conditions are met.

[0100] In one example, the module in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0101] For example, when modules in a device can be implemented via a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0102] In this application, various objects such as messages / information / devices / network elements / systems / apparatus / actions / operations / processes / concepts may be named. It is understood that these specific names do not constitute a limitation on the relevant objects. The names may be changed depending on the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from their functions and technical effects embodied / performed in the technical solution.

[0103] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0104] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0105] Based on the same technical concept, this application also provides an electronic device, such as... Figure 4 As shown, the electronic device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0106] The processor 301 controls the overall operation of the electronic device 300 to complete all or part of the steps in the above-described actual road driving simulation test method. The memory 302 stores various types of data to support the operation of the electronic device 300. This data may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0107] I / O interface 303 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 304 is used to test wired or wireless communication between electronic device 300 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 304 may include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0108] The communication bus 305 may include a path for transmitting information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 can be divided into an address bus, a data bus, a control bus, etc.

[0109] The electronic device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the actual road driving simulation test method given in the above embodiments.

[0110] Electronic device 300 may include, but is not limited to, mobile terminals such as digital broadcast receivers, PDAs (personal digital assistants), and PMPs (portable multimedia players), as well as fixed terminals such as digital TVs and desktop computers, and may also be servers.

[0111] Based on the same technical concept, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described actual road driving simulation test method.

[0112] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0114] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0115] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0116] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A real-road driving simulation testing system, characterized in that, Includes actual data acquisition device, chassis dynamometer, panoramic display and control device; The actual data acquisition device is used to acquire route data of the measured road section. The route data includes road mileage, elevation changes and environmental information. The environmental information includes road signs, traffic light locations, intersection distribution and roadside scenes. The chassis dynamometer is used to simulate the driving state of the vehicle under test based on the route data. The driving state includes driving resistance, vehicle inertia and real-time vehicle speed. The panoramic display is used to dynamically display the road scene when the vehicle under test is driving, and the road scene includes the environmental information and background vehicles; The control device is used to acquire target environment information matching the road mileage traveled by the tested vehicle based on the real-time vehicle speed; and to dynamically generate target simulation information corresponding to the background vehicle based on the driving state and the target environment information, wherein the target simulation information includes driving mode, vehicle type and number of vehicles. The control device is further configured to generate a scene update command based on the target environment information and the target simulation information, so that the panoramic display updates the current road scene to the target road scene.

2. The actual road driving simulation test system according to claim 1, characterized in that, The simulation testing system also includes a data acquisition and processing module; The acquisition and processing module is used to acquire vehicle operation information in real time and perform in-depth analysis and processing on the vehicle operation information to obtain target information. The vehicle operation information includes the driving status, the output data of the chassis dynamometer, and the road scene. The output data includes torque, speed, and power. The target information includes the energy consumption, environmental performance, acceleration performance, or braking performance of the vehicle under test. The data acquisition and processing module is also used to generate a performance test analysis report of the vehicle under test based on the target information.

3. The actual road driving simulation test system according to claim 2, characterized in that, The data acquisition and processing module has a built-in edge computing unit; The edge computing unit is used to identify the vehicle operation information based on a machine learning model, obtain the driving behavior pattern of the driver corresponding to the tested vehicle, and obtain the safety risk assessment information corresponding to the tested vehicle based on the driving behavior pattern. The driving behavior pattern includes driving habits or driving style. The edge computing unit is also used to generate a multi-dimensional test report based on the driving behavior pattern, the safety risk assessment information, and the performance test analysis report.

4. A real-road driving simulation testing system according to any one of claims 1 to 3, characterized in that, The control device includes a traffic flow simulation module; The traffic flow simulation module is used to obtain a traffic flow model based on the actual test requirements corresponding to the vehicle under test, so that the traffic flow model generates initial background vehicle behavior based on the traffic flow data of the measured road segment; it is also used to adjust the initial background vehicle behavior in real time based on the driving state and the target environment information to obtain the target simulation information.

5. The actual road driving simulation test system according to claim 4, characterized in that, The control device also includes an unexpected scenario generation module; The unexpected scene generation module is used to generate unexpected scenes based on the scene triggering mode and update the unexpected scenes to the target road scene. The scene triggering mode is a random triggering mode or a controllable triggering mode. The unexpected scenes include pedestrians crossing the road, vehicles in front braking suddenly, vehicles breaking down and stopping, traffic accidents, or severe weather. If the scene triggering mode is a random triggering mode, then the unexpected scene generation module is used to randomly generate the unexpected scene based on a preset probability; If the scene triggering mode is a controllable triggering mode, then the unexpected scene generation module is used to generate the preset unexpected scene when the preset triggering conditions are met.

6. The actual road driving simulation test system according to claim 1, characterized in that, The panoramic display includes a surround LED display array, a viewpoint tracking sensor, and a scene rendering engine; The surround LED display array is composed of multiple LED screens spliced ​​together to form the visual display area corresponding to the panoramic display. The viewpoint tracking sensor is used to track the driver's head position and direction in real time in order to adjust the viewing angle and display range of the panoramic display. The scene rendering engine is used to render the target road scene in real time.

7. A method for simulating driving on actual roads, characterized in that, Applied to control devices, including: Based on the real-time speed of the vehicle under test, target environment information matching the road mileage traveled by the vehicle under test is obtained. The target environment information includes road signs, traffic light locations, intersection distribution, and roadside scenes. Based on the driving status and the target environment information, target simulation information corresponding to the background vehicle is dynamically generated. The target simulation information includes driving mode, vehicle type and number of vehicles. The driving status includes driving resistance, vehicle inertia and real-time vehicle speed simulated by the chassis dynamometer based on route data. Based on the target environment information and the target simulation information, a scene update command is generated and sent to the panoramic display so that the panoramic display updates the current road scene to the target road scene.

8. A real-road driving simulation testing device, characterized in that, include: The environment acquisition module is used to acquire target environment information that matches the road mileage traveled by the test vehicle based on the real-time vehicle speed. The target environment information includes road signs, traffic light locations, intersection distribution, and roadside scenes. The background vehicle simulation module is used to dynamically generate target simulation information corresponding to the background vehicle based on the driving status and the target environment information. The target simulation information includes driving mode, vehicle type and number of vehicles. The driving status includes driving resistance, vehicle inertia and real-time vehicle speed simulated by the chassis dynamometer based on route data. The scene update module is used to generate a scene update command based on the target environment information and the target simulation information, and send it to the panoramic display so that the panoramic display updates the current road scene to the target road scene.

9. An electronic device, characterized in that, It includes a processor and a memory, wherein the processor is coupled to the memory; The processor is configured to execute a computer program stored in the memory, so that the electronic device performs the method as described in claim 7.

10. A computer-readable storage medium, characterized in that, It includes a computer program or instructions that, when run on a computer, cause the computer to perform the method as described in claim 7.

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