A multi-domain signal coordination networked intelligent driving test method and related equipment

Abstract

The application provides a networked intelligent driving test method based on multi-domain signal cooperation and related equipment. The method comprises the following steps: acquiring real-time pose information of a networked intelligent driving vehicle to be tested, and generating a virtual traffic scene containing multiple virtual networked vehicles; generating GNSS simulation signals and V2X radio frequency signals; determining GNSS attenuation parameters and V2X attenuation parameters based on the same set of environmental feature information and real-time pose information, and performing attenuation processing respectively; under the same test time reference, outputting the GNSS simulation signals after attenuation processing to the networked intelligent driving vehicle to be tested, and injecting the V2X radio frequency signals after attenuation processing into an on-board unit (OBU); collecting test data and generating a test evaluation result. The application can construct a test scene participated by multiple virtual networked vehicles under the condition of a single real vehicle, and improve the physical consistency of signal degradation.

Description

Technical Field

[0001] This application belongs to the field of connected intelligent driving testing technology, specifically relating to a multi-domain signal coordination connected intelligent driving testing method and related equipment. Background Technology

[0002] Connected intelligent driving vehicles typically rely on both GPS positioning information and vehicular wireless communication information for environmental perception, collaborative decision-making, and driving control. Testing connected intelligent driving functions requires placing the vehicle under test in a controllable traffic scenario and acquiring its functional responses and driving behaviors under different positioning conditions, communication conditions, and traffic participant states.

[0003] Existing testing methods typically include closed-track testing with real vehicles, simulation testing, and virtual-real fusion testing. Closed-track testing with real vehicles can reflect the actual operating state of vehicles, but it is difficult to construct large-scale connected traffic scenarios due to limitations in the number of test vehicles, site conditions, and testing costs. Simulation testing is convenient for generating complex traffic scenarios, but the participation of the actual positioning receiving link and the vehicle-to-everything (V2X) wireless communication receiving link of the tested vehicle is insufficient, and the test results are prone to deviation from the actual operating state of the vehicle.

[0004] In virtual-real fusion testing, existing solutions often simulate GNSS positioning signals and V2X communication signals separately, or only provide virtual traffic participant information to the vehicle under test at the message layer. Because GNSS positioning signals and V2X radio frequency signals lack coordinated application under the same test time reference, and the degradation processes of the two types of signals lack the same environmental characteristic constraints, the vehicle positioning information received by the vehicle under test and the virtual connected vehicle motion state information are easily inconsistent in time, space, or attenuation environment, thus affecting the authenticity and repeatability of the test evaluation results. Summary of the Invention

[0005] The purpose of this application is to provide a multi-domain signal coordination test method and related equipment for connected intelligent driving, so as to overcome the technical problems in the prior art where GNSS positioning signals and V2X communication signals are simulated independently, lacking coordinated application under the same test time reference and attenuation processing driven by the same set of environmental feature information, resulting in insufficient physical consistency of multi-domain signal degradation and difficulty in carrying out large-scale multi-domain signal coordination tests in connected traffic scenarios under single vehicle conditions.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides a testing method for connected intelligent driving with multi-domain signal coordination, including: The system acquires the real-time pose information of the connected intelligent driving vehicle under test in the test field, and generates a virtual traffic scene containing multiple virtual connected vehicles based on the real-time pose information. The system also determines the environmental feature information and motion state information of each virtual connected vehicle corresponding to the virtual traffic scene. Based on the real-time pose information, a Global Navigation Satellite System (GNSS) simulated signal is generated, and the motion state information is converted into a Vehicle-to-Everything (V2X) radio frequency signal; wherein, the output power of the GNSS simulated signal is set to be higher than the power of the real satellite signal over the test site; Using the same set of environmental feature information as the driving source, a multi-domain signal collaborative attenuation model is used to determine GNSS attenuation parameters based on the environmental feature information and the real-time pose information, and V2X attenuation parameters based on the environmental feature information, the real-time pose information and the motion state information. The GNSS analog signal and the V2X radio frequency signal are attenuated according to the GNSS attenuation parameters and the V2X attenuation parameters, respectively. Under the same test time reference, the attenuated GNSS analog signal is output to the connected intelligent driving vehicle under test, and the attenuated V2X radio frequency signal is injected into the on-board unit (OBU) of the connected intelligent driving vehicle under test. Test data of the tested connected intelligent driving vehicle under attenuation processing of GNSS analog signal and V2X radio frequency signal are collected, and test evaluation results are generated.

[0007] In one embodiment, generating a Global Navigation Satellite System (GNSS) analog signal based on the real-time pose information includes: The carrier frequency and modulation scheme of the GNSS analog signal are set to be consistent with those of the real satellite signal; The output power of the GNSS analog signal is set according to a preset suppression margin, which corresponds to the locking condition of the GNSS receiver of the tested connected intelligent driving vehicle to the GNSS analog signal when the GNSS analog signal and the real satellite signal coexist. The satellite constellation configuration corresponding to the GNSS simulated signal is set according to the actual satellite constellation configuration of the test site's geographical location, and the ephemeris parameters of the GNSS simulated signal are adjusted based on the real-time pose information to generate the GNSS simulated signal.

[0008] In one embodiment, the step of outputting the attenuated GNSS analog signal to the connected intelligent driving vehicle under test under the same test time reference, and injecting the attenuated V2X radio frequency signal into the on-board unit (OBU) of the connected intelligent driving vehicle under test, includes: The scene simulator, GNSS signal simulator, and V2X signal generator establish the same test time reference through Precision Time Protocol (PTP) or GNSS timing. The motion state information of each virtual connected vehicle is calculated using the scene simulator at a first update cycle. The attenuated GNSS analog signal is output through the GNSS signal simulator in the second update cycle; The motion state information is converted into the V2X radio frequency signal by the V2X signal generator in a third update cycle; The attenuated V2X radio frequency signal is injected into the on-board unit (OBU). Based on the signal processing delays of the scenario simulator, the GNSS signal simulator, and the V2X signal generator, the first update cycle, the second update cycle, and the third update cycle are coordinated to ensure that the output of the GNSS analog signal and the injection of the V2X radio frequency signal are executed synchronously under the same test time reference.

[0009] In one embodiment, determining the GNSS attenuation parameters based on the environmental feature information and the real-time pose information includes: Based on the three-dimensional geometric model of the building in the environmental feature information and the real-time pose information, the signal propagation path from each simulated satellite to the tested connected intelligent driving vehicle is occlusion judged to determine the set of visible satellites and the set of occluded satellites. For the simulated satellites in the visible satellite set, the number, amplitude, delay, and phase of the reflection paths are calculated based on the three-dimensional geometric model of the building to obtain GNSS multipath parameters; Based on the set of visible satellites, the set of obscured satellites, and the GNSS multipath parameters, the carrier-to-noise ratio degradation value is determined. The visible satellite set, the obscured satellite set, the GNSS multipath parameter, and the carrier-to-noise ratio degradation value are used as the GNSS attenuation parameter.

[0010] In one embodiment, determining the V2X attenuation parameter based on the environmental feature information, the real-time pose information, and the motion state information includes: Based on the three-dimensional geometric model of the building in the environmental feature information, the V2X radio frequency signal propagation path from each virtual connected vehicle to the tested connected intelligent driving vehicle is blocked to determine the line-of-sight propagation path and the non-line-of-sight propagation path. The path loss is determined based on the line-of-sight propagation path and the non-line-of-sight propagation path, as well as the real-time relative distance between the tested connected intelligent driving vehicle and each virtual connected vehicle. The Doppler shift is determined based on the real-time relative speed between the tested connected intelligent driving vehicle and each virtual connected vehicle and the V2X carrier frequency; Based on the three-dimensional geometric model of the building, the number, amplitude, and time delay spread of the reflection paths are determined to obtain the V2X multipath parameters. The path loss, the Doppler frequency shift, and the V2X multipath parameter are used as the V2X attenuation parameter.

[0011] In one embodiment, the step of collecting test data of the tested connected intelligent driving vehicle under attenuation processing of GNSS analog signals and V2X radio frequency signals, and generating test evaluation results, includes: Collect signal quality data, functional response data, and driving behavior data, wherein the signal quality data includes GNSS positioning accuracy data and V2X communication quality data; The signal quality evaluation result is determined based on the signal quality data, the function response evaluation result is determined based on the function response data, and the driving safety evaluation result is determined based on the driving behavior data. A first mapping relationship is established between the signal quality evaluation result and the functional response evaluation result, and a second mapping relationship is established between the functional response evaluation result and the driving safety evaluation result.

[0012] In one embodiment, after generating the test evaluation results, the method further includes: The GNSS positioning accuracy degradation level and V2X communication quality degradation level are determined based on the signal quality evaluation results, and a multi-domain joint attenuation parameter space is constructed based on the GNSS positioning accuracy degradation level and the V2X communication quality degradation level. Multiple sets of parameter combinations are selected in the multi-domain joint attenuation parameter space. The corresponding GNSS attenuation parameter and V2X attenuation parameter are determined according to each set of parameter combinations. The GNSS analog signal and the V2X radio frequency signal are attenuated according to the corresponding GNSS attenuation parameter and V2X attenuation parameter. Under the same test time reference, the attenuated GNSS analog signal corresponding to each parameter combination is output to the connected intelligent driving vehicle under test, and the attenuated V2X radio frequency signal corresponding to each parameter combination is injected into the on-board unit (OBU) to collect the test data corresponding to each parameter combination. Based on the test data corresponding to each set of parameters, the multi-domain joint degradation critical parameter combination for the connected intelligent driving function of the tested connected intelligent driving vehicle to change from a normal state to a degraded state or a failure state is determined.

[0013] Secondly, this application provides a multi-domain signal coordination-based connected intelligent driving test system, comprising: The scene generation module is used to acquire the real-time pose information of the connected intelligent driving vehicle under test in the test field, and generate a virtual traffic scene containing multiple virtual connected vehicles based on the real-time pose information, and determine the environmental feature information and motion state information of each virtual connected vehicle corresponding to the virtual traffic scene. The signal generation module is used to generate a Global Navigation Satellite System (GNSS) analog signal based on the real-time pose information, and to convert the motion state information into a vehicle-to-everything (V2X) radio frequency signal. The attenuation processing module is used to determine GNSS attenuation parameters based on the environmental feature information and the real-time pose information through a multi-domain signal collaborative attenuation model, using the same set of environmental feature information as the driving source, and V2X attenuation parameters based on the environmental feature information, the real-time pose information and the motion state information, and to perform attenuation processing on the GNSS analog signal and the V2X radio frequency signal according to the GNSS attenuation parameters and the V2X attenuation parameters respectively. The signal coordination module is used to output the attenuated GNSS analog signal to the connected intelligent driving vehicle under test under the same test time reference, and to inject the attenuated V2X radio frequency signal into the on-board unit (OBU) of the connected intelligent driving vehicle under test; wherein, the output power of the GNSS analog signal is set to be higher than the power of the real satellite signal over the test field; The evaluation generation module is used to collect test data of the tested connected intelligent driving vehicle under attenuation processing of GNSS analog signals and V2X radio frequency signals, and generate test evaluation results.

[0014] Thirdly, this application provides a computer device, including a processor and a computer-readable storage medium; The processor is adapted to execute computer programs; The computer-readable storage medium stores a computer program, which, when executed by the processor, implements the multi-domain signal coordination networked intelligent driving test method as described in any of the first aspects.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the multi-domain signal coordination networked intelligent driving test method described in any one of the first aspects.

[0016] Compared with the prior art, this application has the following beneficial effects: Firstly, this application provides a multi-domain signal collaborative testing method for connected intelligent driving. This application generates a virtual traffic scene containing multiple virtual connected vehicles by acquiring the real-time pose information of the connected intelligent driving vehicle under test in a test field. Using the same set of environmental feature information as the driving source, a multi-domain signal collaborative attenuation model is used to determine GNSS attenuation parameters and V2X attenuation parameters based on environmental feature information, real-time pose information, and motion state information, respectively. Attenuation processing is applied to the GNSS analog signal and the V2X radio frequency signal. Then, under the same test time reference, the attenuated signals of both types are applied to the connected intelligent driving vehicle under test. This ensures that the positioning signal received by the vehicle under test and the communication signal of the virtual connected vehicle correspond to the attenuation conditions driven by the same test scene, the same test time reference, and the same set of environmental feature information, thereby improving the physical consistency of multi-domain signal degradation, the controllability of the test process, and the repeatability of the test evaluation results in connected intelligent driving testing.

[0017] Meanwhile, this application sets the output power of the GNSS simulated signal to be higher than that of the real satellite signal over the test site, enabling the connected intelligent driving vehicle under test to receive controlled GNSS simulated signals in the test site environment, reducing the impact of real satellite signals on the test input; and converts the motion state information of the virtual connected vehicle into V2X radio frequency signals and injects them into the on-board unit (OBU), so that the V2X receiving link of the vehicle under test can participate in the test. Thus, it is possible to construct a connected traffic test scenario involving multiple virtual connected vehicles with only one connected intelligent driving vehicle under test, reducing the dependence on the number of real vehicles and site conditions.

[0018] Secondly, this application provides a multi-domain signal coordination-based connected intelligent driving test system. This system implements the scene generation, signal generation, attenuation processing, signal coordination, and result evaluation processes in the aforementioned test method through a scene generation module, a signal generation module, an attenuation processing module, a signal coordination module, and an evaluation generation module. This enables the data processing and signal application links for connected intelligent driving tests to be implemented through corresponding modules, facilitating systematic deployment and stable execution.

[0019] Thirdly, this application provides a computer device. This computer device executes a computer program through a processor to implement the core processing flow of the aforementioned multi-domain signal coordination-based connected intelligent driving test method, enabling real-time pose processing, virtual traffic scene generation, attenuation parameter determination, test data acquisition, and test evaluation result generation to be completed within the computer device.

[0020] Fourthly, this application provides a computer-readable storage medium. This computer-readable storage medium is used to store a computer program that implements the above-described multi-domain signal coordination-based connected intelligent driving test method, enabling the method to be implemented through program loading and execution, facilitating its invocation and operation in a computer device. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope; for those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the multi-domain signal coordination testing method for connected intelligent driving in this application embodiment; Figure 2 This is a schematic diagram illustrating the process of determining GNSS attenuation parameters and V2X attenuation parameters in the embodiments of this application; Figure 3 This is a schematic diagram of the multi-domain joint attenuation parameter space in the embodiments of this application; Figure 4 This is a schematic diagram of the composition of the connected intelligent driving test system with multi-domain signal coordination in the embodiments of this application; Figure 5 This is a diagram showing the internal structure of a computer device in an embodiment of this application. Detailed Implementation

[0023] Connected intelligent driving tests need to consider both the positioning input and the vehicle's wireless communication input. In existing virtual-real fusion tests, positioning signals and communication signals are easily constructed separately, the correspondence between the virtual traffic scene and the actual operating state of the vehicle under test is not clear enough, and the attenuation processing of the two types of signals is also prone to being separated from the same test environment, affecting the authenticity and repeatability of the test data.

[0024] Based on the above background, this application generates a virtual traffic scene by acquiring the real-time pose information of the connected intelligent driving vehicle under test in the test field, and determines the environmental feature information and the motion state information of each virtual connected vehicle based on the same virtual traffic scene; then, it generates GNSS analog signals and V2X radio frequency signals respectively, and applies the two types of signals after attenuation processing to the connected intelligent driving vehicle under test under the same test time reference, so that the vehicle under test generates test data under controllable positioning input and controllable communication input.

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] To facilitate understanding of the English abbreviations used in the following embodiments, their meanings are explained uniformly below: Global Navigation Satellite System (GNSS) Vehicle-to-Everything (V2X) wireless communication; On-Board Unit (OBU) Precision Time Protocol (PTP) Basic Safety Message (BSM); Real-Time Kinematic (RTK) technology; Circular Error Probable (CEP) Received Signal Strength Indicator (RSSI); Collision time, TTC (Time To Collision); Time Headway (THW) Root Mean Square (RMS) Society of Automotive Engineers (SAE).

[0027] When the above-mentioned English abbreviations and units appear again in the following embodiments, their meanings are consistent with the description in this paragraph.

[0028] Example 1 like Figure 1As shown, this embodiment provides a multi-domain signal coordination method for testing connected intelligent driving. This method is used to test a connected intelligent driving vehicle under test in a test field. The test field can be a closed or controlled test site. When the connected intelligent driving vehicle under test is running in the test field, the test system acquires the real-time pose information of the connected intelligent driving vehicle under test. The real-time pose information is used to characterize the real-time spatial state of the connected intelligent driving vehicle under test in the test field and serves as the basic input for generating virtual traffic scenarios and generating GNSS simulation signals.

[0029] The testing system generates a virtual traffic scenario containing multiple virtual connected vehicles based on real-time pose information. This virtual traffic scenario is used to construct a traffic environment for the tested connected autonomous vehicle (V2V) under conditions where the number of vehicles in a real test track is limited. The virtual traffic scenario corresponds to the real-time pose information of the tested V2V, enabling the virtual connected vehicles to form a controllable traffic participant state around the tested V2V. After generating the virtual traffic scenario, the testing system determines the environmental feature information corresponding to the virtual traffic scenario and the motion state information of each virtual connected vehicle. The environmental feature information describes the environmental state within the virtual traffic scenario, while the motion state information describes the motion state of each virtual connected vehicle within the virtual traffic scenario. The environmental feature information and motion state information provide inputs for subsequent attenuation parameter determination and V2X radio frequency signal generation, respectively.

[0030] The test system generates simulated GNSS signals based on real-time pose information. These simulated GNSS signals provide a controllable positioning signal input to the connected autonomous vehicle under test. During the generation of the simulated GNSS signals, the output power is set higher than the power of real satellite signals over the test site, enabling the connected autonomous vehicle under test to use the simulated GNSS signals as a controlled positioning input when receiving positioning signals. In conjunction with the generation of the simulated GNSS signals, the test system converts the motion state information of each virtual connected vehicle into V2X radio frequency signals. These V2X radio frequency signals are used to provide the motion state information of the virtual connected vehicles to the V2X receiving link of the connected autonomous vehicle under test in radio frequency signal form.

[0031] After generating GNSS analog signals and V2X radio frequency signals, the test system determines GNSS attenuation parameters based on environmental feature information and real-time pose information, and V2X attenuation parameters based on environmental feature information, real-time pose information, and motion state information. The GNSS attenuation parameters characterize the attenuation state of the GNSS analog signals in the corresponding virtual traffic scenario, while the V2X attenuation parameters characterize the attenuation state of the V2X radio frequency signals in the same virtual traffic scenario. Since both types of attenuation parameters are determined based on environmental feature information corresponding to the virtual traffic scenario, the attenuation processing of the GNSS analog signals and V2X radio frequency signals can be carried out around the same test environment, avoiding the separation of degradation conditions between the two types of signals.

[0032] The test system attenuates the simulated GNSS signal based on GNSS attenuation parameters and the V2X radio frequency signal based on V2X attenuation parameters. The attenuated GNSS signal is used to simulate the positioning signal received by the tested connected intelligent driving vehicle under corresponding test environments, and the attenuated V2X radio frequency signal is used to simulate the vehicle wireless communication signal received by the tested connected intelligent driving vehicle under corresponding test environments.

[0033] Under the same test time base, the test system outputs attenuated GNSS analog signals to the connected autonomous vehicle under test (V2V), and injects attenuated V2X radio frequency signals into the V2V's on-board unit (OBU). The OBU receives the V2X radio frequency signals. By using the same test time base, the GNSS analog signals and V2X radio frequency signals received by the V2V maintain a temporal correspondence during the test, enabling the V2V to receive positioning and communication inputs under the same test procedure.

[0034] When the tested connected intelligent driving vehicle operates under attenuated GNSS analog signals and V2X radio frequency signals, the test system collects test data from the vehicle and generates test evaluation results based on the data. The test data reflects the operating status of the tested connected intelligent driving vehicle under controllable positioning input, controllable communication input, and corresponding attenuation processing conditions. Through the above process, this embodiment can utilize real test vehicles and virtual connected vehicles to jointly form a test scenario in the test field, and ensure that positioning signals and communication signals participate in the test under the same test time reference and the same virtual traffic scenario, thereby providing consistent signal input conditions for connected intelligent driving test evaluation.

[0035] Example 2 The multi-domain signal coordination connected intelligent driving test method provided in this embodiment, based on embodiment 1, further explains the generation of GNSS simulated signals of the Global Navigation Satellite System, the signal coordination execution under the same test time reference, and the determination methods of GNSS attenuation parameters and V2X attenuation parameters.

[0036] In this embodiment, when the test system generates a simulated GNSS signal based on the real-time pose information of the connected intelligent driving vehicle under test, the carrier frequency and modulation method of the simulated GNSS signal can be set to be consistent with the real satellite signal over the test site, so that the GNSS receiver of the connected intelligent driving vehicle under test can receive the simulated GNSS signal in the same way as receiving real satellite signals.

[0037] When setting the output power of the simulated GNSS signal, a preset suppression margin can be used. This preset suppression margin corresponds to the locking condition of the GNSS receiver of the connected vehicle under test for the simulated GNSS signal when the simulated GNSS signal and the real satellite signal coexist. Optionally, the power of the real satellite signal reaching the ground over the test site can be approximately -130 dBm, and the power of the simulated GNSS signal at the antenna end of the connected vehicle under test can be approximately -127 dBm to -125 dBm, making the simulated GNSS signal 3 dB to 5 dB higher than the real satellite signal. Because the code tracking loop and carrier tracking loop of the GNSS receiver tend to lock onto the stronger signal, the GNSS receiver of the connected vehicle under test can preferentially lock onto the simulated GNSS signal and reduce the impact of the real satellite signal on the test positioning input.

[0038] When generating GNSS simulated signals, the satellite constellation configuration corresponding to the GNSS simulated signals can be set according to the actual satellite constellation configuration at the test site's geographical location. The ephemeris parameters of the GNSS simulated signals can also be adjusted based on real-time pose information. The satellite constellation configuration is used to match the GNSS simulated signals with the satellite visibility status at the test site's geographical location, while the ephemeris parameters are used to correspond the GNSS simulated signals with the real-time pose information of the connected intelligent driving vehicle under test. Optionally, the GNSS signal simulator can calculate satellite pseudorange and GNSS Doppler shift based on the real-time pose information of the connected intelligent driving vehicle under test, the satellite constellation configuration, and the ephemeris parameters. Based on the calculated satellite pseudorange and GNSS Doppler shift, it generates GNSS simulated signals, allowing the positioning signal received by the connected intelligent driving vehicle under test to be updated according to its real-time pose changes.

[0039] When performing signal coordination under the same test time base, the scenario simulator, GNSS signal simulator, and V2X signal generator can establish the same test time base through Precision Time Protocol (PTP) or GNSS timing. Optionally, this same test time base can achieve a synchronization accuracy better than 100 ns. The scenario simulator is used to update the virtual traffic scene based on the real-time pose information of the connected intelligent driving vehicle under test, and calculates the motion state information of each virtual connected vehicle in the first update cycle. The GNSS signal simulator is used to output the attenuated GNSS analog signal in the second update cycle. The V2X signal generator is used to convert the motion state information into V2X radio frequency signals in the third update cycle.

[0040] Optionally, the virtual traffic scenario may include Virtual connected vehicles In one specific implementation, The first update cycle is 10ms, the second is 1ms, and the third is 100ms. The third update cycle corresponds to the 10Hz transmission frequency of the Basic Safety Message (BSM). The V2X signal generator can encode the motion state information of each virtual connected vehicle into a Basic Safety Message (BSM) conforming to the SAE J2735 standard, and convert it into a V2X radio frequency signal for injection into the On-Board Unit (OBU) of the connected intelligent driving vehicle under test.

[0041] In the above process, the first, second, and third update cycles can be coordinated based on the signal processing delays of the scenario simulator, the GNSS signal simulator, and the V2X signal generator. The scenario simulator at any given time... The calculated virtual connected vehicle location can be represented as The location of the virtual connected vehicle is delayed by encoding. Then it arrives at the vehicle-to-everything (V2X) wireless communication signal generator, the vehicle-to-everything (V2X) wireless communication signal generator at time Transmits V2X radio frequency signals containing the Basic Safety Message (BSM). Correspondingly, the GNSS receiver of the tested connected autonomous vehicle transmits V2X radio frequency signals at a specific time. The positioning solution based on the analog signal output of the Global Navigation Satellite System (GNSS) can be expressed as: By adjusting the encoding latency in the scene simulator. Pre-compensation can align the virtual connected vehicle location and GNSS positioning solution in the Basic Safety Message (BSM) on the timestamp, thereby enabling the output of the GNSS analog signal and the injection of the V2X radio frequency signal to be executed synchronously under the same test time reference.

[0042] See Figure 2 When determining GNSS attenuation parameters, the signal propagation path from each simulated satellite to the tested connected intelligent vehicle can be obstructed based on the three-dimensional geometric model of buildings and real-time pose information in the environmental feature information, thus determining the set of visible satellites and the set of obstructed satellites. Specifically, the direct ray from each simulated satellite to the GNSS receiving antenna of the tested connected intelligent vehicle can be calculated, and it can be determined whether the direct ray intersects with a building in the three-dimensional geometric model of the building. If it intersects, the corresponding simulated satellite is classified into the set of obstructed satellites; if it does not intersect, the corresponding simulated satellite is classified into the set of visible satellites.

[0043] For simulated satellites in the visible satellite array, the number, amplitude, delay, and phase of reflection paths can be calculated based on the three-dimensional geometric model of the building to obtain GNSS multipath parameters. Optionally, the specular reflection path of simulated satellite signals from the building walls in the scene can be calculated to reach the GNSS receiving antenna of the connected intelligent driving vehicle under test, and the amplitude, delay, and phase of the reflection path can be recorded. The amplitude of the reflection path can be determined by the wall reflection coefficient and the reflection path length, and the delay of the reflection path can be determined by dividing the difference between the reflection path and the direct path by the speed of light.

[0044] Based on the set of visible satellites, the set of obscured satellites, and GNSS multipath parameters, the carrier-to-noise ratio (CNR) degradation value of the GNSS receiver for each simulated satellite in the tested connected intelligent vehicle can be determined. The CNR degradation value can be calculated according to the following model:

[0045] in, The carrier-to-noise ratio (CNR) of a GNSS receiver to a corresponding analog satellite. This serves as a benchmark value for the carrier-to-noise ratio of a simulated satellite in an open environment. This represents the carrier-to-noise ratio degradation caused by multipath propagation. The carrier-to-noise ratio degradation caused by occlusion. It can be related to the satellite's elevation angle. It can be related to the amplitude and number of reflection paths; for obscured satellites, A larger value can be taken, making It is below the tracking threshold of a GNSS receiver.

[0046] By using the set of visible satellites, the set of obscured satellites, GNSS multipath parameters, and carrier-to-noise ratio (CNR) degradation values ​​as GNSS attenuation parameters, these parameters can be sent to the Global Navigation Satellite System (GNSS) signal simulator for execution. The GNSS signal simulator can adjust the transmit power of each simulated satellite signal based on the CNR degradation value, add multipath components according to the GNSS multipath parameters, and reduce the signal power of obscured satellites to below the receiver's loss-of-lock threshold.

[0047] When determining V2X attenuation parameters, the propagation path of the V2X radio frequency signal from each virtual connected vehicle to the tested connected intelligent driving vehicle can be determined by analyzing the 3D geometric model of buildings in the environmental feature information. This determines the line-of-sight (LOS) and non-line-of-sight (NOS) propagation paths. Specifically, the direct ray from each virtual connected vehicle to the OBU antenna of the tested connected intelligent driving vehicle can be calculated, and it can be determined whether this direct ray intersects with a building in the 3D geometric model. If it intersects, it is determined to be a NOS propagation path; if it does not intersect, it is determined to be a LOS propagation path.

[0048] Path loss can be determined based on the line-of-sight and non-line-of-sight propagation paths, combined with the real-time relative distance between the tested connected intelligent driving vehicle and each virtual connected vehicle. Optionally, the path loss can be determined based on the real-time relative distance between the tested connected intelligent driving vehicle and each virtual connected vehicle. Calculate the path loss according to the free space path loss model:

[0049] in, For path loss, This refers to the real-time relative distance between the tested connected intelligent driving vehicle and the virtual connected vehicle. For vehicle-to-everything (V2X) wireless communication carrier frequencies, The speed of light. In one specific implementation, the V2X carrier frequency for vehicle-to-everything (V2X) wireless communication... It can be 5.9 GHz. For non-line-of-sight propagation paths, non-line-of-sight penetration loss can be added on top of the free-space path loss, and the non-line-of-sight penetration loss can be 15 dB to 20 dB.

[0050] The Doppler frequency shift can be determined based on the real-time relative speed between the tested connected intelligent driving vehicle and each virtual connected vehicle, and the V2X wireless communication carrier frequency. The Doppler frequency shift can be calculated using the following model:

[0051] in, For Doppler frequency shift, The real-time relative speed between the tested connected intelligent driving vehicle and the virtual connected vehicle. For vehicle-to-everything (V2X) wireless communication carrier frequencies, It is the speed of light.

[0052] For propagation environments with reflectors, the number, amplitude, and time delay spread of reflection paths can be determined based on a three-dimensional geometric model of the building, yielding V2X multipath parameters. The time delay spread can be expressed as... This parameter reflects the dispersion of multipath energy in the time domain and can be used to characterize the impact of multipath propagation on the V2X (Vehicle-to-Everything) wireless communication reception process. After using path loss, Doppler shift, and V2X multipath parameters as V2X attenuation parameters, the V2X attenuation parameters can be sent to the V2X channel simulator. The V2X channel simulator applies attenuation to the RF signal output from the V2X signal generator to the On-Board Unit (OBU) of the connected intelligent driving vehicle under test, based on the path loss, Doppler shift, and V2X multipath parameters.

[0053] In this embodiment, both GNSS attenuation parameters and V2X attenuation parameters are determined based on the same environmental characteristic information and calculated by combining the real-time pose information of the connected intelligent vehicle under test and the motion state information of each virtual connected vehicle. Since the propagation obstruction judgment from simulated satellites to the connected intelligent vehicle under test and from virtual connected vehicles to the connected intelligent vehicle under test uses the same three-dimensional geometric model of a building and the same pose of the connected intelligent vehicle under test as input, the degradation state of the GNSS analog signal and the V2X radio frequency signal can reflect the propagation conditions under the same physical environment, thus ensuring that the attenuation processing of the two types of signals remains consistent in the test environment and time execution.

[0054] Example 3 The multi-domain signal coordination connected intelligent driving test method provided in this embodiment, based on embodiment 1 or embodiment 2, further explains the determination method of test data, test evaluation results, multi-domain joint attenuation parameter space, and multi-domain joint degradation critical parameter combination.

[0055] In this embodiment, the test system collects test data from the connected intelligent driving vehicle under test under attenuated GNSS analog signals and V2X radio frequency signals. The test data may include signal quality data, functional response data, and driving behavior data. Signal quality data reflects the quality status of the GNSS analog signals and V2X radio frequency signals received by the connected intelligent driving vehicle under test; functional response data reflects the response of the connected intelligent driving functions of the connected intelligent driving vehicle under test under the corresponding signal inputs; and driving behavior data reflects the driving status of the connected intelligent driving vehicle under test during the test.

[0056] In some implementations, a three-tiered evaluation system can be constructed, encompassing signal quality, functional response, and driving safety. Signal quality evaluation quantifies the degree of signal degradation in the GNSS and V2X signal domains after multi-domain signal attenuation; functional response evaluation quantifies the impact of signal degradation on the functional response of connected intelligent driving systems; and driving safety evaluation quantifies the impact of functional response on driving safety. Specific evaluation indicators are shown in Table 1.

[0057] Table 1. Three-tier evaluation indicators and data sources

[0058] The testing system can determine signal quality evaluation results based on signal quality data, functional response evaluation results based on functional response data, and driving safety evaluation results based on driving behavior data. Signal quality evaluation results represent the input status of GNSS positioning signals and V2X (vehicle-to-everything) wireless communication signals; functional response evaluation results represent the response status of connected intelligent driving functions to the above input statuses; and driving safety evaluation results represent the safety evaluation results corresponding to the vehicle's operating status.

[0059] After obtaining the signal quality evaluation results, functional response evaluation results, and driving safety evaluation results, a first mapping relationship can be established between the signal quality evaluation results and the functional response evaluation results, and a second mapping relationship can be established between the functional response evaluation results and the driving safety evaluation results. Optionally, the first and second mapping relationships can be established using statistical regression or machine learning methods. The first mapping relationship is used to quantify the impact of changes in GNSS positioning signal quality and V2X communication quality on the functional response of connected intelligent driving; the second mapping relationship is used to quantify the transmission impact of changes in connected intelligent driving functional response on driving behavior safety. Through the above processing, the test evaluation results can not only reflect changes in individual indicators, but also reflect the process of multi-domain signal attenuation being transmitted to driving safety through functional response.

[0060] Based on the above embodiments, considering that connected intelligent driving functions may be simultaneously affected by GNSS positioning accuracy degradation and V2X communication quality degradation, it is difficult to determine the critical conditions for the connected intelligent driving function to transition from a normal state to a degraded state or a failure state when both types of signals degrade together, using degradation tests of a single signal domain or tests with several fixed parameter combinations. Therefore, this embodiment further explains the determination method of the multi-domain joint attenuation parameter space and the multi-domain joint degradation critical parameter combination.

[0061] See Figure 3 After generating the test evaluation results, the GNSS positioning accuracy degradation level and V2X communication quality degradation level can be determined based on the signal quality evaluation results. A multi-domain joint attenuation parameter space is then constructed based on these levels. The GNSS positioning accuracy degradation level can be controlled by adjusting the carrier-to-noise ratio degradation value and GNSS multipath parameters, while the V2X communication quality degradation level can be controlled by adjusting path loss, Doppler shift, and V2X multipath parameters. The multi-domain joint attenuation parameter space represents the combined state of attenuation parameters under the combined effects of GNSS positioning degradation and V2X communication quality degradation.

[0062] In one specific implementation, the GNSS positioning accuracy degradation level can be divided into five levels, L1 to L5, where L1 is normal (positioning accuracy less than 1m) and L5 is severely degraded (positioning accuracy greater than 10m). The V2X communication quality degradation level can be divided into five levels, C1 to C5, where C1 is normal (packet loss rate less than 1%) and C5 is severely degraded (packet loss rate greater than 30%). The GNSS positioning accuracy degradation level and the V2X communication quality degradation level are combined to form 25 parameter grids (5 x 5), each parameter grid corresponding to a joint degradation degree in the GNSS and V2X signal domains.

[0063] In the multi-domain joint attenuation parameter space, multiple parameter combinations can be selected. For each parameter combination, the corresponding GNSS attenuation parameters and V2X attenuation parameters can be determined based on this combination. The GNSS analog signal and V2X radio frequency signal are then attenuated according to these parameters. After attenuation processing, the test system, under the same test time reference, outputs the attenuated GNSS analog signal corresponding to each parameter combination to the connected intelligent driving vehicle under test, and injects the attenuated V2X radio frequency signal corresponding to each parameter combination into the on-board unit (OBU), collecting test data corresponding to each parameter combination.

[0064] In some implementations, binary search or Bayesian optimization algorithms can be used to adaptively search for the critical combination of multi-domain joint degradation parameters in the multi-domain joint attenuation parameter space. When using Bayesian optimization, initial grid sampling points can be uniformly selected in the parameter space. As a specific implementation, nine initial grid sampling points can be selected, covering the center, four corners, and midpoints of the four sides of the parameter space, respectively. For each initial grid sampling point, signal attenuation, signal application, and test data acquisition processes are performed to obtain the corresponding connected intelligent driving function response results, and these results are used as prior training data for the surrogate model.

[0065] After obtaining the initial test results, the surrogate model can predict the normal probability and prediction uncertainty of any parameter combination in the parameter space. The expected improvement acquisition function is then used to select the next set of test parameter combinations in the parameter space. This function comprehensively considers both prediction probability and prediction uncertainty, prioritizing regions near the degradation threshold with higher model uncertainty, ensuring that each test round obtains effective information for locating the degradation threshold. After each parameter combination test is completed, the test system adds the corresponding connected intelligent driving function response data and driving behavior data to the surrogate model's training set and updates the surrogate model.

[0066] Repeat the parameter combination selection and test data acquisition process until the convergence condition is met. Optionally, the convergence condition can be that the change in the position of the degradation critical surface predicted by the surrogate model in two consecutive iterations is less than a preset threshold, which can be 10% of the parameter space grid spacing. After convergence, output the parameter combination on the degradation critical surface as the multi-domain joint degradation critical parameter combination for the connected intelligent driving function of the tested connected intelligent driving vehicle to change from a normal state to a degraded state or a failure state.

[0067] The degradation critical surface divides the multi-domain joint attenuation parameter space into a normal functional zone and a degraded functional zone. The normal functional zone corresponds to a parameter combination area with relatively minor degradation in GNSS positioning accuracy and V2X communication quality, where the connected intelligent driving function of the tested vehicle maintains normal response. The degraded functional zone corresponds to a parameter combination area with severe degradation in GNSS positioning accuracy and V2X communication quality, where the connected intelligent driving function of the tested vehicle degrades or fails. By determining the multi-domain joint degradation critical parameter combination, the critical conditions under which the connected intelligent driving function transitions from a normal state to a degraded or failed state under the combined effects of GNSS positioning accuracy degradation and V2X communication quality degradation can be obtained. This avoids evaluating connected intelligent driving functions solely based on a single signal domain or fixed parameter combination, providing parameter basis for functional testing under conditions of joint degradation of multi-domain signals.

[0068] Example 4 This embodiment provides a complete testing process for a multi-domain signal coordination-based connected intelligent driving test method, which is used to cover scenario types, baseline values ​​of attenuation parameters, scene transitions, and signal attenuation execution methods that were not fully explored in the aforementioned embodiments.

[0069] In this embodiment, the connected intelligent driving vehicle under test can be deployed in a test field and driven along a preset test route. The test system continuously acquires the real-time pose information of the connected intelligent driving vehicle under test, which may include at least one of position, velocity, heading angle, and attitude angle. The real-time pose information serves as a common input for updating the virtual traffic scene, generating GNSS analog signals, generating V2X radio frequency signals, and performing multi-domain signal attenuation processing.

[0070] The testing system generates multiple virtual connected vehicles in a virtual traffic scenario based on the real-time pose information of the connected intelligent driving vehicle under test. These virtual connected vehicles can be distributed around the tested vehicle and, according to testing requirements, can exhibit various traffic states such as following, parallel driving, cutting in, intersection conflicts, or cooperative driving. The virtual traffic scenario may also include environmental features such as 3D geometric models of roads, lanes, roadside structures, and buildings, as well as information on the spatial distribution of these structures. The motion state information of each virtual connected vehicle can include at least one of position, speed, acceleration, heading angle, and lane information. The aforementioned real-time pose information, environmental features, and motion state information collectively constitute the foundational data for subsequent signal generation, scenario type determination, and signal attenuation processing.

[0071] In some implementations, the test system can preset multiple scenario types, each corresponding to a set of multi-domain signal cooperative attenuation parameter baseline values. These baseline values ​​can simultaneously include GNSS signal domain baseline parameters and V2X signal domain baseline parameters. The test system can query the current scenario type based on the real-time pose information of the connected intelligent driving vehicle under test within the test field, and retrieve the corresponding GNSS signal domain baseline parameters and V2X signal domain baseline parameters from a preset multi-domain signal cooperative attenuation parameter library.

[0072] Table 2. Scene types and baseline values ​​of multi-domain signal attenuation parameters corresponding to environmental feature information.

[0073] In open areas, the propagation paths from simulated satellites to the tested connected intelligent vehicle and from the virtual connected vehicle to the tested connected intelligent vehicle are unobstructed. At this time, the number of visible satellites in the GNSS attenuation parameters is relatively large. Maintaining a high level, the multipath parameter is zero or weak; the path loss in the V2X attenuation parameter can be free space loss, and multipath mainly comes from ground reflection. Therefore, both the GNSS signal domain and the V2X signal domain can be in a normal state simultaneously.

[0074] In an urban canyon, tall buildings flank the tested connected autonomous vehicle. Low-elevation simulated satellites are easily obstructed by buildings, while high-elevation simulated satellites remain visible. Simultaneously, the direct propagation path from the virtual connected vehicle to the tested connected autonomous vehicle may be blocked by buildings, creating strong reflection paths. In this situation, both the GNSS and V2X signal domains exhibit moderate degradation, and the degree of degradation is related to environmental characteristics such as building height, spacing, and wall reflection characteristics in the three-dimensional geometric model of the buildings.

[0075] Inside the tunnel, the three-dimensional geometric model of the building or the tunnel structure can completely block the propagation path of the simulated satellite to the tested connected intelligent driving vehicle, reducing the number of visible satellites to zero, and making... The signal falls below the receiver tracking threshold, causing the GNSS receiver to lose lock. Simultaneously, the tunnel structure affects the propagation path of V2X radio frequency signals from the virtual connected vehicle to the tested connected autonomous vehicle, increasing V2X path loss and causing Rayleigh fading. Consequently, the GNSS signal domain suffers severe degradation in tunnel scenarios, and the V2X signal domain also experiences corresponding degradation.

[0076] Beneath the viaduct, where there is an elevated structure above or around the tested connected intelligent driving vehicle, simulated satellite signals may be intermittently blocked, reducing the number of visible satellites. The path loss is reduced and a certain number of multipaths are generated; the vehicle-to-everything (V2X) radio frequency signal of the virtual connected vehicle to the tested connected intelligent driving vehicle may also be affected by the elevated structure, with path loss increasing intermittently and forming multiple reflection paths.

[0077] On tree-lined roads, trees or obstructions on both sides of the road can cause weak obstruction or scattering of some analog satellite signals and vehicle-to-everything (V2X) radio frequency signals. At this time, the number of visible satellites and... Compared to open areas, the path loss in V2X attenuation parameters is reduced, but can be increased by 3-5 dB on top of free space loss, and a certain number of multipaths are formed.

[0078] In some implementations, when the connected autonomous vehicle under test transitions from one scene type to another, the GNSS attenuation parameters and V2X attenuation parameters can undergo linear or nonlinear transitions according to their respective gradient functions. The starting position, duration, and parameter change rate of the gradient transition can be determined based on the speed of the connected autonomous vehicle under test and the scene geometry. In this way, the signal attenuation processing can reflect the spatial gradient state of signal degradation when the connected autonomous vehicle under test moves from one environment to another in the test field.

[0079] Taking the tested connected intelligent driving vehicle entering a tunnel entrance from an open area as an example, the attenuation transition can begin 50m from the tunnel entrance. During this transition, the number of visible satellites in the GNSS attenuation parameters can be linearly reduced from 8 to 0, with each reduction corresponding to... The path loss in the V2X attenuation parameters decreases by 3-5 dB; it can increase linearly from free space loss to free space +30 dB. If the speed of the connected autonomous vehicle under test is 60 km / h, the gradual transition time corresponding to a distance of 50 m is approximately 3 seconds. Through the above gradual transition, the process of multi-domain signal degradation gradually worsening when the connected autonomous vehicle under test enters a tunnel in a real environment can be simulated.

[0080] During the test, the GNSS analog signal and the V2X radio frequency signal corresponded to the positioning and receiving links of the connected autonomous vehicle under test, respectively. The GNSS analog signal was generated based on the real-time pose information of the connected autonomous vehicle under test, enabling it to receive controlled positioning signal input. The V2X radio frequency signal was generated based on the motion state information of each virtual connected vehicle, allowing these virtual connected vehicles to participate in the connected driving function test of the connected autonomous vehicle under test using V2X radio frequency signals.

[0081] In some implementations, the vehicular wireless communication (V2X) radio frequency signal can carry the Basic Safety Message (BSM) of the virtual connected vehicle. The BSM may contain information such as the virtual connected vehicle's position, speed, heading angle, vehicle size, or vehicle status. By converting the BSM into a V2X radio frequency signal and injecting it into the on-board unit (OBU), the connected autonomous vehicle under test can receive the motion status information of the virtual connected vehicle through a real vehicular wireless communication receiving link, rather than simply obtaining virtual traffic participant data directly in the upper-layer software. Therefore, the OBU's radio frequency reception, message parsing, and subsequent connected driving function responses can all participate in the testing process.

[0082] During signal attenuation processing, the test system can calculate attenuation parameters for GNSS analog signals and V2X radio frequency signals using a multi-domain signal cooperative attenuation model. The multi-domain signal cooperative attenuation model takes environmental feature information corresponding to the virtual traffic scenario as common input, and combines it with the real-time pose information of the tested connected intelligent driving vehicle and the motion state information of each virtual connected vehicle to output GNSS attenuation parameters and V2X attenuation parameters respectively. The three-dimensional geometric models of buildings and the spatial distribution information of structures in the environmental feature information can serve as a common driving source for the calculation of both types of attenuation parameters.

[0083] In some implementations, propagation obstruction and multipath effects of GNSS analog signals and V2X radio frequency signals can be determined based on ray tracing or empirical lookup table methods. When using ray tracing, the propagation path from the simulated satellite to the GNSS receiving antenna of the connected autonomous vehicle under test, and the propagation path from the virtual connected vehicle to the On-Board Unit (OBU) antenna of the connected autonomous vehicle under test, can be determined based on the three-dimensional geometric model of the building. When using empirical lookup table methods, the GNSS signal domain baseline parameters and V2X signal domain baseline parameters shown in Table 2 can be looked up based on the current scenario type, and the corresponding attenuation parameters can be adjusted in conjunction with real-time pose information.

[0084] In the attenuation process of GNSS simulated signals, GNSS attenuation parameters can be sent to the GNSS signal simulator. The GNSS signal simulator can adjust the transmit power of each simulated satellite signal based on the carrier-to-noise ratio degradation value, add multipath components according to GNSS multipath parameters, and reduce the signal power of blocked satellites to below the receiver's loss-of-lock threshold. In some implementations, the execution of GNSS attenuation parameters can be performed by the GNSS signal simulator itself.

[0085] In the attenuation process of V2X (Vehicle-to-Everything) radio frequency (RF) signals, V2X attenuation parameters can be sent to the V2X channel simulator. The V2X channel simulator can apply path loss, Doppler shift, and multipath fading effects to the V2X RF signals output from the V2X signal generator to the On-Board Unit (OBU) of the connected autonomous vehicle under test, based on path loss, Doppler shift, and V2X multipath parameters. By performing fading processing through the V2X channel simulator, the OBU can receive the attenuated V2X RF signals at the RF receiving layer.

[0086] Under the same test time reference, the test system outputs attenuated GNSS analog signals to the connected autonomous vehicle under test (V2V), and injects attenuated V2X radio frequency signals into the V2V's on-board unit (OBU). The GNSS analog signals influence the V2V's positioning input, while the V2X signals influence the virtual connected vehicle motion status information received by the V2V. Both are applied collaboratively under the same test time reference, ensuring that the V2V receives corresponding positioning and V2X inputs within the same test period.

[0087] During a complete test, the testing system can record input data, signal processing data, and vehicle response data in chronological order. Input data may include real-time pose information, motion state information of the virtual connected vehicle, and environmental characteristic information. Signal processing data may include GNSS analog signals, V2X radio frequency signals, GNSS attenuation parameters, V2X attenuation parameters, scene type, and attenuation parameter gradient process. Vehicle response data may include functional response data and driving behavior data output by the connected intelligent driving vehicle under test. All of the above data can be stored in a correlated manner according to the test timeline for subsequent generation of test evaluation results.

[0088] Through the complete testing process in this embodiment, multiple virtual connected vehicles can be introduced into a test field involving a real connected intelligent driving vehicle under test, while simultaneously controlling the attenuation status of the GNSS analog signal and the V2X radio frequency signal. Since the virtual traffic scenario, environmental characteristics, scenario type, baseline attenuation parameter value, gradual transition process, positioning signal input, and V2X radio frequency signal input are all correlated within the same testing procedure, the test data of the connected intelligent driving vehicle under test can reflect the functional response and driving behavior when both positioning input and V2X radio frequency input change together.

[0089] Example 5 like Figure 4 As shown, this embodiment provides a multi-domain signal coordination connected intelligent driving test system, which can be used to execute the multi-domain signal coordination connected intelligent driving test method described in the above embodiment. The system includes a scene generation module, a signal generation module, an attenuation processing module, a signal coordination module, and an evaluation generation module.

[0090] The scene generation module acquires the real-time pose information of the tested connected intelligent driving vehicle within the test range and generates a virtual traffic scene containing multiple virtual connected vehicles based on this information. The scene generation module also determines the environmental feature information corresponding to the virtual traffic scene and the motion state information of each virtual connected vehicle. The environmental feature information can be provided to the attenuation processing module to determine GNSS attenuation parameters and V2X attenuation parameters; the motion state information of each virtual connected vehicle can be provided to the signal generation module to convert it into V2X radio frequency signals for vehicle-to-everything (V2X) wireless communication.

[0091] The signal generation module generates a simulated GNSS signal based on real-time pose information and converts motion state information into a V2X (Vehicle-to-Everything) radio frequency signal. The simulated GNSS signal corresponds to the positioning input of the connected autonomous vehicle under test, while the V2X radio frequency signal corresponds to the wireless communication input provided by the virtual connected vehicle to the connected autonomous vehicle under test. The signal generation module can be configured to correspond to both the GNSS signal simulator and the V2X signal generator, or it can be implemented using a combination of devices capable of generating the corresponding signals.

[0092] The attenuation processing module determines GNSS attenuation parameters based on environmental feature information and real-time pose information, and determines V2X attenuation parameters based on environmental feature information, real-time pose information, and motion state information. The attenuation processing module also performs attenuation processing on the GNSS analog signal and the V2X radio frequency signal based on the GNSS attenuation parameters and V2X radio frequency signal, respectively. Through the attenuation processing module, the GNSS analog signal and the V2X radio frequency signal can form corresponding signal degradation states under the constraints of environmental feature information corresponding to the same virtual traffic scenario.

[0093] The signal coordination module is used to output attenuated GNSS analog signals to the connected autonomous vehicle under test (V2V) and inject attenuated V2X radio frequency signals into the V2V's on-board unit (OBU) under the same test time reference. The output power of the GNSS analog signals is set higher than the power of real satellite signals over the test site. By coordinating the output of the GNSS analog signals and the injection of the V2X radio frequency signals under the same test time reference, the signal coordination module enables the V2V to receive corresponding positioning and V2X inputs during the same test process.

[0094] The evaluation generation module collects test data from the tested connected intelligent driving vehicle under attenuated GNSS analog signals and V2X radio frequency signals, and generates test evaluation results. The test data may include signal quality data, functional response data, and driving behavior data. The evaluation generation module can generate test evaluation results based on the signal quality data, functional response data, and driving behavior data, characterizing the operating status and functional performance of the tested connected intelligent driving vehicle under multi-domain signal collaborative testing conditions.

[0095] exist Figure 4In the illustrated implementation, the scene generation module, signal generation module, attenuation processing module, signal coordination module, and evaluation generation module are connected according to the test data stream and signal stream. The scene generation module outputs real-time pose information, environmental feature information, and motion state information; the signal generation module generates GNSS analog signals and V2X radio frequency signals based on the real-time pose and motion state information; the attenuation processing module determines and applies GNSS attenuation parameters and V2X attenuation parameters based on the environmental feature information; the signal coordination module applies the attenuated signals to the connected intelligent driving vehicle under test; and the evaluation generation module receives the test data from the connected intelligent driving vehicle under test during the test and generates test evaluation results. The cooperative relationship between these modules enables the system to realize multi-domain signal coordination connected intelligent driving testing in a modular manner.

[0096] Example 6 like Figure 5 As shown, this embodiment provides a computer device that can be used to implement the multi-domain signal coordination connected intelligent driving test method described in the above embodiments. The computer device includes a processor and a computer-readable storage medium. The processor is adapted to execute a computer program, and the computer-readable storage medium stores the computer program. When the computer program is executed by the processor, it implements the multi-domain signal coordination connected intelligent driving test method described in the above embodiments.

[0097] In this embodiment, the computer device can acquire the real-time pose information of the tested connected intelligent driving vehicle in the test field, and generate a virtual traffic scene containing multiple virtual connected vehicles based on the real-time pose information, determining the environmental feature information corresponding to the virtual traffic scene and the motion state information of each virtual connected vehicle. When the processor executes the computer program, it can generate control data for GNSS analog signals based on the real-time pose information, and generate control data for V2X radio frequency signals based on the motion state information, enabling the corresponding signal equipment to generate GNSS analog signals and V2X radio frequency signals.

[0098] When the processor executes the computer program, it can also determine GNSS attenuation parameters based on environmental feature information and real-time pose information, and determine V2X attenuation parameters based on environmental feature information, real-time pose information, and motion state information. Based on the GNSS attenuation parameters and V2X attenuation parameters, it performs attenuation processing on the analog GNSS signal and the V2X radio frequency signal of the connected vehicle under test, respectively. After the attenuation processing is completed, the computer equipment can control the output of the attenuated GNSS analog signal to the connected vehicle under test under the same test time reference, and control the injection of the attenuated V2X radio frequency signal into the on-board unit (OBU) of the connected vehicle under test.

[0099] When the tested connected intelligent driving vehicle operates under attenuated GNSS analog signals and V2X radio frequency signals, the processor executes a computer program to collect test data from the vehicle and generate test evaluation results based on the data. Thus, real-time pose information processing, virtual traffic scene generation, signal attenuation parameter determination, test data acquisition, and test evaluation result generation can all be completed within the computer equipment.

[0100] In some implementations, the computer device may also include a communication interface. The communication interface can communicate with a scenario simulator, a GNSS signal simulator, a V2X signal generator, an attenuation processing device, or a data acquisition device, so that the computer device can send control commands or parameter data to the corresponding device and receive test data fed back by the corresponding device. The communication interface can be a wired communication interface or a wireless communication interface; this embodiment does not limit the specific type of communication interface.

[0101] In some implementations, the computer-readable storage medium may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store the operating system and computer programs, while the internal memory may provide an environment for the operation of the operating system and computer programs. The processor may be a central processing unit, a graphics processing unit, a digital signal processor, a microcontroller, or other processing units with data processing capabilities. The specific hardware composition of the computer device can be configured according to the deployment method of the test system, as long as it can execute the computer program through the processor to realize the above-mentioned multi-domain signal coordination connected intelligent driving test method.

[0102] This embodiment also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the multi-domain signal coordination connected intelligent driving test method described in the above embodiments. The computer-readable storage medium can be a read-only memory, random access memory, disk, optical disk, flash memory, or other media capable of storing a computer program. By storing the computer program implementing the multi-domain signal coordination connected intelligent driving test method in a computer-readable storage medium, the above method can be run on a computer device through program loading and execution, facilitating deployment and invocation in different test systems or computer devices. The various embodiments in this specification are described in a progressive manner; similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the description of the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; however, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0103] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A testing method for connected intelligent driving with multi-domain signal coordination, characterized in that, include: The system acquires the real-time pose information of the connected intelligent driving vehicle under test in the test field, and generates a virtual traffic scene containing multiple virtual connected vehicles based on the real-time pose information. The system also determines the environmental feature information and motion state information of each virtual connected vehicle corresponding to the virtual traffic scene. Based on the real-time pose information, a Global Navigation Satellite System (GNSS) simulated signal is generated, and the motion state information is converted into a Vehicle-to-Everything (V2X) radio frequency signal; wherein, the output power of the GNSS simulated signal is set to be higher than the power of the real satellite signal over the test site; Using the same set of environmental feature information as the driving source, a multi-domain signal collaborative attenuation model is used to determine GNSS attenuation parameters based on the environmental feature information and the real-time pose information, and V2X attenuation parameters based on the environmental feature information, the real-time pose information and the motion state information. The GNSS analog signal and the V2X radio frequency signal are attenuated according to the GNSS attenuation parameters and the V2X attenuation parameters, respectively. Under the same test time reference, the attenuated GNSS analog signal is output to the connected intelligent driving vehicle under test, and the attenuated V2X radio frequency signal is injected into the on-board unit (OBU) of the connected intelligent driving vehicle under test. Test data of the tested connected intelligent driving vehicle under attenuation processing of GNSS analog signal and V2X radio frequency signal are collected, and test evaluation results are generated.

2. The multi-domain signal coordination test method for connected intelligent driving according to claim 1, characterized in that, The generation of GNSS analog signals based on the real-time pose information includes: Set the carrier frequency and modulation scheme of the GNSS analog signal to be consistent with the real satellite signal; The output power of the GNSS analog signal is set according to a preset suppression margin, which corresponds to the locking condition of the GNSS receiver of the tested connected intelligent driving vehicle to the GNSS analog signal when the GNSS analog signal and the real satellite signal coexist. The satellite constellation configuration corresponding to the GNSS simulated signal is set according to the actual satellite constellation configuration of the test site's geographical location, and the ephemeris parameters of the GNSS simulated signal are adjusted based on the real-time pose information to generate the GNSS simulated signal.

3. The multi-domain signal coordination test method for connected intelligent driving according to claim 1, characterized in that, The process of outputting attenuated GNSS analog signals to the tested connected intelligent driving vehicle under the same test time reference, and injecting attenuated V2X radio frequency signals into the on-board unit (OBU) of the tested connected intelligent driving vehicle, includes: The scene simulator, GNSS signal simulator, and V2X signal generator establish the same test time reference through Precision Time Protocol (PTP) or GNSS timing. The motion state information of each virtual connected vehicle is calculated using the scene simulator at a first update cycle. The attenuated GNSS analog signal is output through the GNSS signal simulator in the second update cycle; The motion state information is converted into the V2X radio frequency signal by the V2X signal generator in a third update cycle; The attenuated V2X radio frequency signal is injected into the on-board unit (OBU). Based on the signal processing delays of the scenario simulator, the GNSS signal simulator, and the V2X signal generator, the first update cycle, the second update cycle, and the third update cycle are coordinated to ensure that the output of the GNSS analog signal and the injection of the V2X radio frequency signal are executed synchronously under the same test time reference.

4. The multi-domain signal coordination test method for connected intelligent driving according to claim 1, characterized in that, The determination of GNSS attenuation parameters based on the environmental feature information and the real-time pose information includes: Based on the three-dimensional geometric model of the building in the environmental feature information and the real-time pose information, the signal propagation path from each simulated satellite to the tested connected intelligent driving vehicle is occlusion judged to determine the set of visible satellites and the set of occluded satellites. For the simulated satellites in the visible satellite set, the number, amplitude, delay, and phase of the reflection paths are calculated based on the three-dimensional geometric model of the building to obtain GNSS multipath parameters; Based on the set of visible satellites, the set of obscured satellites, and the GNSS multipath parameters, the carrier-to-noise ratio degradation value is determined. The visible satellite set, the obscured satellite set, the GNSS multipath parameter, and the carrier-to-noise ratio degradation value are used as the GNSS attenuation parameter.

5. The multi-domain signal coordination test method for connected intelligent driving according to claim 1, characterized in that, The determination of V2X attenuation parameters based on the environmental feature information, the real-time pose information, and the motion state information includes: Based on the three-dimensional geometric model of the building in the environmental feature information, the V2X radio frequency signal propagation path from each virtual connected vehicle to the tested connected intelligent driving vehicle is blocked to determine the line-of-sight propagation path and the non-line-of-sight propagation path. The path loss is determined based on the line-of-sight propagation path and the non-line-of-sight propagation path, as well as the real-time relative distance between the tested connected intelligent driving vehicle and each virtual connected vehicle. The Doppler shift is determined based on the real-time relative speed between the tested connected intelligent driving vehicle and each virtual connected vehicle and the V2X carrier frequency; Based on the three-dimensional geometric model of the building, the number, amplitude, and time delay spread of the reflection paths are determined to obtain the V2X multipath parameters. The path loss, the Doppler frequency shift, and the V2X multipath parameter are used as the V2X attenuation parameter.

6. The multi-domain signal coordination test method for connected intelligent driving according to claim 1, characterized in that, The test data of the connected intelligent driving vehicle under test under attenuated GNSS analog signal and V2X radio frequency signal are collected to generate test evaluation results, including: Collect signal quality data, functional response data, and driving behavior data, wherein the signal quality data includes GNSS positioning accuracy data and V2X communication quality data; The signal quality evaluation result is determined based on the signal quality data, the function response evaluation result is determined based on the function response data, and the driving safety evaluation result is determined based on the driving behavior data. A first mapping relationship is established between the signal quality evaluation result and the functional response evaluation result, and a second mapping relationship is established between the functional response evaluation result and the driving safety evaluation result.

7. The multi-domain signal coordination test method for connected intelligent driving according to claim 6, characterized in that, After generating the test evaluation results, the following is also included: The GNSS positioning accuracy degradation level and V2X communication quality degradation level are determined based on the signal quality evaluation results, and a multi-domain joint attenuation parameter space is constructed based on the GNSS positioning accuracy degradation level and the V2X communication quality degradation level. Multiple sets of parameter combinations are selected in the multi-domain joint attenuation parameter space. The corresponding GNSS attenuation parameter and V2X attenuation parameter are determined according to each set of parameter combinations. The GNSS analog signal and the V2X radio frequency signal are attenuated according to the corresponding GNSS attenuation parameter and V2X attenuation parameter. Under the same test time reference, the attenuated GNSS analog signal corresponding to each parameter combination is output to the connected intelligent driving vehicle under test, and the attenuated V2X radio frequency signal corresponding to each parameter combination is injected into the on-board unit (OBU) to collect the test data corresponding to each parameter combination. Based on the test data corresponding to each set of parameters, the multi-domain joint degradation critical parameter combination for the connected intelligent driving function of the tested connected intelligent driving vehicle to change from a normal state to a degraded state or a failure state is determined.

8. A multi-domain signal coordinated connected intelligent driving test system, characterized in that, include: The scene generation module is used to acquire the real-time pose information of the connected intelligent driving vehicle under test in the test field, and generate a virtual traffic scene containing multiple virtual connected vehicles based on the real-time pose information, and determine the environmental feature information and motion state information of each virtual connected vehicle corresponding to the virtual traffic scene. The signal generation module is used to generate a Global Navigation Satellite System (GNSS) analog signal based on the real-time pose information, and to convert the motion state information into a vehicle-to-everything (V2X) radio frequency signal. The attenuation processing module is used to determine GNSS attenuation parameters based on the environmental feature information and the real-time pose information through a multi-domain signal collaborative attenuation model, using the same set of environmental feature information as the driving source, and V2X attenuation parameters based on the environmental feature information, the real-time pose information and the motion state information, and to perform attenuation processing on the GNSS analog signal and the V2X radio frequency signal according to the GNSS attenuation parameters and the V2X attenuation parameters respectively. The signal coordination module is used to output the attenuated GNSS analog signal to the connected intelligent driving vehicle under test under the same test time reference, and to inject the attenuated V2X radio frequency signal into the on-board unit (OBU) of the connected intelligent driving vehicle under test; wherein, the output power of the GNSS analog signal is set to be higher than the power of the real satellite signal over the test field; The evaluation generation module is used to collect test data of the tested connected intelligent driving vehicle under attenuation processing of GNSS analog signals and V2X radio frequency signals, and generate test evaluation results.

9. A computer device, characterized in that, Includes processors and computer-readable storage media; The processor is adapted to execute computer programs; The computer-readable storage medium stores a computer program, which, when executed by the processor, implements the multi-domain signal coordination networked intelligent driving test method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the multi-domain signal coordination networked intelligent driving test method as described in any one of claims 1 to 7.

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