Method and device for testing communication efficiency of intelligent assisted driving vehicle

Abstract

The embodiment of the application provides a kind of intelligent auxiliary driving vehicle communication efficiency test method and device, the method comprises: first traffic duration data is the traffic duration data of test vehicle under intelligent auxiliary driving mode;Second traffic duration data is the traffic duration data of test vehicle under manual driving mode;According to the first traffic duration data and the second traffic duration data of test vehicle in each road type section Comprehensive vehicle traffic efficiency index is determined;According to the evaluation standard corresponding to the preset communication efficiency level of comprehensive vehicle traffic efficiency index Determine target vehicle communication efficiency level;Comprehensive vehicle traffic efficiency index and target vehicle communication efficiency level are determined as the vehicle communication efficiency test result corresponding to the vehicle brand of target vehicle brand.It can be established by the application to cover complex urban road environment, fusion multi-scene, multi-dimensional traffic efficiency quantitative evaluation system, meet the demand of market to the objective of system comprehensive performance, comparable evaluation.

Description

Technical Field

[0001] This invention relates to the field of intelligent assisted driving technology, and more specifically, to a method and apparatus for testing vehicle communication efficiency in intelligent assisted driving. Background Technology

[0002] In recent years, with the rapid evolution of intelligent assisted driving technology, the relevant testing and evaluation system has gradually extended from functional definition to systematic performance verification.

[0003] However, the existing testing and evaluation system still focuses on static or quasi-static performance verification under idealized road conditions. It has not yet established a quantitative evaluation system for traffic efficiency that covers complex urban road environments and integrates multiple scenarios and dimensions, making it difficult to meet the market's demand for objective and comparable evaluation of the system's overall performance. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a vehicle communication efficiency testing method and device for intelligent assisted driving, which can establish a quantitative evaluation system for traffic efficiency that covers complex urban road environments and integrates multiple scenarios and dimensions, so as to meet the market's demand for objective and comparable evaluation of the system's overall performance.

[0005] In a first aspect, embodiments of this application provide a method for testing the vehicle communication efficiency of intelligent assisted driving, the method comprising: Obtain first and second travel time data for each test vehicle under the target vehicle brand on each road type and road segment of a preset test route; the first travel time data is the travel time data of the corresponding test vehicle in intelligent assisted driving mode; the second travel time data is the travel time data of the corresponding test vehicle in manual driving mode; the preset test route is a real route that meets the preset route complexity conditions; Based on the first travel time data and the second travel time data of each test vehicle in each road type segment, the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand is determined; The target vehicle communication efficiency level is determined based on the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand and the evaluation criteria corresponding to the preset communication efficiency level. The comprehensive vehicle traffic efficiency index and the target vehicle communication efficiency level are determined as the vehicle communication efficiency test results corresponding to the target vehicle brand.

[0006] In one possible implementation, determining the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand based on the first travel time data and the second travel time data corresponding to each test vehicle in each road type segment includes: For each road type segment, based on the first travel time data and the second travel time data of each test vehicle corresponding to the road type segment, the travel efficiency coefficient of the target vehicle brand under the road type segment is determined; Based on the traffic efficiency coefficient of the target vehicle brand across all road types and road sections, determine the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand.

[0007] In one possible implementation, determining the traffic efficiency coefficient of the target vehicle brand under the road type segment based on the first travel time data and the second travel time data of each test vehicle corresponding to the road type segment includes: Based on the first travel time data of each test vehicle corresponding to the road type segment, calculate the first average travel time of the target vehicle brand corresponding to the road type segment in intelligent assisted driving mode; Based on the second travel time data of each test vehicle corresponding to the road type segment, calculate the second average travel time of the target vehicle brand corresponding to the road type segment in manual driving mode; Based on the first average travel time and the second average travel time corresponding to the road type and road segment, the traffic efficiency coefficient of the target vehicle brand under the road type and road segment is determined.

[0008] In one possible implementation, determining the target vehicle communication efficiency level corresponding to the target vehicle brand based on the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand and the evaluation criteria corresponding to the preset communication efficiency level includes: If the comprehensive vehicle traffic efficiency index is less than the first preset communication efficiency threshold, then the target vehicle communication efficiency level is excellent. If the comprehensive vehicle traffic efficiency index is greater than or equal to the first preset communication efficiency threshold and less than or equal to the second preset communication efficiency threshold, then the target vehicle communication efficiency level is good. If the comprehensive vehicle traffic efficiency index is greater than the second preset communication efficiency threshold and less than or equal to the third preset communication efficiency threshold, then the target vehicle communication efficiency level is qualified. If the comprehensive vehicle traffic efficiency index is greater than the third preset communication efficiency threshold, then the target vehicle communication efficiency level is unqualified.

[0009] In one possible implementation, the method further includes: Obtain the first total travel time and the second total travel time of each test vehicle under the target vehicle brand on the preset test route; the first total travel time is the total travel time of the corresponding test vehicle in intelligent assisted driving mode; the second travel time data is the total travel time of the corresponding test vehicle in manual driving mode; The total traffic efficiency coefficient of the target vehicle brand is determined based on the first total traffic duration and the second total traffic duration. The comprehensive vehicle traffic efficiency index, the target vehicle communication efficiency level, and the total traffic efficiency coefficient are determined as the vehicle communication efficiency test results corresponding to the target vehicle brand.

[0010] Secondly, embodiments of this application also provide a vehicle communication efficiency testing device for intelligent assisted driving, the device comprising: The acquisition module is used to acquire first and second travel time data for each test vehicle under the target vehicle brand on each road type segment of a preset test route; the first travel time data is the travel time data of the corresponding test vehicle in intelligent assisted driving mode; the second travel time data is the travel time data of the corresponding test vehicle in manual driving mode; the preset test route is a real route that meets the preset route complexity conditions; The determination module is used to determine the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand based on the first travel time data and the second travel time data of each test vehicle in each road type segment. The determining module is further configured to determine the target vehicle communication efficiency level corresponding to the target vehicle brand based on the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand and the evaluation standard corresponding to the preset communication efficiency level. The determining module is further configured to determine the comprehensive vehicle traffic efficiency index and the target vehicle communication efficiency level as the vehicle communication efficiency test result corresponding to the target vehicle brand.

[0011] In one possible implementation, the determining module is specifically used to determine, for each road type segment, the traffic efficiency coefficient of the target vehicle brand under the road type segment based on the first travel time data and the second travel time data of each test vehicle under the road type segment; and to determine the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand based on the traffic efficiency coefficient of the target vehicle brand under all road type segments.

[0012] In one possible implementation, the determining module is specifically configured to: calculate the first average travel time of the target vehicle brand in the intelligent assisted driving mode on the road segment based on the first travel time data of each test vehicle in the road segment corresponding to the road type; calculate the second average travel time of the target vehicle brand in the manual driving mode on the road segment based on the second travel time data of each test vehicle in the road segment corresponding to the road type; and determine the traffic efficiency coefficient of the target vehicle brand on the road segment based on the first average travel time and the second average travel time corresponding to the road segment.

[0013] In one possible implementation, the determining module is specifically configured to: if the comprehensive vehicle traffic efficiency index is less than a first preset communication efficiency threshold, then the target vehicle communication efficiency level is excellent; if the comprehensive vehicle traffic efficiency index is greater than or equal to the first preset communication efficiency threshold and less than or equal to a second preset communication efficiency threshold, then the target vehicle communication efficiency level is good; if the comprehensive vehicle traffic efficiency index is greater than the second preset communication efficiency threshold and less than or equal to a third preset communication efficiency threshold, then the target vehicle communication efficiency level is qualified; if the comprehensive vehicle traffic efficiency index is greater than the third preset communication efficiency threshold, then the target vehicle communication efficiency level is unqualified.

[0014] In one possible implementation, the acquisition module is further configured to acquire a first total travel time and a second total travel time for each test vehicle under the target vehicle brand on a preset test route; the first total travel time is the total travel time of the corresponding test vehicle in intelligent assisted driving mode; the second travel time is the total travel time of the corresponding test vehicle in manual driving mode; the determination module is further configured to determine the total traffic efficiency coefficient of the target vehicle brand based on the first total travel time and the second total travel time; the determination module is further configured to determine the comprehensive vehicle traffic efficiency index, the target vehicle communication efficiency level, and the total traffic efficiency coefficient as the vehicle communication efficiency test result corresponding to the target vehicle brand.

[0015] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the vehicle communication efficiency testing method for intelligent assisted driving as described in any of the first aspects.

[0016] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the vehicle communication efficiency testing method for intelligent assisted driving as described in any of the first aspects.

[0017] This application provides a method and apparatus for testing vehicle communication efficiency in intelligent assisted driving. The method includes: first travel time data, which is the travel time data of the corresponding test vehicle in intelligent assisted driving mode; second travel time data, which is the travel time data of the corresponding test vehicle in manual driving mode; determining a comprehensive vehicle traffic efficiency index corresponding to a target vehicle brand based on the first and second travel time data of each test vehicle on various road types and road segments; determining a target vehicle communication efficiency level corresponding to the target vehicle brand based on the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand and the evaluation criteria corresponding to the preset communication efficiency level; and determining the comprehensive vehicle traffic efficiency index and the target vehicle communication efficiency level as the vehicle communication efficiency test result corresponding to the target vehicle brand. This application enables the establishment of a quantitative evaluation system for traffic efficiency that covers complex urban road environments, integrates multiple scenarios, and multiple dimensions, meeting the market's demand for objective and comparable evaluation of the system's overall performance. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced 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.

[0019] Figure 1 A flowchart of a vehicle communication efficiency testing method for intelligent assisted driving provided in an embodiment of this application is shown; Figure 2 A flowchart illustrating the determination of the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand provided in this application embodiment is shown. Figure 3 A flowchart is shown below illustrating another vehicle communication efficiency testing method for intelligent assisted driving provided in an embodiment of this application; Figure 4 This illustration shows a structural schematic diagram of a vehicle communication efficiency testing device for intelligent assisted driving provided in an embodiment of this application; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0020] 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. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0021] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] To enable those skilled in the art to utilize the content of this application, and in conjunction with the specific application scenario of "intelligent assisted driving technology," the following embodiments are provided. For those skilled in the art, the general principles defined herein can be applied to other embodiments and application scenarios without departing from the spirit and scope of this application. Although this application is primarily described within the "intelligent assisted driving technology field," it should be understood that this is merely an exemplary embodiment.

[0023] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0024] The following is a detailed description of a vehicle communication efficiency testing method for intelligent assisted driving provided in the embodiments of this application.

[0025] Reference Figure 1 The diagram shown is a flowchart illustrating a vehicle communication efficiency testing method for intelligent assisted driving provided in this application embodiment. The exemplary steps of this application embodiment are described below: S101. Obtain the first and second travel time data for each test vehicle under the target vehicle brand on each road type segment of the preset test route.

[0026] In this application embodiment, the preset test route is a real route that meets the complex conditions of the preset route. The setting method is as follows: select a real physical route that is "repeatable, replicable and highly comparable" as the preset test route; the preset test route is a real route that meets the complex conditions of the preset route, such as including four types of typical road sections: urban congested roads, urban expressways, roundabouts and ramps.

[0027] The total length of the preset test route, the proportion of each road type in the preset test route, the speed limit, the location and cycle of traffic lights, etc., are all fixed.

[0028] For example, the starting point A and ending point B of the preset test route are representative landmarks in the city (such as a city center square to a suburban industrial park) to ensure that the route has real-world traffic significance. The total length of the preset test route can be set according to actual test conditions; in this embodiment, it is 20km. Of this, 5km (25%) is urban congested roads, 10km (50%) is urban expressways, 1km (5%) is roundabouts, and 4km (20%) is ramps, satisfying the typical urban travel path structure.

[0029] Here, the standardization of route parameters is key to achieving cross-brand and cross-model horizontal comparisons. All tests are conducted under identical traffic conditions, including weather conditions (sunny, no rain), traffic flow (e.g., an average traffic volume of 200 vehicles / km on congested roads), and road surface conditions (dry, smooth). The route design complies with the basic principles of test environment control in GB / T 44461.1-2024 and GB / T44461.2-2024, but further extends to complex urban multi-scenario integrated environments, making up for the shortcomings of existing standards in covering real traffic structures.

[0030] To eliminate the impact of individual differences on the evaluation results, at least three intelligent assisted driving vehicles of the same model and configuration are selected as test vehicles when conducting traffic efficiency tests on a specific target vehicle brand (referring to the vehicle brand under test). This ensures that the hardware and software versions of the test vehicles are consistent. All test vehicles are driven by professional drivers holding a C1 driver's license with more than five years of urban driving experience, ensuring the stability and representativeness of their driving behavior. Drivers receive standardized training before the test to familiarize themselves with the test procedures and routes, avoiding time deviations due to differences in operating habits. To ensure the fairness and repeatability of the test, multiple sets of tests (e.g., five sets) are conducted for the target vehicle brand.

[0031] In each test, each test vehicle completed one passage on a preset test route using intelligent assisted driving mode and another using manual driving mode. To avoid driver fatigue or vehicle overheating affecting the test results, the interval between the same driver driving a test vehicle was greater than or equal to the preset minimum interval (e.g., 1 hour; for example, if a driver finishes a passage on the preset test route at 9:00, then that driver can only start the next passage on the preset test route after 10:00). Furthermore, the same test vehicle was operated by the same driver.

[0032] During the test, the first and second travel times for each test vehicle of the target vehicle brand were recorded for each road type and segment of the preset test route. The first travel time data represents the travel time of the corresponding test vehicle in intelligent assisted driving mode; the second travel time data represents the travel time of the corresponding test vehicle in manual driving mode. The first travel time data includes the first travel time for each test vehicle in intelligent assisted driving mode for each test group; the second travel time data includes the second travel time for each test vehicle in intelligent assisted driving mode for each test group.

[0033] In addition, the first total travel time and the second total travel time of the test vehicle on the preset test route were also recorded during the test. The first total travel time is the total travel time of the corresponding test vehicle in intelligent assisted driving mode; the second travel time is the total travel time of the corresponding test vehicle in manual driving mode.

[0034] For example, in each round of passage, the total travel time of the test vehicle from point A to point B is automatically recorded. Second total travel time The system records segmented time data according to road type and road segment, i.e., the first travel time data, such as the first and second travel times on congested roads, urban expressways, roundabouts, and ramps. The segmented time is based on the geographical boundaries of each road type and road segment in the preset test route, and is automatically identified through GPS positioning or lane-level navigation information.

[0035] Optionally, to ensure data accuracy, all duration data is cleaned after collection to remove outliers (such as sudden time changes caused by temporary parking or emergency avoidance).

[0036] S102. Based on the first and second travel time data of each test vehicle on each road type and road segment, determine the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand.

[0037] In this embodiment, the comprehensive vehicle traffic efficiency index is used to comprehensively evaluate the overall efficiency of the intelligent assisted driving system by combining the test vehicle's traffic conditions on various road types and sections. The smaller the value of the comprehensive vehicle traffic efficiency index, the better the vehicle communication efficiency of the target vehicle brand.

[0038] Specifically, refer to Figure 2 The diagram shown is a flowchart for determining the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand provided in this application embodiment. The implementation steps are as follows: S201. For each road type segment, based on the first and second travel time data of each test vehicle in that road type segment, determine the traffic efficiency coefficient of the target vehicle brand in that road type segment.

[0039] In this application embodiment, the traffic efficiency coefficient of the target vehicle brand under any road type is determined by the following steps: Step 1: Based on the first travel time data of each test vehicle on the road segment of this road type, calculate the first average travel time of the target vehicle brand on the road segment of this road type in intelligent assisted driving mode.

[0040] In this embodiment, the first travel time data of each test vehicle corresponding to the road type segment is substituted into the following formula to calculate the first average travel time of the target vehicle brand corresponding to the road type segment in intelligent assisted driving mode: ; in, The first average travel time for the target vehicle brand on road type and road segment n in intelligent assisted driving mode; The number of test groups; The number of vehicles tested; In the first travel time data, the first travel time of the kth test vehicle under the m-th test group is the first travel time corresponding to road type segment n.

[0041] Step 2: Based on the second travel time data of each test vehicle for this road type and segment, calculate the second average travel time of the target vehicle brand for this road type and segment in manual driving mode.

[0042] In this embodiment, the second travel time data of each test vehicle corresponding to this road type segment is substituted into the following formula to calculate the second average travel time of the target vehicle brand corresponding to this road type segment in intelligent assisted driving mode: ; in, The second average travel time for the target vehicle brand on road type and road segment n in manual driving mode; The number of test groups; The number of vehicles tested; In the second travel duration data, the second travel duration of the kth test vehicle under the m-th test group is the second travel duration corresponding to road type segment n.

[0043] Step 3: Determine the traffic efficiency coefficient of the target vehicle brand under the road type and road segment based on the first average travel time and the second average travel time corresponding to the road segment.

[0044] In this embodiment of the application, the first average travel time and the second average travel time corresponding to the road segment of this road type are substituted into the following formula to obtain the traffic efficiency coefficient of the target vehicle brand under this road segment: .

[0045] in, The traffic efficiency coefficient of the target vehicle brand under road type and road segment n.

[0046] Traffic efficiency coefficient is used to analyze the efficiency performance of intelligent assisted driving systems in different typical scenarios (such as urban roads, highways, congested sections, suburban roads, etc.) to accurately identify their strengths and weaknesses.

[0047] S202. Based on the traffic efficiency coefficient of the target vehicle brand under all road types and road sections, determine the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand.

[0048] In this application embodiment, the traffic efficiency coefficient of the target vehicle brand under all road types and road segments is substituted into the following formula to obtain the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand (combining the importance (weight) of different segment scenarios to comprehensively evaluate the overall efficiency of the intelligent assisted driving system, which is more targeted and objective than a single total traffic efficiency coefficient).

[0049] ; in, The comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand; The importance weight is the weight corresponding to road segment n of road type.

[0050] S103. Determine the target vehicle communication efficiency level corresponding to the target vehicle brand based on the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand and the evaluation criteria corresponding to the preset communication efficiency level.

[0051] In this application embodiment, the evaluation criteria corresponding to the preset communication efficiency level are verified based on actual test data of multiple brands of vehicles in the embodiment, and have good distinguishability and practicality.

[0052] Specifically, if the comprehensive vehicle traffic efficiency index is less than the first preset communication efficiency threshold (e.g., 0.9), then the target vehicle communication efficiency level is excellent—the overall traffic efficiency of intelligent assisted driving is significantly better than that of manual driving. Specifically, if the comprehensive vehicle traffic efficiency index is greater than or equal to the first preset communication efficiency threshold (e.g., 0.9) and less than or equal to the second preset (e.g., 1.1) communication efficiency threshold, then the target vehicle communication efficiency level is good—the traffic efficiency is comparable to that of manual driving, and the performance is stable. Specifically, if the comprehensive vehicle traffic efficiency index is greater than the second preset communication efficiency threshold (e.g., 1.1) and less than or equal to the third preset communication efficiency threshold (e.g., 1.3), then the target vehicle communication efficiency level is qualified—the traffic efficiency is slightly lower than that of manual driving, and there is room for optimization. Specifically, if the comprehensive vehicle traffic efficiency index is greater than the third preset communication efficiency threshold (e.g., 1.3), then the target vehicle communication efficiency level is unqualified—the traffic efficiency is significantly lower than that of manual driving, and there are obvious defects in the system decision-making or control logic.

[0053] S104. The comprehensive vehicle traffic efficiency index and the target vehicle communication efficiency level are used to determine the vehicle communication efficiency test result corresponding to the target vehicle brand.

[0054] Reference Figure 3 The diagram shown is a flowchart of another vehicle communication efficiency testing method for intelligent assisted driving provided in this application embodiment. The implementation steps are as follows: S301. Obtain the first total travel time and the second total travel time for each test vehicle under the target vehicle brand on the preset test route.

[0055] In this embodiment of the application, the first total travel time is the total travel time of the corresponding test vehicle in the intelligent assisted driving mode; the second travel time data is the total travel time of the corresponding test vehicle in the manual driving mode.

[0056] S302. Determine the total traffic efficiency coefficient of the target vehicle brand based on the first total traffic duration and the second total traffic duration.

[0057] In this embodiment of the application, the first total travel time is... Second total travel time Substituting into the following formula, we obtain the overall traffic efficiency coefficient for the target vehicle brand. : .

[0058] meaning: <1: Intelligent assisted driving has better overall traffic efficiency than manual driving; >1: Intelligent assisted driving has a lower overall traffic efficiency than manual driving; =1: The overall efficiency of the two modes is comparable.

[0059] In addition, when generating vehicle communication efficiency test reports for target vehicle brands, the report not only outputs the comprehensive vehicle traffic efficiency index, the target vehicle communication efficiency level, and the total traffic efficiency coefficient, but also analyzes the efficiency performance of each brand in different road scenarios by combining the traffic efficiency coefficients of the target vehicle brand under various road types and road sections. For example, Brand A performs well on expressways and congested roads, but its efficiency is slightly lower in roundabouts; Brand B's efficiency drops significantly in roundabout scenarios, etc., providing precise guidance for the optimization of intelligent assisted driving.

[0060] S303. The comprehensive vehicle traffic efficiency index, the target vehicle communication efficiency level, and the total traffic efficiency coefficient are determined as the vehicle communication efficiency test results corresponding to the target vehicle brand.

[0061] Optionally, embodiments of this application also establish a complete multi-source data synchronous acquisition system to ensure comprehensive recording and restoration of the entire testing process. The acquisition equipment includes: (1) Vehicle bus data acquisition equipment: Read CAN bus signals in real time through OBD-II interface or dedicated data acquisition device, and record throttle opening, brake pressure, steering angle, gear status, ADAS system status (such as whether SACC is activated, hands-free detection status) and system exit reason; (2) Driver monitoring system: Subject to compliance with privacy protection regulations, use in-vehicle cameras or eye trackers to collect the driver's gaze direction, the time his hands are off the steering wheel, whether a takeover request is made and its response time; (3) Environmental perception data recording: Record the recognition results and confidence levels of the vehicle's own sensors (such as millimeter-wave radar, lidar, and cameras) on the status of surrounding vehicles, pedestrians, non-motorized vehicles, and traffic lights, which are used to analyze the rationality of the system's decision-making logic.

[0062] In summary, the embodiments of this application, by constructing a complete technical chain of unified route, dual-mode comparison, multi-source data fusion and weighted index evaluation, have for the first time achieved a quantitative, objective and comparable evaluation of the traffic efficiency of intelligent assisted driving systems in real and complex urban road environments, which has significant technical advantages and broad application prospects.

[0063] Example 1: Typical Application Scenarios This embodiment, based on a standardized evaluation route set in the user's plan, simulates a real urban commuting scenario to verify the feasibility and effectiveness of the invention in a typical complex urban road environment. The evaluation route starts at People's Square in Changchun City (a landmark transportation hub) and ends at a nearby industrial park (a typical commuting endpoint), with a total distance of 20km. The route structure and parameters strictly follow the user's plan: 5km of congested urban roads (25%), 10km of urban expressways (50%), 1km of roundabouts (5%), and 4km of ramps (20%). Speed ​​limits for each section are: 40km / h for congested roads, 80km / h for expressways, 30km / h for roundabouts, and 50km / h for ramps. Traffic lights are located at the intersections of congested roads and expressways, with a cycle of 90 seconds (60 seconds of green light and 30 seconds of red light). The traffic light timings are fixed to ensure consistent test conditions.

[0064] The vehicles evaluated were three mid-size sedans from brands A, B, and C, all equipped with Level 2 intelligent driver assistance systems. Three vehicles of the same model and configuration from each brand were selected, all from the year 2023, and all software versions were updated to the latest release. Test drivers were professional testers holding a C1 driver's license and possessing over 5 years of urban driving experience, who underwent standardized training before performing the testing tasks. The tests were conducted in clear, rain-free, and wind-free weather conditions, with average traffic flow on congested roads controlled at 200 vehicles / km (typical of peak urban traffic), and the road surface was dry, smooth, and free of construction or temporary traffic control.

[0065] The test procedure is as follows: Each vehicle first completes the route 5 times in intelligent assisted driving mode, with an interval of no less than 1 hour between each attempt to avoid vehicle overheating and driver fatigue; then, the same driver completes the same route 5 times in manual driving mode. Throughout the process, CAN bus signals (throttle opening, brake pressure, steering angle, ADAS status, etc.) are synchronously recorded via the OBD data acquisition device. The in-vehicle camera (compliant with the Personal Information Protection Law and the Several Provisions on Automobile Data Security Management) collects the driver's gaze direction, the time their hands leave the steering wheel, and the response time to take over requests. Vehicle sensors record the recognition results of surrounding vehicles, pedestrians, and traffic lights. All time data is automatically segmented and recorded by the GPS positioning system, with segment boundaries precisely defined based on lane-level navigation information. See Table 1 for the test results provided in this embodiment.

[0066] Table 1

[0067] The test results above show that Brand A performs excellently in expressway and congested road scenarios, with an average total travel time of 32.5 minutes in intelligent assisted driving mode, better than the 35.8 minutes in manual driving mode, with a comprehensive efficiency index E≈0.902 and an evaluation level of "good". Brand B's efficiency decreases in roundabout and congested road scenarios, with E≈1.047, but its overall efficiency is comparable to manual driving. Brand C performs outstandingly in ramp scenarios, with intelligent assisted driving mode taking 3.7 minutes, 0.4 minutes shorter than manual driving, with E≈0.972, and its overall efficiency is close to that of manual driving. This embodiment verifies the feasibility of achieving objective and comparable evaluation in typical urban commuting scenarios.

[0068] Example 2: Expanding application scenarios This embodiment extends the method to "dynamic adaptability testing under multiple time periods and multiple traffic flow conditions" to verify its evaluation robustness (stability, reliability, and durability) and practicality under different traffic conditions. The test route remains the standard 20km route from point A (city center square) to point B (industrial park), with the road segment composition and speed limit configuration unchanged. However, the test environment is divided into three phases: "morning peak hours (7:30–9:00)," "off-peak hours (11:00–13:00)," and "evening peak hours (17:30–19:30)," with data collected separately for each period to simulate the impact of different traffic flows on the efficiency of the intelligent assisted driving system.

[0069] During the morning rush hour, the average traffic flow on congested roads increased to 300 vehicles per kilometer, with increased traffic density on expressways and brief queues at roundabouts and ramps. During off-peak hours, traffic flow remained stable at 150 vehicles per kilometer, with smooth traffic. During the evening rush hour, traffic flow rebounded to 280 vehicles per kilometer, with traffic light cycles significantly impacting traffic efficiency. The test still selected three vehicles from brands A, B, and C, with three vehicles from each brand. The same driver completed five passages each in intelligent assisted driving and manual driving modes during the three time periods, for a total of 90 tests.

[0070] The data acquisition method is consistent with that of Example 1, including the synchronous recording of CAN bus signals, driver monitoring data, and environmental perception data. Specifically, this example focuses on analyzing changes in the system's decision-making logic under high-density traffic flow: such as whether ACC frequently starts and stops, whether LKA frequently corrects its direction, and whether it brakes suddenly due to a vehicle changing lanes ahead. Analyzing this data can further explain the reasons for changes in efficiency indicators.

[0071] Test results show that Brand A's average total travel time in intelligent assisted driving mode during morning rush hour was 41.2 minutes, a reduction of 3.6 minutes compared to 44.8 minutes for manual driving, with K≈0.92 and E≈0.91, maintaining its efficiency advantage. Brand B's efficiency was slightly lower than manual driving during peak hours due to frequent lane changes and fluctuations in following other vehicles, with K rising to 1.12 and E≈1.09. Brand C performed best during off-peak hours, with E≈0.95, but its efficiency increased to 1.06 during evening rush hour due to lane-changing hesitation caused by delayed ramp recognition. This example demonstrates that the method of the present invention can not only evaluate the system's efficiency under ideal conditions but also reveal its adaptability differences in dynamic traffic environments, providing a key basis for system optimization.

[0072] Example 3: Application in special scenarios This embodiment applies the method of the present invention to a "special assessment of highly complex traffic structures," focusing on "urban interchange ramp groups and multi-roundabout complex areas" to test its evaluation capability under extremely complex scenarios. The test route was adjusted as follows: point A is an intersection of urban main roads, and point B is another intersection of main roads, with a total length of 25km. Among them, 6km are congested urban roads (accounting for 24%), 12km are urban expressways (accounting for 48%), 2km are roundabouts (accounting for 8%), and 5km are ramps (accounting for 20%). The route includes 2 roundabouts and 3 ramp intersections, forming a typical "interchange + roundabout" complex structure.

[0073] The test vehicles consisted of three models from brands A, B, and C, all equipped with Level 2 intelligent assisted driving systems, with three vehicles from each brand and the same driver. Tests were conducted in clear weather during off-peak hours, with traffic flow controlled at 180 vehicles per kilometer to ensure the system had sufficient decision-making space. The focus was on collecting behavioral data from the system during challenging maneuvers such as merging onto ramps, changing lanes within roundabouts, and continuous lane changes across multiple lanes.

[0074] In terms of data acquisition, in addition to conventional CAN bus and driver monitoring data, high-precision environmental perception data recording is specially enabled: including lidar point cloud, millimeter-wave radar target trajectory, and camera recognition results of traffic lights and lane lines, with a timestamp accuracy of 1ms, supporting in-depth analysis of system decision delay and path planning rationality.

[0075] Test results showed that Brand A could identify the target lane in advance and smoothly merge when merging into the ramp, with an average total travel time of 38.7 minutes in intelligent assisted driving mode and 40.5 minutes in manual driving mode, K≈0.956, E≈0.93, demonstrating excellent efficiency. Brand B frequently changed lanes within the roundabout, causing the system to frequently exit LKA, K≈1.18, E≈1.15, resulting in a significant decrease in efficiency. Brand C exhibited a "detour" phenomenon in multi-ramp intersection areas due to its conservative path planning algorithm, which, while safe, resulted in the lowest efficiency, E≈1.21. This embodiment verifies that the method of the present invention can effectively identify system shortcomings in highly complex traffic structures, possesses the ability to evaluate "atypical scenarios," and provides precise guidance for performance optimization of intelligent assisted driving systems in complex interchange environments.

[0076] All data is recorded synchronously with a unified timestamp and a time accuracy of no less than 10ms to ensure that each data stream is aligned on the timeline, supporting subsequent detailed analysis of intelligent assisted driving behavior and human-machine interaction processes.

[0077] Based on the same inventive concept, this application also provides a vehicle communication efficiency testing device for intelligent assisted driving, which corresponds to the vehicle communication efficiency testing method for intelligent assisted driving. Since the principle of the device in this application is similar to the vehicle communication efficiency testing method for intelligent assisted driving described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0078] Reference Figure 4 The diagram shown is a schematic of a vehicle communication efficiency testing device for intelligent assisted driving provided in an embodiment of this application. The vehicle communication efficiency testing device for intelligent assisted driving includes: The acquisition module 401 is used to acquire first and second travel time data for each test vehicle under the target vehicle brand on each road type segment of a preset test route; the first travel time data is the travel time data of the corresponding test vehicle in intelligent assisted driving mode; the second travel time data is the travel time data of the corresponding test vehicle in manual driving mode; the preset test route is a real route that meets the preset route complexity conditions; The determining module 402 is used to determine the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand based on the first travel time data and the second travel time data corresponding to each test vehicle in each road type segment. The determining module 402 is further configured to determine the target vehicle communication efficiency level corresponding to the target vehicle brand based on the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand and the evaluation standard corresponding to the preset communication efficiency level. The determining module 402 is further configured to determine the comprehensive vehicle traffic efficiency index and the target vehicle communication efficiency level as the vehicle communication efficiency test result corresponding to the target vehicle brand.

[0079] like Figure 5 As shown in the embodiment of this application, an electronic device 500 includes a processor 501, a memory 502, and a bus. The memory 502 stores machine-readable instructions that can be executed by the processor 501. When the electronic device is running, the processor 501 communicates with the memory 502 via the bus. The processor 501 executes the machine-readable instructions to perform the steps of the vehicle communication efficiency test method for intelligent assisted driving described above.

[0080] Specifically, the memory 502 and processor 501 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 501 runs the computer program stored in the memory 502, it can execute the above-mentioned intelligent assisted driving vehicle communication efficiency test method.

[0081] Corresponding to the above-described vehicle communication efficiency testing method for intelligent assisted driving, this application embodiment also provides a computer-readable storage medium storing a computer program, which, when run by a processor, executes the steps of the above-described vehicle communication efficiency testing method for intelligent assisted driving.

[0082] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0083] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0085] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0086] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for testing communication efficiency of an intelligent assisted driving vehicle, characterized in that, The method includes: Obtain first and second travel time data for each test vehicle under the target vehicle brand on each road type and road segment of a preset test route; the first travel time data is the travel time data of the corresponding test vehicle in intelligent assisted driving mode; the second travel time data is the travel time data of the corresponding test vehicle in manual driving mode; the preset test route is a real route that meets the preset route complexity conditions; Based on the first travel time data and the second travel time data of each test vehicle on each road type and road segment, the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand is determined; The target vehicle communication efficiency level is determined based on the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand and the evaluation criteria corresponding to the preset communication efficiency level. The comprehensive vehicle traffic efficiency index and the target vehicle communication efficiency level are determined as the vehicle communication efficiency test results corresponding to the target vehicle brand.

2. The vehicle communication efficiency testing method for intelligent assisted driving according to claim 1, characterized in that, The step of determining the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand based on the first travel time data and the second travel time data of each test vehicle on each road type segment includes: For each road type segment, based on the first travel time data and the second travel time data of each test vehicle corresponding to the road type segment, the travel efficiency coefficient of the target vehicle brand under the road type segment is determined; Based on the traffic efficiency coefficient of the target vehicle brand across all road types and road sections, determine the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand.

3. The vehicle communication efficiency testing method for intelligent assisted driving according to claim 2, characterized in that, The step of determining the traffic efficiency coefficient of the target vehicle brand under the road type segment based on the first travel time data and the second travel time data of each test vehicle corresponding to the road type segment includes: Based on the first travel time data of each test vehicle corresponding to the road type segment, calculate the first average travel time of the target vehicle brand corresponding to the road type segment in intelligent assisted driving mode; Based on the second travel time data of each test vehicle corresponding to the road type segment, calculate the second average travel time of the target vehicle brand corresponding to the road type segment in manual driving mode; Based on the first average travel time and the second average travel time corresponding to the road type and road segment, the traffic efficiency coefficient of the target vehicle brand under the road type and road segment is determined.

4. The vehicle communication efficiency testing method for intelligent assisted driving according to claim 1, characterized in that, The step of determining the target vehicle communication efficiency level corresponding to the target vehicle brand based on the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand and the evaluation criteria corresponding to the preset communication efficiency level includes: If the comprehensive vehicle traffic efficiency index is less than the first preset communication efficiency threshold, then the target vehicle communication efficiency level is excellent. If the comprehensive vehicle traffic efficiency index is greater than or equal to the first preset communication efficiency threshold and less than or equal to the second preset communication efficiency threshold, then the target vehicle communication efficiency level is good. If the comprehensive vehicle traffic efficiency index is greater than the second preset communication efficiency threshold and less than or equal to the third preset communication efficiency threshold, then the target vehicle communication efficiency level is qualified. If the comprehensive vehicle traffic efficiency index is greater than the third preset communication efficiency threshold, then the target vehicle communication efficiency level is unqualified.

5. The vehicle communication efficiency testing method for intelligent assisted driving according to claim 1, characterized in that, The method further includes: Obtain the first total travel time and the second total travel time of each test vehicle under the target vehicle brand on the preset test route; the first total travel time is the total travel time of the corresponding test vehicle in intelligent assisted driving mode; the second travel time data is the total travel time of the corresponding test vehicle in manual driving mode; The total traffic efficiency coefficient of the target vehicle brand is determined based on the first total traffic duration and the second total traffic duration. The comprehensive vehicle traffic efficiency index, the target vehicle communication efficiency level, and the total traffic efficiency coefficient are determined as the vehicle communication efficiency test results corresponding to the target vehicle brand.

6. A vehicle communication efficiency testing device for intelligent assisted driving, characterized in that, The device includes: The acquisition module is used to acquire first and second travel time data for each test vehicle under the target vehicle brand on each road type segment of a preset test route; the first travel time data is the travel time data of the corresponding test vehicle in intelligent assisted driving mode; the second travel time data is the travel time data of the corresponding test vehicle in manual driving mode; the preset test route is a real route that meets the preset route complexity conditions; The determination module is used to determine the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand based on the first travel time data and the second travel time data of each test vehicle in each road type segment. The determining module is further configured to determine the target vehicle communication efficiency level corresponding to the target vehicle brand based on the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand and the evaluation standard corresponding to the preset communication efficiency level. The determining module is further configured to determine the comprehensive vehicle traffic efficiency index and the target vehicle communication efficiency level as the vehicle communication efficiency test result corresponding to the target vehicle brand.

7. The vehicle communication efficiency testing device for intelligent assisted driving according to claim 6, characterized in that, The determining module is specifically used for: For each road type segment, based on the first travel time data and the second travel time data of each test vehicle corresponding to the road type segment, the travel efficiency coefficient of the target vehicle brand under the road type segment is determined; Based on the traffic efficiency coefficient of the target vehicle brand across all road types and road sections, determine the comprehensive vehicle traffic efficiency index corresponding to the target vehicle brand.

8. The vehicle communication efficiency testing device for intelligent assisted driving according to claim 7, characterized in that, The determining module is specifically used for: Based on the first travel time data of each test vehicle corresponding to the road type segment, calculate the first average travel time of the target vehicle brand corresponding to the road type segment in intelligent assisted driving mode; Based on the second travel time data of each test vehicle corresponding to the road type segment, calculate the second average travel time of the target vehicle brand corresponding to the road type segment in manual driving mode; Based on the first average travel time and the second average travel time corresponding to the road type and road segment, the traffic efficiency coefficient of the target vehicle brand under the road type and road segment is determined.

9. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the vehicle communication efficiency test method for intelligent assisted driving as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the vehicle communication efficiency testing method for intelligent assisted driving as described in any one of claims 1 to 5.

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