Traditional testing methods no longer meet the requirements of ICV testing. Scenario based testing can perfectly solve this problem. The testing scenarios must come from real traffic data, and how to extract valuable scenarios from the massive actual collected data is a key issue. This paper proposes an automated scenario extraction method that accurately identifies five typical scenarios based on LiDAR target data. Based on the fragments of the right of way competition, determine whether the different key parameters of various scenarios have reached the threshold, in order to intercept and output valuable typical scenarios. Finally, the extraction results are verified by stratified sampling method, and the scenario recognition accuracy of the data segment is obtained by weighted calculation. The validation results indicate that the method designed in this paper has extremely high extraction accuracy and can effectively and correctly extract five typical scenarios.
In this paper, a vehicle in the loop simulation test approach of performance evaluation for driving assistance system is proposed, which contains testing model validation method and testing execution procedure. The vehicle dynamics model of the vehicle on the dynamometer is proposed to control the road load simulation that applied by shaft-coupled dynamometer. The method of integrating radar simulator, camera simulator, dynamometer system, and real time machine and scenario software to the vehicle in the closed loop simulation test platform is proposed. The performance of the vehicle with ACC function is evaluated on the simulation test platform, as well as the tests on closed-road. The results demonstrate that the performance of the vehicle tested on the simulation test bench is highly consistent with the results of closed-road. The proposed vehicle in the loop test approach is useful for evaluating the performance in longitudinal scenarios.
With the extensive application of driver fatigue detection system (DFDS) in commercial vehicles, in-depth testing and evaluation of DFDs becomes an urgent need. Based on the experience of commercial vehicle test, a DFDs in the loop test platform is developed. The platform can simulate the external driving environment of vibration and illumination in the laboratory, and a bionic robot is integrated to perform the driver's fatigue behaviors with good repeatability such as closing eyes and yawning. In this paper, the performance of a DFDs is tested on the test platform. The performance of DFDs under different vibration excitation and environmental illumination is analyzed, and the factors affecting the detection rate and recognition rate are found out, which is of great significance to the design and improvement of DFDs. The results show that the vibration excitation will reduce the detection rate, and the direction of illumination will greatly reduce the recognition rate. The combination of vibration and illumination excitation will further reduce the detection rate of driver fatigue action and the recognition rate.
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