Construction of virtual test scenario for intelligent vehicle and pedestrian interaction
To satisfy the testing requirements for intelligent vehicles and pedestrians interaction under urban conditions,a scenario generation method that comprehensively considers the appearance frequency of scenarios in the real world and their challenges to intelligent vehicles performance is proposed.First,the original scenarios are extracted from the natural driving dataset.Then a critical scenario extraction method based on importance sampling theory is designed to extract essential scenarios from the original scenarios according to the accelerated test requirement,and pedestrian crossing road scenarios based on natural driving data are constructed.Finally,comparing the distribution of essential scenarios and original scenarios,the results show that this method can effectively screen out scenarios that may pose challenges to intelligent vehicles safety and it also realizes accelerated testing while retaining the statistical characteristics of test scenarios.
control science and engineeringscenario generationintelligent vehiclenatural driving dataimportance samplingpedestrian
万方数据
维普
