面向智能汽车-行人交互的虚拟测试场景构建

Construction of virtual test scenario for intelligent vehicle and pedestrian interaction

针对城市工况下的智能汽车与行人交互测试需求,提出了一种综合考虑场景在真实世界中的出现频率及其对人车交互性能挑战程度的场景生成方法。首先,依据智能汽车与行人交互的关键特征从自然驾驶数据集中提取出行人横穿道路原始场景数据;然后,针对加速测试需求设计了基于重要性采样理论的关键场景提取方法,从原始场景中提取并构建针对智能汽车-行人交互测试的重要场景;最后,通过对重要场景与原始场景的数据分布比较,证明本文方法能够有效筛选出驾驶过程中可能对安全性能带来挑战的场景,从而实现加速测试,同时兼顾测试场景的统计特征。

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.