Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems. We propose efficient mechanisms to both characterize and generate testing scenarios using a state-of-the-art driving simulator. For any scenario, our method generates a set of possible driving paths and identifies all the possible safe driving trajectories that can be taken starting at different times, to compute metrics that quantify the complexity of the scenario. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project, as well as adversarial scenarios generated in simulation. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident. We demonstrate a strong correlation between the proposed metrics and human intuition.
翻译:从真实世界数据中提取有趣的情景以及产生故障案例,对于自主系统的开发和测试非常重要。我们建议采用高效机制,使用最先进的驾驶模拟器来描述和生成测试情景。对于任何情景,我们的方法都会产生一套可能的驾驶路径,并查明从不同时间开始的所有可能的安全驾驶轨迹,以计算量化情景复杂性的尺度。我们使用我们的方法来描述下一代模拟(NGSIM)项目以及模拟中产生的对抗性情景中的真实驾驶数据。我们根据避免事故的复杂性来界定这些情景,并深入了解AV如何将发生事故的可能性降到最低。我们展示了拟议指标与人类直觉之间的密切关联。