Automated Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the safety and reliability of these systems, extensive testings are being conducted before their future mass deployment. Testing the system on the road is the closest to real-world and desirable approach, but it is incredibly costly. Also, it is infeasible to cover rare corner cases using such real-world testing. Thus, a popular alternative is to evaluate an ADS's performance in some well-designed challenging scenarios, a.k.a. scenario-based testing. High-fidelity simulators have been widely used in this setting to maximize flexibility and convenience in testing what-if scenarios. Although many works have been proposed offering diverse frameworks/methods for testing specific systems, the comparisons and connections among these works are still missing. To bridge this gap, in this work, we provide a generic formulation of scenario-based testing in high-fidelity simulation and conduct a literature review on the existing works. We further compare them and present the open challenges as well as potential future research directions.
翻译:近年来,自动驾驶系统(ADS)取得了迅速的进展。为了确保这些系统的安全和可靠性,目前正在在今后大规模部署之前进行广泛的测试。在公路上测试系统是最接近现实世界和可取的方法,但成本极高。此外,使用这种现实世界测试覆盖罕见的角落情况也是不可行的。因此,一个流行的替代办法是评估ADS在一些设计良好的富有挑战性假设情景(a.k.a.假设情景测试)中的性能。在这个环境中广泛使用高忠诚模拟器,以最大限度地灵活和方便地测试假设情景。虽然已经提出了许多工程建议,为测试具体系统提供了不同的框架/方法,但是这些工程之间的比较和联系仍然缺乏。为了缩小这一差距,我们在这项工作中提供了一种基于假设性测试的通用方法,对现有作品进行文献审查。我们进一步比较了这些假设,并提出了公开的挑战以及潜在的未来研究方向。