用智能搜索算法高效测试自动驾驶车辆在危险场景下的表现
Efficient Safety Testing of Autonomous Vehicles via Adaptive Search over Crash-Derived Scenarios
- 基于真实车祸数据构建危险场景，用自适应算法快速生成测试用例
- 覆盖率达96.83%的事故场景和92.07%的接近事故场景
- 适合自动驾驶安全验证团队使用，显著优于传统测试方法
确保自动驾驶车辆（AV）的安全性在其研发与部署中至关重要。安全关键场景带来更大挑战，亟需高效的测试方法来验证其安全性。本研究聚焦于设计一种加速测试算法，用于评估自动驾驶系统在安全关键场景中的表现。首先，从中国深度交通安全研究-交通事故（CIMSS-TA）数据库中提取典型逻辑场景，并通过重构获得碰撞前特征。其次，集成百度阿波罗（Baidu Apollo）这一先进的黑箱自动驾驶系统（ADS），控制主车行为。第三，提出一种自适应大变邻域-模拟退火算法（ALVNS-SA），以加速测试过程。实验结果表明，采用ALVNS-SA后，测试效率显著提升：安全关键场景覆盖率高达84.00%，其中事故场景覆盖96.83%，近事故场景覆盖92.07%。相比遗传算法（GA）、自适应大邻域-模拟退火算法（ALNS-SA）及随机测试，ALVNS-SA在安全关键场景覆盖上具有明显优势。
原文摘要 · Abstract (English)
Ensuring the safety of autonomous vehicles (AVs) is paramount in their development and deployment. Safety-critical scenarios pose more severe challenges, necessitating efficient testing methods to validate AVs safety. This study focuses on designing an accelerated testing algorithm for AVs in safety-critical scenarios, enabling swift recognition of their driving capabilities. First, typical logical scenarios were extracted from real-world crashes in the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database, obtaining pre-crash features through reconstruction. Second, Baidu Apollo, an advanced black-box automated driving system (ADS) is integrated to control the behavior of the ego vehicle. Third, we proposed an adaptive large-variable neighborhood-simulated annealing algorithm (ALVNS-SA) to expedite the testing process. Experimental results demonstrate a significant enhancement in testing efficiency when utilizing ALVNS-SA. It achieves an 84.00% coverage of safety-critical scenarios, with crash scenario coverage of 96.83% and near-crash scenario coverage of 92.07%. Compared to genetic algorithm (GA), adaptive large neighborhood-simulated annealing algorithm (ALNS-SA), and random testing, ALVNS-SA exhibits substantially higher coverage in safety-critical scenarios.
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