用智能搜索方法高效覆盖自动驾驶测试中的危险场景，提升安全评估效率。
LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization
- 通过采样密度自适应与束搜索，实现对危险场景子空间的快速聚焦。
- 在二维和五维合成函数上，比随机搜索快33倍和6000倍，95%覆盖率。
- 适合自动驾驶虚拟测试中需全面评估安全性的研究人员和工程师。
基于场景的虚拟测试是评估自动驾驶系统（ADS）安全性的重要方法，但难以穷举逻辑场景空间中的所有具体场景。近年来，黑箱优化（BBO）利用历史测试信息加速测试生成，但单一最优解不足以支撑全面的安全评估。本文认为，逻辑场景空间中所有危险子空间，而非仅最危急的具体场景，更关键。在有限测试次数下覆盖尽可能多的危险场景，被定义为黑箱覆盖（BBC）问题。本文在样本化搜索范式中形式化该问题，并基于混淆矩阵分析构建覆盖标准。提出LAMBDA（潜行动蒙特卡洛束搜索与密度自适应），通过递归划分逻辑场景空间，区分可接受与不可接受区域。相比前代LaMCTS，LAMBDA引入采样密度以克服优化偏差，并采用束搜索提高并行性。实验表明，LAMBDA在所有基线中表现最优，在二维和五维合成函数上分别比随机搜索快33倍和6000倍即可达到95%关键区域覆盖率。结果也验证了其在自动驾驶虚拟测试中具有广阔应用前景。
原文摘要 · Abstract (English)
Scenario-based virtual testing is one of the most significant methods to test and evaluate the safety of automated driving systems (ADSs). However, it is impractical to enumerate all concrete scenarios in a logical scenario space and test them exhaustively. Recently, Black-Box Optimization (BBO) was introduced to accelerate the scenario-based test of ADSs by utilizing the historical test information to generate new test cases. However, a single optimum found by the BBO algorithm is insufficient for the purpose of a comprehensive safety evaluation of ADSs in a logical scenario. In fact, all the subspaces representing danger in the logical scenario space, rather than only the most critical concrete scenario, play a more significant role for the safety evaluation. Covering as many of the critical concrete scenarios in a logical scenario space through a limited number of tests is defined as the Black-Box Coverage (BBC) problem in this paper. We formalized this problem in a sample-based search paradigm and constructed a coverage criterion with Confusion Matrix Analysis. Furthermore, we propose LAMBDA (Latent-Action Monte-Carlo Beam Search with Density Adaption) to solve BBC problems. LAMBDA can quickly focus on critical subspaces by recursively partitioning the logical scenario space into accepted and rejected parts. Compared with its predecessor LaMCTS, LAMBDA introduces sampling density to overcome the sampling bias from optimization and Beam Search to obtain more parallelizability. Experimental results show that LAMBDA achieves state-of-the-art performance among all baselines and can reach at most 33 and 6000 times faster than Random Search to get 95% coverage of the critical areas in 2- and 5-dimensional synthetic functions, respectively. Experiments also demonstrate that LAMBDA has a promising future in the safety evaluation of ADSs in virtual tests.
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