The simulation-based testing of Autonomous Driving Systems (ADSs) has gained significant attention. However, current approaches often fall short of accurately assessing ADSs for two reasons: over-reliance on expert knowledge and the utilization of simplistic evaluation metrics. That leads to discrepancies between simulated scenarios and naturalistic driving environments. To address this, we propose the Matrix-Fuzzer, a behavior tree-based testing framework, to automatically generate realistic safety-critical test scenarios. Our approach involves the $log2BT$ method, which abstracts logged road-users' trajectories to behavior sequences. Furthermore, we vary the properties of behaviors from real-world driving distributions and then use an adaptive algorithm to explore the input space. Meanwhile, we design a general evaluation engine that guides the algorithm toward critical areas, thus reducing the generation of invalid scenarios. Our approach is demonstrated in our Matrix Simulator. The experimental results show that: (1) Our $log2BT$ achieves satisfactory trajectory reconstructions. (2) Our approach is able to find the most types of safety-critical scenarios, but only generating around 30% of the total scenarios compared with the baseline algorithm. Specifically, it improves the ratio of the critical violations to total scenarios and the ratio of the types to total scenarios by at least 10x and 5x, respectively, while reducing the ratio of the invalid scenarios to total scenarios by at least 58% in two case studies.

翻译：仿真测试在自动驾驶系统（ADS）中受到广泛关注。然而，当前方法往往因过度依赖专家知识和采用简单评估指标，难以准确评估ADS，导致仿真场景与自然驾驶环境之间存在差异。为此，我们提出Matrix-Fuzzer——一种基于行为树的测试框架，可自动生成真实安全关键测试场景。该方法通过$log2BT$技术将记录的道路使用者轨迹抽象为行为序列，进一步从真实驾驶分布中改变行为属性，并采用自适应算法探索输入空间。同时，我们设计通用评估引擎引导算法聚焦关键区域，减少无效场景生成。该方法在Matrix Simulator仿真器中得到验证。实验结果表明：（1）$log2BT$实现了高精度的轨迹重构；（2）与基线算法相比，本方法能发现最多类型的危险安全场景，且生成的场景总数仅占基线算法的约30%。具体而言，在两个案例研究中，本方法将关键违规场景占比提升至少10倍，场景类型占比提升至少5倍，同时无效场景占比降低至少58%。