ビヘイビアツリーによる自動運転システム向け現実的な安全臨界シナリオ探索
Realistic Safety-critical Scenarios Search for Autonomous Driving System via Behavior Tree
実走行ログをビヘイビアツリーで抽象化し、適応的探索と評価エンジンで現実的な安全臨界シナリオを自動生成するテスト手法を提案した。
著者: Ping Zhang, Lingfeng Ming, Tingyi Yuan, Cong Qiu, Yang Li, Xinhua Hui, Zhiquan Zhang, Chao Huang
分類: cs.RO, cs.SE
原文アブストラクト
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.
