自動運転システムのテストのための組み合わせ生成と最適化ツールComOpT
ComOpT: Combination and Optimization for Testing Autonomous Driving Systems
自動運転の計画・制御を対象に、k-way組み合わせ網羅と制約を考慮した抽象シナリオ生成から具体シナリオ化・KPI評価・局所変異までを行うオープンソーステストツールを開発し、Apollo 6で多様な問題を発見した。
著者: Changwen Li, Chih-Hong Cheng, Tiantian Sun, Yuhang Chen, Rongjie Yan
分類: cs.RO, cs.SE
原文アブストラクト
ComOpT is an open-source research tool for coverage-driven testing of autonomous driving systems, focusing on planning and control. Starting with (i) a meta-model characterizing discrete conditions to be considered and (ii) constraints specifying the impossibility of certain combinations, ComOpT first generates constraint-feasible abstract scenarios while maximally increasing the coverage of k-way combinatorial testing. Each abstract scenario can be viewed as a conceptual equivalence class, which is then instantiated into multiple concrete scenarios by (1) randomly picking one local map that fulfills the specified geographical condition, and (2) assigning all actors accordingly with parameters within the range. Finally, ComOpT evaluates each concrete scenario against a set of KPIs and performs local scenario variation via spawning a new agent that might lead to a collision at designated points. We use ComOpT to test the Apollo~6 autonomous driving software stack. ComOpT can generate highly diversified scenarios with limited test budgets while uncovering problematic situations such as inabilities to make simple right turns, uncomfortable accelerations, and dangerous driving patterns. ComOpT participated in the 2021 IEEE AI Autonomous Vehicle Testing Challenge and won first place among more than 110 contending teams.
