用冲突代替碰撞，更高效发现自动驾驶安全隐患。
From Conflicts to Collisions: A Two-Stage Collision Scenario-Testing Approach for Autonomous Driving Systems
- 先找潜在冲突，再将其变异为真实碰撞，分两阶段挖掘危险场景。
- 单次运行发现12种碰撞类型，多样性是当前最佳方法的两倍。
- 适合自动驾驶安全测试团队，提升评估效率与覆盖广度。
自动驾驶系统（ADS）属于高安全要求场景，需在公开部署前进行严格测试。基于仿真的场景测试提供了一种安全且低成本的替代方案，可在多样化高风险条件下高效评估ADS性能。然而，现有方法主要关注已接近碰撞的场景，忽略了大量其他危险情境。为此，本文引入‘冲突’作为碰撞相关的中间搜索目标，提出一种两阶段场景测试框架：首先搜索冲突场景，再对这些冲突场景进行变异以诱发实际碰撞。在百度阿波罗平台上的评估表明，该方法单次运行可揭示多达12种不同的碰撞类型，其发现的多样性是当前最先进基线方法的两倍，同时因采用冲突导向的变异策略，所需仿真次数更少。结果表明，将冲突作为中间目标可显著扩展搜索范围，大幅提升ADS安全评估的效率与有效性。
原文摘要 · Abstract (English)
Autonomous driving systems (ADS) are safety-critical and require rigorous testing before public deployment. Simulation-based scenario testing provides a safe and cost-effective alternative to extensive on-road trials, enabling efficient evaluation of ADS under diverse and high-risk conditions. However, existing approaches mainly evaluates the scenarios based on their proximity to collisions and focus on scenarios already close to collision, leaving many other hazardous situations unexplored. To bridge this, we introduce a collision-related concept of conflict as an intermediate search target and propose a two-stage scenario testing framework that first searches for conflicts and then mutates these conflict scenarios to induce actual collisions. Evaluated on Baidu Apollo, our approach reveals up to 12 distinct collision types in a single run, doubling the diversity discovered by state-of-the-art baselines while requiring fewer simulations thanks to conflict-targeted mutations. These results show that using conflicts as intermediate objectives broadens the search horizon and significantly improves the efficiency and effectiveness of ADS safety evaluation.
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