ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Driving

Computer Science > Artificial Intelligence [Submitted on 20 May 2026] ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Driving Qiyu Ruan, Yuxuan Wang, He Li, Zhenning Li, Cheng-zhong Xu Safety-critical scenarios are central to evaluating autonomous driving systems, yet their rarity in naturalistic logs makes simulation-based stress testing indispensable. Most scenario generation methods treat surrounding agents as adversaries, but they either (i) induce failures without explicitly modeling vehicle-road physical limits, yielding visually extreme yet physically unsolvable crashes, or (ii) enforce physical feasibility or policy feasibility in isolation, which can over-focus on aggressive maneuvers or remain tied to a controller-dependent capability boundary. We propose ScenePilot, a feasibility-guided, boundary-driven framework that targets the boundary band: scenarios that are physically solvable in principle yet still cause the deployed autonomy stack to fail. We formulate generation as constrained multi-objective reinforcement learning, combining an RSS-derived physical-feasibility score \sigma with an online-learned AV-risk predictor \Phi, and introduce step-level feasibility-aware shielding to keep exploration near the feasibility boundary while avoiding infeasible artifacts. Experiments on SafeBench with multiple planners show that ScenePilot yields substantially higher collision rates (+6.2 percentage points) while preserving physical validity, and that adversarial fine-tuning on these boundary-band scenarios consistently reduces downstream crash rates. The code is available at this https URL. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.21168 [cs.AI]   (or arXiv:2605.21168v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.21168 Focus to learn more Submission history From: Qiyu Ruan [view email] [v1] Wed, 20 May 2026 13:39:02 UTC (7,010 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)