{"ID":2895970,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.07444","arxiv_id":"2507.07444","title":"Towards Safe Autonomous Driving: A Real-Time Safeguarding Concept for Motion Planning Algorithms","abstract":"Ensuring the functional safety of motion planning modules in autonomous vehicles remains a critical challenge, especially when dealing with complex or learning-based software. Online verification has emerged as a promising approach to monitor such systems at runtime, yet its integration into embedded real-time environments remains limited. This work presents a safeguarding concept for motion planning that extends prior approaches by introducing a time safeguard. While existing methods focus on geometric and dynamic feasibility, our approach additionally monitors the temporal consistency of planning outputs to ensure timely system response. A prototypical implementation on a real-time operating system evaluates trajectory candidates using constraint-based feasibility checks and cost-based plausibility metrics. Preliminary results show that the safeguarding module operates within real-time bounds and effectively detects unsafe trajectories. However, the full integration of the time safeguard logic and fallback strategies is ongoing. This study contributes a modular and extensible framework for runtime trajectory verification and highlights key aspects for deployment on automotive-grade hardware. Future work includes completing the safeguarding logic and validating its effectiveness through hardware-in-the-loop simulations and vehicle-based testing. The code is available at: https://github.com/TUM-AVS/motion-planning-supervisor","short_abstract":"Ensuring the functional safety of motion planning modules in autonomous vehicles remains a critical challenge, especially when dealing with complex or learning-based software. Online verification has emerged as a promising approach to monitor such systems at runtime, yet its integration into embedded real-time environm...","url_abs":"https://arxiv.org/abs/2507.07444","url_pdf":"https://arxiv.org/pdf/2507.07444v1","authors":"[\"Korbinian Moller\",\"Rafael Neher\",\"Marvin Seegert\",\"Johannes Betz\"]","published":"2025-07-10T05:44:34Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false,"code_links":[{"ID":612230,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2895970,"paper_url":"https://arxiv.org/abs/2507.07444","paper_title":"Towards Safe Autonomous Driving: A Real-Time Safeguarding Concept for Motion Planning Algorithms","repo_url":"https://github.com/TUM-AVS/motion-planning-supervisor","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}