在嵌入式设备上实现实时运动规划，为自动驾驶提供故障下的主动安全保障。
Towards Safe Autonomous Driving: A Real-Time Motion Planning Algorithm on Embedded Hardware
- 在车载实时系统上部署轻量采样规划器，持续生成受资源约束的轨迹。
- 实验显示延迟有界、抖动极小，满足安全级硬件的确定性时序要求。
- 为下一代自动驾驶的故障容错系统提供可落地的应急规划基础，适合安全关键场景研究者。
确保自动驾驶车辆的功能安全性，要求运动规划模块在严格实时约束下运行，并在系统故障时仍保持可控性。现有防护机制如在线验证（OV）能检测不可行的规划输出，但缺乏主规划器失效时的主动应对能力。本文首次提出面向故障容错自动驾驶的主动安全扩展方案：在符合车规的嵌入式平台（运行实时操作系统RTOS）上部署轻量级采样轨迹规划器，持续在有限计算资源下生成轨迹，为未来应急规划架构奠定基础。实验表明，该系统具备确定性时序行为，延迟有界且抖动极小，验证了在安全认证硬件上实现轨迹规划的可行性。研究揭示了集成主动回退机制在下一代防护框架中的潜力与现存挑战。代码开源：https://github.com/TUM-AVS/real-time-motion-planning
原文摘要 · Abstract (English)
Ensuring the functional safety of Autonomous Vehicles (AVs) requires motion planning modules that not only operate within strict real-time constraints but also maintain controllability in case of system faults. Existing safeguarding concepts, such as Online Verification (OV), provide safety layers that detect infeasible planning outputs. However, they lack an active mechanism to ensure safe operation in the event that the main planner fails. This paper presents a first step toward an active safety extension for fail-operational Autonomous Driving (AD). We deploy a lightweight sampling-based trajectory planner on an automotive-grade, embedded platform running a Real-Time Operating System (RTOS). The planner continuously computes trajectories under constrained computational resources, forming the foundation for future emergency planning architectures. Experimental results demonstrate deterministic timing behavior with bounded latency and minimal jitter, validating the feasibility of trajectory planning on safety-certifiable hardware. The study highlights both the potential and the remaining challenges of integrating active fallback mechanisms as an integral part of next-generation safeguarding frameworks. The code is available at: https://github.com/TUM-AVS/real-time-motion-planning
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