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

翻译：确保自动驾驶车辆（AV）的功能安全，需要运动规划模块不仅能在严格的实时约束下运行，还须在系统故障时保持可控性。现有的安全保障概念，如在线验证（OV），提供了检测不可行规划输出的安全层。然而，它们缺乏在主规划器失效时确保安全运行的主动机制。本文提出了迈向故障可操作自动驾驶（AD）主动安全扩展的第一步。我们在运行实时操作系统（RTOS）的车规级嵌入式平台上，部署了一个轻量级的基于采样的轨迹规划器。该规划器在受限的计算资源下持续计算轨迹，为未来的紧急规划架构奠定了基础。实验结果表明，系统具有确定性的时序行为、有界的延迟和最小的抖动，验证了在可安全认证硬件上进行轨迹规划的可行性。本研究强调了将主动后备机制作为下一代安全保障框架不可或缺部分的潜力与尚存的挑战。代码发布于：https://github.com/TUM-AVS/real-time-motion-planning