A new technical paper titled “Towards Safe Autonomous Driving: A Real-Time Motion Planning Algorithm on Embedded Hardware” was published by researchers at TU Munich.
Abstract
“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: this https URL.”
Find the technical paper here. January 2026.
Moller, Korbinian, Glenn Johannes Tungka, Lucas Jürgens, and Johannes Betz. “Towards Safe Autonomous Driving: A Real-Time Motion Planning Algorithm on Embedded Hardware.” arXiv preprint arXiv:2601.03904 (2026).
Note: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
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