安全な自動運転に向けて：組み込みハードウェア上でのリアルタイム動作計画アルゴリズム
Towards Safe Autonomous Driving: A Real-Time Motion Planning Algorithm on Embedded Hardware
自動車グレードの組み込みプラットフォームとRTOS上で軽量なサンプリングベース軌道プランナを実装し、決定論的なタイミングと低遅延で動作することを実験的に示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Korbinian Moller, Glenn Johannes Tungka, Lucas Jürgens, Johannes Betz
分類: cs.RO
原文アブストラクト
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
