Towards Safe Autonomous Driving: A Real-Time Motion Planning Algorithm on Embedded Hardware

2026-01-07

arXiv.org

AI Summary

Current autonomous driving systems lack mechanisms to actively ensure safe operation when the primary planner fails, making it difficult to simultaneously meet functional safety and real-time requirements. This work proposes a fail-operational active safety extension architecture that, for the first time, integrates a lightweight sampling-based trajectory planner into a certifiable safety framework and implements it on automotive-grade embedded hardware using a real-time operating system. The approach enables deterministic real-time computation under stringent resource constraints. Experimental results demonstrate that the system exhibits bounded latency and extremely low timing jitter, thereby validating the feasibility of performing real-time emergency trajectory planning on a safety-certifiable platform.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning
• Intelligent Robots: Motion and Path Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

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: https://github.com/TUM-AVS/real-time-motion-planning

Problem

• Autonomous Driving
• Motion Planning
• Fail-operational
• Real-Time
• Functional Safety

Innovation

• real-time motion planning
• fail-operational autonomy
• embedded safety-critical systems
• sampling-based trajectory planner
• active fallback mechanism