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

TL;DR

This paper introduces a real-time, embedded motion planning algorithm for autonomous vehicles that operates within strict safety and timing constraints, serving as a foundation for active fail-safe mechanisms.

Contribution

It presents a lightweight, sampling-based trajectory planner implemented on automotive-grade hardware with real-time guarantees, advancing active safety in autonomous driving.

Findings

Deterministic timing with bounded latency achieved

Feasibility demonstrated on safety-certifiable hardware

Potential for integration into active fallback safety systems

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