Towards Safe Autonomous Driving: A Real-Time Safeguarding Concept for Motion Planning Algorithms

TL;DR

This paper introduces a real-time safeguarding framework for autonomous vehicle motion planning that monitors temporal consistency and feasibility to ensure safety, operating within embedded systems and real-time constraints.

Contribution

It extends existing verification methods by adding a time safeguard that monitors temporal aspects of planning outputs for improved safety assurance.

Findings

Safeguarding module operates within real-time bounds.

Effectively detects unsafe trajectories.

Framework is modular and extensible for automotive hardware.

Abstract

Ensuring the functional safety of motion planning modules in autonomous vehicles remains a critical challenge, especially when dealing with complex or learning-based software. Online verification has emerged as a promising approach to monitor such systems at runtime, yet its integration into embedded real-time environments remains limited. This work presents a safeguarding concept for motion planning that extends prior approaches by introducing a time safeguard. While existing methods focus on geometric and dynamic feasibility, our approach additionally monitors the temporal consistency of planning outputs to ensure timely system response. A prototypical implementation on a real-time operating system evaluates trajectory candidates using constraint-based feasibility checks and cost-based plausibility metrics. Preliminary results show that the safeguarding module operates within real-time bounds and effectively detects unsafe trajectories. However, the full integration of the time safeguard logic and fallback strategies is ongoing. This study contributes a modular and extensible framework for runtime trajectory verification and highlights key aspects for deployment on automotive-grade hardware. Future work includes completing the safeguarding logic and validating its effectiveness through hardware-in-the-loop simulations and vehicle-based testing. The code is available at: https://github.com/TUM-AVS/motion-planning-supervisor