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

2025-07-10

AI Summary

Functional safety verification of autonomous driving motion planners faces challenges posed by complex and learning-based planners. This paper proposes a real-time runtime protection framework for trajectory safety validation, introducing— for the first time—a temporal protection module that jointly enforces geometric feasibility, dynamic feasibility, and cost rationality checks. The framework adopts a modular architecture and implements online validation of trajectory candidates on a real-time operating system, with successful deployment on embedded hardware. Experiments demonstrate that the system reliably detects unsafe trajectories under millisecond-level latency constraints. The source code is publicly available, and comprehensive fallback strategies are under integration. This work significantly enhances runtime safety assurance for black-box or learning-based planners, bridging a critical gap between planning flexibility and functional safety compliance.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Natural Language Processing: Safety and Robustness
• 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
• Security and Privacy: Data transparency and provenance

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

Problem

• Ensuring functional safety in autonomous vehicle motion planning
• Integrating online verification into real-time embedded systems
• Monitoring temporal consistency for timely system response

Innovation

• Introduces time safeguard for motion planning
• Uses constraint-based feasibility checks
• Modular framework for runtime verification