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

arXiv: https://arxiv.org/abs/2601.03904 · HTML: https://mlsanity.com/paper/2601.03904/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 9 of 9)

- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement](https://mlsanity.com/paper/2205.11853/) (2022)
- [TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge](https://mlsanity.com/paper/2205.15979/) (2022)
- [FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Driving](https://mlsanity.com/paper/2402.01443/) (2024)
- [Real-time Motion Planning for autonomous vehicles in dynamic environments](https://mlsanity.com/paper/2406.02916/) (2024)
- [Online Verification Concept for Autonomous Vehicles -- Illustrative Study for a Trajectory Planning Module](https://mlsanity.com/paper/2005.07740/) (2020)
- [Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles](https://mlsanity.com/paper/2409.08665/) (2024)
- [Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios](https://mlsanity.com/paper/2504.01409/) (2025)
- [Towards Safe Autonomous Driving: A Real-Time Safeguarding Concept for Motion Planning Algorithms](https://mlsanity.com/paper/2507.07444/) (2025)

## Abstract (arXiv)

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