# Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments

arXiv: https://arxiv.org/abs/2305.03312 · HTML: https://mlsanity.com/paper/2305.03312/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2023.

## Built on by (most cited first, 3 of 3)

- [Driving from Vision through Differentiable Optimal Control](https://mlsanity.com/paper/2403.15102/) (2024)
- [Safe and Nonconservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers](https://mlsanity.com/paper/2509.07464/) (2025)
- [A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving](https://mlsanity.com/paper/2606.28384/) (2026)

## Abstract (arXiv)

The full deployment of autonomous driving systems on a worldwide scale requires that the self-driving vehicle be operated in a provably safe manner, i.e., the vehicle must be able to avoid collisions in any possible traffic situation. In this paper, we propose a framework based on Model Predictive Control (MPC) that endows the self-driving vehicle with the necessary safety guarantees. In particular, our framework ensures constraint satisfaction at all times, while tracking the reference trajectory as close as obstacles allow, resulting in a safe and comfortable driving behavior. To discuss the performance and real-time capability of our framework, we provide first an illustrative simulation example, and then we demonstrate the effectiveness of our framework in experiments with a real test vehicle.