This paper demonstrates the applicability of the safe model predictive control (SMPC) framework to autonomous driving scenarios, focusing on the design of adaptive cruise control (ACC) and automated lane-change systems. Building on the SMPC approach with priority-driven constraint softening -- which ensures the satisfaction of \emph{hard} constraints under external disturbances by selectively softening a predefined subset of adjustable constraints -- we show how the algorithm dynamically relaxes lower-priority, comfort-related constraints in response to unexpected disturbances while preserving critical safety requirements such as collision avoidance and lane-keeping. A learning-based algorithm approximating the time consuming SMPC is introduced to enable real-time execution. Simulations in real-world driving scenarios subject to unpredicted disturbances confirm that this prioritized softening mechanism consistently upholds stringent safety constraints, underscoring the effectiveness of the proposed method.
View on arXiv@article{prignoli2025_2505.05933,
title={ Priority-Driven Safe Model Predictive Control Approach to Autonomous Driving Applications },
author={ Francesco Prignoli and Ying Shuai Quan and Mohammad Jeddi and Jonas Sjöberg and Paolo Falcone },
journal={arXiv preprint arXiv:2505.05933},
year={ 2025 }
}