Priority-Driven Safe Model Predictive Control Approach to Autonomous Driving Applications

TL;DR

This paper presents a priority-driven safe model predictive control framework for autonomous driving that adaptively relaxes comfort constraints under disturbances while maintaining safety, with a learning-based approximation for real-time application.

Contribution

It introduces a novel priority-driven constraint softening mechanism within SMPC for autonomous driving, enabling real-time safety and comfort management under disturbances.

Findings

The approach maintains safety constraints under disturbances.

The learning-based algorithm enables real-time control.

Simulations confirm effectiveness in real-world scenarios.

Abstract

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.