A Stochastic Predictive Adaptive Cruise Control System With Uncertainty-Aware Velocity Prediction and Parameter Self-Learning

Connectivity technologies in intelligent transportation systems offer unprecedented opportunities to enhance mobility, fuel economy, and safety for automotive systems. However, the uncertain driving behavior of surrounding vehicles in real-world traffic scenarios can significantly undermine these benefits. To tackle this challenge, this article develops a stochastic predictive-adaptive cruise control (P-ACC) system that effectively addresses uncertainties and automatically adapts to various driving scenarios. The proposed system employs a Gaussian process (GP)-based velocity predictor as its foundation, accurately capturing the driving dynamics of the preceding vehicle while accounting for prediction uncertainty using variances. The real-time feasibility is assessed in a dSPACE rapid prototyping system. In addition, the developed stochastic-model predictive control (S-MPC) approach incorporates the predicted velocity variance into the probabilistic chance constraints, conservatively narrowing the optimization space of the velocity planning domain, thereby enabling more reliable control. To further enhance the system's performance in adapting to different driving conditions, a scenario-based parameter self-learning (PSL) technique is introduced in the S-MPC controller, utilizing Bayesian optimization (BO). Finally, the performance of the proposed controller is comprehensively evaluated by leveraging a high-fidelity simulator and on-board actual vehicle testing data. Simulation results demonstrate that the proposed method achieved a boost in tracking performance and driving comfort while maintaining fuel-saving benefits.