Because of their substantial weight and high centers of gravity, commercial vehicles require braking systems that ensure maximum performance and safety. Accurate braking control is vital for preserving safe vehicle dynamics by preventing lateral instability due to excessive deceleration or rear-wheel lock-up. Considering the growing demand for safety in medium-duty commercial vehicles, we introduce a rule-based dynamic braking controller for pneumatic electronic parking brake (EPB) systems. The proposed system is established using a model-based design (MBD) framework involving a V-cycle development process. The rule-based controller is designed to control the braking force based on wheel slip, thereby ensuring both adequate braking distance and lateral stability during emergency braking. Simulations and real-vehicle tests confirmed that the proposed control strategy can maintain lateral stability across varying loading and road-surface conditions. The results highlight the dynamic braking capability of the proposed pneumatic EPB system and its feasibility as an emergency braking solution. The effectiveness of the proposed controller in preventing wheel lock supports the use of MBD for developing safety-aware controllers.

This paper proposes a socially acceptable human-like behavior planning method to improve the driving efficiency for connected cars, especially in the dilemma zone on signalized road networks. Specifically, the proposed method is based on a soft-constrained model predictive control using slack variables that are assigned to the state constraints, resulting in the slight violation of the road speed limit to improve the efficiency. By exploiting the upcoming signal-phase-and-timing information of multiple traffic lights via vehicle connectivity technologies, a connected car optimizes the speed trajectory, leading to reduced stops in the dilemma zone compared to the vehicles that strictly comply with traffic rules. As a result, it is observed that connected cars could pass through multiple traffic lights when the green or yellow signals are turned on, resulting in the minimization of the entire trip time. The proposed method is comprehensively verified through simulations under various situations, and, the behavior of the connected cars is observed similar to that of experienced human drivers, particularly in the dilemma zone. We also show the efficacy of our approach through the experiments comparing the speed trajectory generated by our approach with those of different human drivers using a lab-scale driving simulator.

Hybrid electric vehicles (HEVs) utilize dual power sources requiring optimal energy distribution through sophisticated energy management systems (EMS). While nonlinear model predictive control (MPC) enables real-time energy optimization using short-term information, it suffers from prediction horizon limitations that prevent utilization of complete driving route information. This paper addresses this limitation by proposing approximated value function MPC (V-MPC) that incorporates reinforcement learning (RL) to approximate complex value function (VF) calculations using speed profile characteristics from repeated driving routes. The results show that V-MPC exhibits fuel consumption deviations of 3.15-5.70% relative to the optimal dynamic programming (DP) solution, while conventional MPC shows significantly larger deviations of 7.22-10.24%. Real-time validation using Autonomie software confirmed real-time applicability, achieving approximately 3.96% fuel consumption reduction compared to rule-based (RB) control while maintaining charge-sustaining (CS) mode operation under repeated driving scenarios. The proposed V-MPC achieves real-time energy optimization for repeated driving routes without requiring complete route information in advance.