Event-triggered model predictive control for autonomous vehicle active obstacle avoidance

In this paper, a strategy for safe driving of autonomous vehicles in complex environments with obstacles is proposed, which combines a double-layer model predictive control (DLMPC) with an event-triggered mechanism. At first, a safe collision distance model is established in the upper path planning layer, utilizing a vehicle point mass model and based on the distance relationship between the vehicle and obstacles. The design of the planning layer improves the safety of the vehicle. After solving the optimization problem, the optimal input variable is derived and then transmitted to the dynamic control layer for accurate tracking. It is followed by designing the triggered condition according to the stability and feasibility of the system, which reduces the computation of the controller and improves the real-time performance of the system. Finally, the simulation results demonstrate the efficacy of the proposed control strategy in addressing the obstacle avoidance challenge faced by autonomous vehicles in complex road conditions. Compared with traditional model predictive control (MPC), this strategy improves the precision of tracking control and reduces the computational complexity.