Abstract
In this paper, with the aim of meeting the requirements of car following, safety, comfort, and economy for adaptive cruise control (ACC) system, an ACC algorithm based on model predictive control (MPC) using constraints softening is proposed. A higher-order kinematics model is established based on the mutual longitudinal kinematics between the host vehicle and the preceding vehicle that considers the changing characteristics of the inter-distance, relative velocity, acceleration, and jerk of the host vehicle. Performance indexes are adopted to represent the multi-objective demands and constraints of the ACC system. To avoid the solution becoming unfeasible because of the overlarge feedback correction, the constraint softening method was introduced to improve robustness. Finally, the proposed ACC method is verified in typical car-following scenarios. Through comparisons and case studies, the proposed method can improve the robustness and control precision of the ACC system, while satisfying the demands of safety, comfort, and economy.
Highlights
Traffic safety is facing severe challenges with the continuous growth of highway traffic mileage and car ownership
The constant time headway was utilized to analyze the mutual longitudinal kinematics model. The results indicate their superiority as compared with the linear quadratic regulator (LQR)-based Adaptive cruise control (ACC) algorithm (LQR-ACC)
The simulation results show that the driving comfort and the economy are drastically improved under the premise of ensuring safety and car following, at the same time, collision can be avoided with maximum braking acceleration when the preceding vehicle is in emergency braking
Summary
Traffic safety is facing severe challenges with the continuous growth of highway traffic mileage and car ownership. Most scholars consider car following and safety in the design of ACC strategies. The control goal of ACC is to improve the ride comfort and economy on the premise of tracking the expected safe car spacing, with the difficulty being in the construction of multi-objective control strategies involving such features as safety, Appl. The motivation of this paper is to design an ACC system that can consider the abovementioned multi-objectives, which include safety, comfort, economy, and car following. For the above multi-objective optimization problem, linear quadratic (LQ) and model predictive control (MPC) are commonly used solutions. It is important to consider that MPC is capable of optimally controlling constrained nonlinear problems in each step by rolling optimization while LQ cannot.
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