Citation

Sun K, Gong S, Zhou Y, Chen Z, Zhao X, Wu X. Transp. Res. C Emerg. Technol. 2024; 159: e104487.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.trc.2024.104487

PMID

unavailable

Abstract

This paper proposes a multi-vehicle cooperative control scheme in mitigating traffic oscillation (MCCS-MTO) for a single-vehicle lane change (LC) scenario applied to connected and autonomous vehicles (CAVs) with guaranteed executing applicability. Specifically, a hierarchical structure is applied in the proposed MCCS-MTO to dampen traffic oscillation on both original and target lanes. It decomposes the MCCS-MTO into two subsets of controllers (i.e. upper-layer and low-layer). The upper-layer controller first regards the LC vehicle and the following vehicle on the target lane as control objects and optimally controls their movements by considering the ambient traffic conditions. The lower-layer controller consecutively controls the moving status of the following vehicle on the original lane according to the LC vehicle's optimized predictive state outputs obtained from the upper-layer and the surrounding traffic. The vehicle dynamic is modeled by incorporating the lateral and longitudinal movements of the LC vehicle into unified control quantities (i.e. axial acceleration and steering angle) to enhance the executing applicability. Both upper-layer and low-layer controllers in the proposed MCCS-MTO are established by leveraging the model predictive control and considering the longitudinal tracking performance, transient traffic smoothness, and lateral LC efficiency with multiple safety constraints. To guarantee a smooth and collision-free tracking performance when preceding vehicles switch, a set of innovative negative exponential and S-shaped functions is designed and integrated into both constraints and objective functions. To ensure the successful implementation of the proposed method, the sequential feasibility properties for the proposed MCCS-MTO are theoretically proven. The proposed approach is validated through numerical experiments in Python with multiple LC scenarios from the NGSIM dataset. The results of the numerical experiments indicate that the proposed study can execute a safe and efficient LC while performing a smooth longitudinal movement that will improve traffic efficiency.

Language: en

Keywords

Connected and autonomous vehicles; Lane change; Mitigating traffic oscillation; Model predictive control; Multi-vehicle cooperative control; Smooth preceding vehicle swithing