Citation

Zhou Q, Zhou B, Hu S, Roncoli C, Wang Y, Hu J, Lu G. Transp. Res. C Emerg. Technol. 2023; 156: e104320.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trc.2023.104320

PMID

unavailable

Abstract

This paper presents a safety-enhanced eco-driving strategy for connected and autonomous vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The driving risk field, shockwave theory, and motion planning and control method are integrated into this framework to optimize the trajectories of CAVs on a signalized arterial under mixed traffic flow, with the aim of reducing the driving risk and fuel consumption of CAVs simultaneously, while ensuring traffic efficiency. The optimization procedure is mainly composed of two parts: long-term trajectory planning based on optimal control and short-term trajectory control based on model predictive control, which makes the strategy more adaptable to the various traffic conditions. The results show that the proposed framework can effectively reduce the safety risk that vehicles are exposed to and their fuel consumption by 18%-24% and 20%-27%, respectively. Furthermore, it reveals that conventional eco-driving strategies may result in negative safety issues when only considering the impact of preceding vehicles on the eco-CAV. However, these negative impacts can be eliminated when the impacts of following vehicles on the eco-CAV are taken into account. In addition, the sensitivity analysis on the Market Penetration Rate (MPR) of CAVs and traffic demand is performed. The results show that the framework is robust and can work under various traffic conditions (including under-saturated and over-saturated ones) and different MPRs.

Language: en

Keywords

Connected and autonomous vehicles; Driving risk field; Eco-driving; Model predictive control; Optimal control; Trajectory optimization