Cut through traffic to catch green light: Eco approach with overtaking capability. (February 2021)

• Record Type: Journal Article
• Authors: Hu, Jia; Zhang, Zihan; Xiong, Lu; Wang, Haoran; Wu, Guoyuan
• Is Part Of: Transportation research. Volume 123(2021)
• Publication Date: 2021-02
• Subjects: Eco approach -- Optimal control -- Overtaking capability -- Stochasticity; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2020.102927
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Able to overtake slowly-moving vehicles for ecological purpose. Able to optimize the travel duration approaching a signalized intersection. Balancing fuel saving and vehicle's mobility. Considering stochasticity of surrounding traffic. Functional under partially connected and automated environment. Abstract: This research presents an enhanced eco-approach controller with overtaking capability. The proposed controller overcomes the shortcomings of the conventional eco approach and is able to: i) overtake slowly-moving vehicles for the ecological purpose; ii) optimize the travel duration approaching an intersection; iii) guarantee both fuel saving and vehicle's mobility; iv) consider stochasticity of surrounding traffic; v) functional under partially connected and automated environment. It takes full advantage of connected vehicle technology by taking in real-time vehicle and infrastructure information as optimization input. The problem is formulated as an optimal control problem and is solved by GPOPS. The nonlinear bicycle model is adopted as the system dynamics to realize CAV's longitudinal and lateral coupling control, and linearized to reduce the computational burden. The stochasticity of surrounding traffic is considered as a probability distribution that is transformed into a linear chance constraint. Quantitative evaluation is conducted to compare the proposed controller against human drivers and the conventional eco approach which only has longitudinal automation. The evaluation results demonstrate that the proposed controller improves the fuel efficiency by 4.13–70.12%, and outperforms two baseline controllers by 6.06–36.73% in terms of fuel saving. The range is caused by the different arrival time of the ego CAV. In addition, the simulation experiment in VISSIM is conducted to analyze how background traffic flow influences the performance of the proposed controller.