This research presents an eco-approach controller with overtake capability. It overcomes the shortcomings of conventional eco approach and is able to: i) overtake slowing moving vehicles for 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 advantage of connected vehicle technology by taking in real-time vehicle and infrastructure information as optimization input, including obstacle vehicle’s speed and position, signal timing and more. The problem is formulated as optimal control and is solved by GPOPS. The nonlinear bicycle model is adopted as the system dynamics, and linearized to reduce computational burden. The stochasticity of surrounding traffic is considered as a probability distribution which is transformed into a linear constraint. Quantitative evaluation is conduct to compare the proposed controller against human drivers and baseline optimal controller which only has longitudinal automation. Sensitivity analysis is conducted for the arrival time of the ego Cooperative and Automated Vehicle (CAV). The evaluation results demonstrate that the proposed controller improves the fuel efficiency by 17.20%-70.12%, and outperforms two baseline controllers by 3.13-26.67% fuel saving. In addition, the greatest improvement in mobility is up to approximately 170%.

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