A Driver-in-the Loop Fuel Economic Control Strategy for Connected Vehicles in Urban Roads
This work addresses fuel efficiency and traffic mobility for connected vehicles in urban roads, but the results are simulation-based and the approach is incremental.
The paper develops a driver-in-the-loop fuel economic control strategy for connected vehicles that uses V2V communication and traffic light information, modeling driver errors with a Markov chain and stochastic MPC. Simulation results demonstrate improved fuel economy and traffic mobility by accounting for driver error injection.
In this paper, we focus on developing driver-in-the loop fuel economic control strategy for multiple connected vehicles. The control strategy is considered to work in a driver assistance framework where the controller gives command to a driver to follow while considering the ability of the driver in following control commands. Our proposed method uses vehicle-to-vehicle (V2V) communication, exploits traffic lights' Signal Phase and Timing (SPAT) information, models driver error injection with Markov chain, and employs scenario tree based stochastic model predictive control to improve vehicle fuel economy and traffic mobility. The proposed strategy is decentralized in nature as every vehicle evaluates its own strategy using only local information. Simulation results show the effect of consideration of driver error injection when synthesizing fuel economic controllers in a driver assistance fashion.