A Control Strategy for Driver Specific Driver Assistant System to Improve Fuel Economy of Connected Vehicles in Urban Roads

In this paper, we focus on developing a fuel economic driver specific driver assistant system's control strategy for multiple connected vehicles in urban road conditions. The control strategy is considered to work in a driver assistance framework where the controller provides 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 distributed in nature as every vehicle evaluates its own strategy using only locally available information. Simulation results show improvement in fuel efficiency when driver error injection is considered while synthesizing fuel economic controllers in a driver assistance fashion.