Longitudinal autonomous driving based on game theory for intelligent hybrid electric vehicles with connectivity

•A game-theory-based longitudinal autonomous driving control framework.•A multi-objective optimal problem, which contains safety, economy, comfort.•Coupled algebraic Riccati equations is solved to obtain the closed-loop strategies.•Better performance including car-following, energy saving, and driving comfort. Autonomous driving hybrid electric vehicles can offer unprecedented opportunities for autonomous safe & energy-efficient driving. However, how to integrate energy optimization during the car-following process and vehicle safety under complex traffic flow is a formidable challenge. Moreover, the coordinated control of three chassis parts including electric motor, internal combustion engine and vehicle brake system is hard to be tackled. Therefore, this paper aims to address longitudinal autonomous driving for intelligent hybrid electric vehicles. A game-theory-based longitudinal autonomous driving control framework is proposed with much easier access to information due to vehicle-to-vehicle/vehicle-to-infrastructure communication, which is our main contribution. Firstly, the whole longitudinal driving control is transformed into a multi-objective optimal problem, which contains safety, economy, comfort, so a game theory model is built to solve the multi-objective equilibrium problem. Then, to obtain the closed-loop strategies in Nash differential game, a system of coupled algebraic Riccati equations is solved. Finally, the game-theory-based control strategies coordinate electric motor, internal combustion engine and vehicle brake system to achieve multi-objective equilibrium. Simulation tests of the proposed framework and previous existing work are carried out, and their results show the proposed framework’s better performance of longitudinal dynamics control including car-following, reducing fuel consumption, and driving comfort.