Cooperative Driving in Mixed Traffic of Manned and Unmanned Vehicles based on Human Driving Behavior Understanding

To achieve safe cooperative driving in mixed traffic of manned and unmanned vehicles, it is necessary to understand and model human drivers' driving behaviors. This paper proposed a Hidden Markov Model (HMM)-based method to analyze human driver's control and vehicle's dynamics; and then recognize the human driver's action, such as accelerating, braking, and changing lanes. With the knowledge of the human driver's actions, a probability model is used to predict the human-driven vehicle's acceleration. Such information on the driver behavior and the vehicle behavior can be used to achieve safer cooperative driving, which is realized using vehicle-to-vehicle (V2V) communication and model predictive control (MPC). The proposed method was tested and evaluated in our custom-built cooperative driving testbed. Experimental results show that the above driver action model is effective and accurate. A preliminary case study on a lane merging scenario is provided to further validate its effectiveness and capability.