A DNN Based Driving Scheme for Anticipatory Car Following Using Road-Speed Profile

The state prediction of the preceding vehicle (PV) is essential for anticipatory driving that enables a vehicle to take early steps for efficient driving. Such driving results in energy efficiency and traffic flow improvement but also requires repeated rigorous computations. Here we propose a Deep Neural Network (DNN) based driving scheme (DDS) that is trained to predict the PV and decide the control input of a host vehicle (HV) using two DNN models. We use the Road-Speed Profile (RSP), which provides approximated traffic speeds along the road using information from limited connected vehicles, to predict the future speed of the PV. Next, with the predicted speed of the PV, the control input is instantly determined to drive the HV efficiently. For making the optimal control decisions, the DNN is trained using a Model Predictive Control (MPC) scheme that generates the control input of the HV by minimizing a typical objective function reflecting costs related to inefficient driving. In the simulation, it is found that the proposed DDS guides the HV to change acceleration early in the optimized way by predicting the PV, particularly in changing traffic conditions, which results in a significant reduction in fuel consumption of the HV. Most remarkably, the DDS can determine the decision almost in real-time, which removes the necessity of setting a long step in the discrete-time control framework as found in MPC to match with its computation time. The proposed DDS is evaluated in some extreme cases and compared with traditional driving.