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Lane keeping assist (LKA) is an autonomous driving technique that enables vehicles to travel along a desired line of lanes by adjusting the front steering angle. Reinforcement learning (RL) is one kind of machine learning. Agents or machines are not told how to act but instead learn from interaction with the environment. It also frees us from coding complex policies manually. But it has not yet been successfully applied to autonomous driving. Two control strategies using different deep reinforcement learning (DRL) algorithms have been proposed and used in the lane keeping assist scenario in this paper. Deep Q-network (DQN) algorithm with discrete action space and deep deterministic policy gradient (DDPG) algorithm with continuous action space have been implemented, respectively. Based on MATLAB/Simulink, deep neural networks representing the control policy are designed. The environment as well as the vehicle dynamics are also modelled in Simulink.

As a global optimization method, dynamic programming (DP) can be employed to seek the optimal velocity with minimum energy consumption for EV on given driving cycles. Due to its terrible computational burden, conventional DP is not suitable for real-time implementation especially with higher dimensions. In this paper, we propose an iterative dynamic programming (IDP) approach to reduce computing time firstly. The IDP can obtain the optimal control laws alike the conventional DP by converging the optimal control strategy iteratively and save considerable computing time. Second, the developed IDP and model predictive control (MPC) are combined to establish a real-time cruising controller called IDP-MPC for an EV with terrain preview. In the predictive controller, we use the IDP to solve a constrained finite horizon nonlinear optimization problem.

Connected and automated vehicles (CAVs) are gaining momentum, especially in the potential to improve road safety and reducing energy consumption and emissions. Lane-change maneuver is one of the most important conventional parts of automated driving. We address the problem of optimally CAVs to accomplish an automated lane-change and eliminate potential collision during the lane-change process on a curved road. Drivers’ safety, comfort, convenience, and fuel economy are also engaged in trajectory planning. We assume that the centripetal motion displacement and the rotational angular displacement meet the requirement of odd-order polynomial constrains. Then, the polynomial coefficient of the trajectory can be reduced and the mathematical model of virtual trajectory for lane-change can be designed based on the models of centripetal displacement and angular displacement by applying the above constrains and boundary conditions.

This paper proposes a semi-active hybrid battery system (HBS), composed by lithium iron phosphate battery (LFP) and lithium titanate battery (LTO) for electric vehicle (EV) to reduce the life cycle cost of energy storage system. Firstly, the topology of this HBS is introduced. The high energy-density battery, LFP is adopted as the primary energy source, while the high power-density one, LTO is connected in parallel with a bidirectional DC-DC converter and used as secondary energy source to extend the lifetime of HBS by reducing the current stress of LFP. The dynamic model of this HBS is built, in which, the LFP and LTO are both modeled as second-order RC model. In addition, dynamic semi-empirical degradation model of the LFP battery is chosen to estimate the lifetime of HBS. Secondly, a fuzzy logic controller with 3 inputs and 1 output is proposed to decide the power split between the primary and secondary power sources.