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With the development of vehicle electrification, electronic hydraulic brake system is gradually applied. Many companies have introduced products related to integrated electronic hydraulic brake system (I-EHB). In this paper, an I-EHB system is introduced, which uses the motor to drive the reduction mechanism as a power source for braking. The reduction mechanism is composed of a turbine, a worm, a gear and a rack. A control method based on command feed-forward is proposed to improve the hydraulic pressure control of I-EHB. Based on previous research, we simplify the system to first order system, and the theoretical design of the command feed-forward compensator is carried out. The feed-forward controller is applied, including the velocity feed-forward and the acceleration feed-forward, to improve the response speed and tracking effect of the system.

This paper proposed a lane-change planning method for autonomous vehicle, aiming at fast obstacles avoidance in a way that make smooth and comfortable. The panning algorithm consists of dynamic programming and closed-loop forward simulation. The dynamic programming (DP) was employed to fast search a reference trajectory that avoids obstacles in topological configure space. And the closed-loop forward simulation (CFS) was used to track the reference trajectory for generating smooth trajectory, since the CFS being able to incorporate any nonlinear law and nonlinear vehicle constraints. Furthermore, an anti-windup lateral controller was designed to make the closed-loop forward simulation robust, as the controller being proved to be stable by Lyapunov function. Finally, the numerical results are provided to illustrate the effectiveness of the proposed method.

The past decade has witnessed the rapid development of autonomous parking technology, since it has promising capacity to improve traffic efficiency and reduce the burden on drivers. However, it is prone to the trap of self-centeredness when each vehicle is automated parking in isolation. And it is easy to cause traffic congestion and even chaos when multiple autonomous vehicles require of parking into the same lot. In order to address the multiple vehicle parking problem, we propose a parking planning method with genetic algorithm. Firstly, an optimal mathematic model is established to describe the multiple autonomous vehicle parking problem. Secondly, a genetic algorithm is designed to solve the optimization problem. Thirdly, illustrative examples are developed to verify the parking planner. The performance of the present method indicates its competence in addressing parking multiple autonomous vehicles problem.

In recent years, intelligent driving technology is being extensively studied. This paper proposes a path planning method for perpendicular parking based on vehicle kinematics model using MPC optimization, which aims to solve the perpendicular parking task. Firstly, in the case of any initial position and orientation of the vehicle, judging whether the vehicle can be parked at one step according to the location of the parking place and the width of the lane, and then calculating the starting position for parking, and use the Bezier curve to connect the initial position and the starting position. Secondly, reference parking path is calculated according to the collision constraints of the parking space. Finally, because the parking path based on the vehicle kinematics model is composed of circle arcs and straight lines, the curvature of the path is discontinuous. The reference parking path is optimized using Model Predictive Control (MPC).

Although autonomous valet parking technology can replace the driver to complete the parking operation, it is easy to cause traffic chaos in the case of lacking scheduling for multiple parking agents, especially when multiple cars compete for the same parking slot at the same time. Therefore, in order to ensure orderly traffic and parking safety, it is necessary to allocate parking slots reasonably for multiple autonomous valet parking vehicles. The parking slots allocation model is built as an optimal problem with constraints. Both parking mileage cost and parking difficult cost are considering at the objective function in the optimization problem. There are three types of constraints. The first is the capacity limit of a single parking slot, the second is the space limit occupied by a single vehicle, and the third is the total capacity limit of the parking lot. After establishing parking slots allocation model, the immune algorithm is coded to solve the problem.

Increasing the degree of individuality of the autopilot and adapting it to the habits of drivers with different driving styles will help to increase occupant acceptance of the autopilot function. Inspired by the Twin Delayed Deep Deterministic policy gradient algorithm(TD3) algorithm to increase action spontaneity, this paper proposes a Soft Actor-Critic(SAC) based personalized following control strategy to increase the degree of strategy personalization through driver data. In order to obtain real driver data, this paper collected driving data based on driver-in-the-loop experiments conducted on a simulated driving platform, and selected data from three drivers with distinctive driving characteristics for model training. A continuous action space model was developed by vehicle following kinematics. A temporal Gate Recurrent Unit (GRU) based reference model is trained to receive temporal state signals and output acceleration actions according to the current state.