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To overcome the shortcomings of subjective evaluation, there have been several studies to examine the correlations between subjective and objective evaluations of on-center steering feel, and some useful results are obtained. However, it is still not clear how to design the steering characteristic based on the correlations. In this paper, we propose a methodology of identifying the optimal on-center steering force characteristic based on the correlations between subjective and objective evaluations. Firstly, significant correlations between subjective and objective evaluations regarding on-center steering feel are established and verified. These verified correlations are then used to design the steering force characteristic. With desired ratings of the subjective evaluation items set as optimization goals, the ideal values of objective evaluation indices are obtained by use of an optimal design method.

The Automatic Parking System (APS) is consisted of environmental perception, path planning and path following. As one of the key technologies in APS, path following module controls the lateral movement of the vehicle during the parking process. A mature path following module should meet all the performance indexes of high precision, fast convergence, convenient tuning and good passenger comfort. However, the current path following control methods can only meet parts of the performance indexes, instead of all. In order to satisfy all the performance indexes above, a path following control method based on Linear Quadratic Regulator (LQR) is proposed in this paper. Firstly, the linearization of the non-linear vehicle kinematic model was done to establish a linear system of the path following error. Secondly, LQR optimal control was used to achieve the closed-loop control of this linear system to guarantee its stability and fast convergence property.

As one of the key technologies in autonomous driving, the lateral planning module guides the lateral movement during the driving process. An integrated lateral planning module should consider the non-holonomic constraints of a vehicle, the optimization of the generated trajectory and the applicability to various scenarios. However, the current lateral planning methods can only meet parts of these requirements. In order to satisfy all the performance requirements above, a novel Potential Field (PF) based lateral planning method is proposed in this paper. Firstly, a PF model is built to describe the potential risk of the traffic entities, including the obstacles, road boundaries and lines. The potential fields of these traffic entities are determined by their properties and the traffic regulations. Secondly, the planning algorithm is presented, which comprises three modules: state prediction, state search and trajectory generation.

An integrated automatic driving system consists of perception, planning and control. As one of the key components of an autonomous driving system, the longitudinal planning module guides the vehicle to accelerate or decelerate automatically on the roads. A complete longitudinal planning module is supposed to consider the flexibility to various scenarios and multi-objective optimization including safety, comfort and efficiency. However, most of the current longitudinal planning methods can not meet all the requirements above. In order to satisfy the demands mentioned above, a new Potential Field (PF) based longitudinal planning method is presented in this paper. Firstly, a PF model is constructed to depict the potential risk of surrounding traffic entities, including obstacles and roads. The shape of each potential field is closely related to the property of the corresponding traffic entity.

In order to satisfy design requirements of Lane Keeping Assistance System (LKAS), a Driver Steering Override (DSO) strategy is necessary for driver’s interaction with the assistance system. The assistance system can be overridden by the strategy in case of lane change, obstacle avoidance and other emergency situations. However, evaluation and optimization of the DSO strategy for LKAS cannot easily be completed quantitatively considering driver’s acceptability. In this research, firstly subjective and objective evaluation experiment is designed. Secondly, correlations between the subjective and the objective evaluation results are established by using regression analysis. Finally, based on the correlations established previously, the optimal performance of DSO strategy is obtained by setting the desired comprehensive evaluation ratings as the optimized goal.