With the increasing frequency of autonomous driving, more and more attention is paid to personalized path planning. However, the path selection preferences of users will change with internal or external factors. Therefore, this paper proposes a personalized path recommendation strategy that can track and study user’s path preference. First, we collect the data of the system, establish the relationship with the user preference factor, and get the user’s initial preference weight vector by dichotomizing the K-means algorithm. The system then determines whether user preferences change based on a set threshold, and when the user’s preference changes, the current preference weight vector can be obtained by redefining the preference factor or calling difference perception. Finally, the road network is quantized separately according to the user preference weight vector, and the optimal path is obtained by using Tabu search algorithm. The simulation results of two scenarios show that the proposed strategy can meet the requirements of autopilot even when user preferences change.

Ride comfort criteria are a key challenge for vehicle dynamic design and optimization. Currently optional parameter is the vertical impact, and longitudinal impact is neglected. With further requirements for future comfortability, effects of longitudinal impact should be investigated in detail. A longitudinal impact model is firstly proposed to evaluate the ride comfort factors based on the dynamic theory and commercial ADAMS® software. Predictions revealed that the hard points of the suspension and the stiffness of rubber bushing (SORB) are the primary factors. A novelty finding is that travel of rubber bushing (TORB) in the linear region is the most important parameter for ride comfort optimization and suspension factor is the weakest, and experimental validation is performed with better agreements.