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Vehicle failure prediction technology is an important part of PHM (Prognostic and Health Management) technology, which is of great significance to the safety of vehicles and to improve driving safety. Based on the vehicle operating data collected by the on-board terminal (T-box) of the telematics system, the research on the state of vehicle failure is conducted. First, this paper conducts statistical analysis on vehicle historical fault data. Preprocessing procedures such as cleaning, integration, and protocol are performed to group the data set. Then, three indexes including recency (R) frequency (F), and days (D) are selected to construct a vehicle security status subdivision system, and K -Means algorithm is utilized to divide different vehicle categories from the perspective of vehicle value. Labeled information of vehicles in different security status are further established.

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.