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This paper presents the results from an experimental project involving the full-scale testing of vehicles in a variety of maneuvers. The maneuvers include tripped rollover maneuvers resulting from impacts of eight test vehicles with both soil and curb pavement discontinuities. The reduced data from experimental tests form the impact condition variables which are augmented with vehicle design parameters in the analysis of results. The data are analyzed in datasets created according to such criteria as maneuver type, maneuver result, and type of terrain discontinuity. These results are investigated on the basis of mean values and coefficients of correlation. Results throughout the study support the conclusion that soil-tripped rollover maneuvers are qualitatively different than curb-tripped rollover maneuvers.

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.