Intelligence is a direction of development for vehicles and transportation. Accurate vehicle positioning plays a vital role in intelligent driving and transportation. In the case of obstruction or too few satellites, the positioning capability of the Global navigation satellite system (GNSS) will be significantly reduced. To eliminate the effect of unlocalization due to missing GNSS signals, a collaborative multi-vehicle localization scheme based on GNSS and vehicle networks is proposed. The vehicle first estimates the location based on GNSS positioning information and then shares this information with the environmental vehicles through vehicle network communication. The vehicle further integrates the relative position of the ambient vehicle observed by the radar with the ambient vehicle position information obtained by communication. A smaller error estimate of the position of self-vehicle and environmental vehicles is obtained by correcting the positioning of self-vehicle and environmental vehicles. The proposed method is validated by simulating multi-vehicle motion scenarios in both lane change and straight-ahead scenarios. The root-mean-square error of the co-location method is below 0.5 m. The results demonstrate that the combined vehicle network communication approach has higher accuracy than single GNSS positioning in both scenarios.

This study aims to improve the vehicle vertical dynamics performance in the sprung and unsprung state for in-wheel-motor-driven electric vehicles (IWMD EVs) while considering the unbalanced electric magnetic force effects. An integrated vibration elimination system (IVES) is developed, containing a dynamic vibration-absorbing structure between the IWM and the suspension. It also includes an active suspension system based on a delay-dependent $H\infty$ controller. Further, a novel frequency-compatible tire (FCT) model is constructed to improve IVES accuracy. The mechanical-electrical-magnetic coupling effects of IWMD EVs are theoretically analyzed. A virtual prototype for the IVES is created by combining the CATIA, ADAMS, and MatLab/Simulink, resulting in a high-fidelity multi-body model, validating the IVES accuracy and practicability. Simulations for the IVES considered three different suspension structure types and time delay considerations were performed. Analyses in frequency and time domains for the simulation results have shown that the root mean square of sprung mass acceleration and the eccentricity are significantly reduced via the IVES, indicating an improvement in ride comfort and IWM vibration suppression.