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Accurate tire pressure monitoring system (TPMS) is of great practical importance and the reliability and safety of its power supply module has great concern. The piezoelectric-based surface acoustic wave (SAW) sensor is considered to have great potential in this field because of its passive, wireless and small size advantages. This paper presents the application of passive and wireless SAW sensors for real-time tire condition monitoring. The pressure sensitive structure is optimized and a three-resonator structure is also designed sensing temperature and pressure. Furthermore, a fast detection system is developed to realize high-speed signal acquisition. At last, experiments are executed and the SAW temperature and pressure sensor property is measured.

Tire-road friction condition is crucial to the safety of vehicle driving. The emergence of autonomous driving makes it more important to estimate the friction limit accurately and at the lowest possible excitation. In this paper, an early detection method of tire-road friction coefficient based on pneumatic trail under cornering conditions is proposed using an intelligent tire system. The previously developed intelligent tire system is based on a triaxial accelerometer mounted on the inner liner of the tire tread. The friction estimation scheme utilizes the highly sensitive nature of the pneumatic trail to the friction coefficient even in the linear region and its approximately linear relationship with the excitation level. An indicator referred as slip degree indicating the utilization of the road friction is proposed using the information of pneumatic trail, and it is used to decide whether the excitation is sufficient to adopt the friction coefficient estimate.

Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on.

Driver's driving style has a great impact on lane changing behavior, especially in scenarios such as freeway on-ramps that contain a strong willingness to change lanes, both in terms of inter-vehicle interactions during lane changing and in terms of the driving styles of the two vehicles. This paper proposes a study on game-theoretic decision-making for lane-changing on highway on-ramps considering driving styles, aiming to facilitate safer and more efficient merging while adequately accounting for driving styles. Firstly, the six features proposed by the EXID dataset of lane-changing vehicles were subjected to Principal Component Analysis (PCA) and the three principal components after dimensionality reduction were extracted, and then clustered according to the principal components by the K-means algorithm. The parameters of lane-changing game payoffs are computed based on the clustering centers under several styles.