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Heat generated by friction between the brake discs and the brake pad causes the disc temperature to rise, which affects the braking performance. This flux generated from the contact surface of the vehicle brake disc not only affects the braking performance but also tends to be wasted and pollutes the environment. However, an accurate system is needed to make efficient use of this generated heat flux, which is usually wasted. Thermoelectric generators (TEGs) are solid-state gadgets utilized in the conversion of heat to electricity. Hence, the aim of this study is to convert the heat flux generated at the disc contact surface into electrical energy by employing a thermoelectric generator. In Addition, the energy harvested energy to power the battery, which in turn charges the temperature monitoring systems. Thermoelectric generators were positioned at different geometrical points of the brake discs to achieve optimal efficiency and energy storage possibilities.

Lane changing manoeuvers is an essential rudiment in vehicle driving and has a significant impact on the characteristics of traffic flow. In the case of traditional cars, the driver operates the vehicle to complete the lane change whilst for autonomous vehicles, completing the lane change requires planning the lane change trajectory and controlling the vehicle speed during the lane change. Unreasonable lane change trajectory and vehicle speed may cause the vehicle to lose stability, threaten driving safety, increase energy consumption and waste energy. This paper considers the safety and economy of the lane changing process, and proposes a new lane changing method for vehicles.

As an important input parameter of intelligent vehicle active safety technology, road adhesion coefficient is of great significance in autonomous collision avoidance, emergency braking and collision avoidance, and variable adhesion road motion control. Traditional recognition methods based on vehicle dynamics require large data volume and low solution accuracy. This paper proposes an adhesion coefficient recognition method based on Elman neural network and Kalman filter. By establishing a seven-degree-of-freedom vehicle dynamics model, dynamic parameters such as yaw angular velocity, longitudinal velocity, lateral velocity, and angular velocity of each wheel, which are easy to measure and strongly related to the road adhesion coefficient, are analyzed as the input of the neural network model.