Sideslip angle estimation is vital to the safety and active control of autonomous vehicles. In this paper, an innovative vehicle kinematic-based sideslip angle estimation method is proposed. The method is built on multi-sensor fusion, which fuses the information of inertial measurement unit, global navigation satellite system (GNSS), and onboard sensors to continuously estimate the attitude and velocity of the vehicle and thus obtain the sideslip angle. The invariant extended Kalman filter framework is adopted and the left-invariant form is derived to implement the GNSS measurement update. In order to solve the unobservability problem when only a single GNSS receiver is equipped, the vehicle motion constraint is introduced into the filter to improve the accuracy of heading angle estimation. The method is validated by field test and the results show that the method is robust in terms of converge efficiency. Furthermore, the sideslip angle estimation accuracy is satisfactory with the average absolute error less than 0.25°, which meets the active safety control requirements for autonomous driving.

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The path control gives the target path through planning, and uses the tracking strategy to make the vehicle converge to the target path. How to balance the tracking performance and the vehicle stability is a crucial and worthy research for the autonomous vehicle safety. In this paper, a hierarchical path control strategy consists of path planning and tracking with the consideration of vehicle lateral stability is proposed. In the adaptive preview distance block, the preview distance is adaptively regulated according to the vehicle speed, sideslip angle, and the preview trajectory curvature to balance the tracking error and stability. In the path planning block, the three-order polynomial fitting method is adopted to give the desired path according to the preview distance and the relative position relationship between vehicle and road or obstacles. The linear quadratic regulator (LQR) controller is designed to tracking the desired path fully using the previewed curvatures and the vehicle motion error. The hardware in the loop (HIL) simulation and vehicle test results illustrate that the proposed strategy can deal with path tracking, avoidance and lance change scenes in low computation burden, and maintain the stability of vehicle simultaneously.

The traction control system (TCS) allows for better power performance and stability of the vehicle. On-demand four-wheel-drive (OD4WD) vehicles are expected to start up in complex situations such as only one wheel landing on the high friction road surface. Therefore, the TCS applied in OD4WD vehicles requires coordinated control of the engine, brake, and transfer case. If the controller is not well designed, the performance of the vehicle would decrease and the components may be damaged. Aiming to deal with the traction control problem of OD4WD vehicles, a coordinated controller is proposed in this paper. First, the models of the main components of the OD4WD vehicle are analyzed. Then, the coordinated controller is designed based on the active disturbance rejection controller (ADRC), the fuzzy controller, and the sliding-mode controller. The estimation and calculation methods of the necessary information are also designed. After that, vehicle experiments of the start-up process are carried out in typical situations to validate the effectiveness of the controller. Results show that the coordinated controller ensures power performance and improves the stability of the vehicle.