There are various uncertain factors such as parameter perturbation and external disturbance during the steering process of a permanent magnet slip clutch electronically controlled hydraulic power steering system (P-ECHPS) of medium and heavy duty vehicles, which is an electronically controlled hydraulic power steering system based on a permanent magnetic slip clutch (PMSC). In order to avoid the immutable single assistance characteristic of a hydraulic power steering system, a PMSC speed-controlled model and P-ECHPS of each subsystem model were studied. Combined with non-singular terminal sliding mode and fast terminal sliding mode, an Adaptive Non-singular Fast Terminal Sliding (ANFTS) mode control strategy was proposed to control precisely the rotor speed of the PMSC in P-ECHPS, thus achieving better power control for the entire P-ECHPS system. The simulation results show that adaptive nonsingular fast terminal sliding mode control enables PMSC output speed to track the target speed. Compared with the non-singular terminal sliding mode control and the ordinary sliding mode control, the convergence speed has been improved by 66.7% and 84.2%, respectively. The rapid control prototype test of PMSC based on dSPACE (dSPACE is a development and verification platform based on MATLAB/Simulink software.) was carried out. The validity of the adaptive NFTSM algorithm and the correctness of the offline simulation results are validated. The adaptive NFTSM algorithm have better robustness and can realize variable assist characteristics and save energy.

Simultaneous localization and mapping (SLAM) is one of the core technologies to realize automated valet parking (AVP). Currently, advanced visual feature-based SLAM systems suffer from feature extraction failure and tracking loss due to the constraints of textureless scenes, unclear illumination, and dynamic conditions. To address these problems, this paper proposes a visual SLAM algorithm based on a semantic closed-loop detection algorithm using surround-view cameras and inertial measurement units (IMU) as sensors. The algorithm combines semantic features and the idea of inverse index to improve the traditional keyframes selection methods and the closed-loop detection algorithms, effectively avoiding the tedious and complicated feature point matching and improving the computational efficiency of the computer. Experiments show that the algorithm in this paper achieves better results in terms of precision and recall, absolute trajectory error (ATE), and relative pose error (RPE), and can meet the demand for SLAM and subsequent navigation in indoor parking lots.

Planning paths for Frenet-based autonomous vehicles (AVs) at roundabouts is difficult without complete and smooth reference paths. In such situations, the interpolating curve planner is often used to create segmented reference paths from simplified geometric roundabout data. While this method ensures curvature continuity within each curve segment, the continuity at the junctions of these segments is poor. Additionally, the determination of merging and diverging point positions at roundabouts has not been thoroughly explored. This paper introduces a novel approach using 5th-order Bézier curves to plan piecewise reference paths for AVs at roundabouts. The proposed method enhances endpoint curvature continuity of the Bézier curves and improves adaptability to non-standard roundabouts. A well-designed objective function is created to optimize both the geometric continuity parameters of the Bézier curves and the positions of merging and diverging points in the circulatory roadway. This function takes into account key factors, including path length and smoothness. Case studies validate the feasibility of maintaining curvature continuity at the endpoints and the method’s ability to generalize across various scenarios, proving its effectiveness for different roundabout structures. The results also confirm the method’s efficacy in generating paths from original geometric roundabout data. Lastly, the acceptable transverse deviations between real-world trajectories and reference paths demonstrate the rationality and practical applicability of this method.

Roundabouts require autonomous vehicles to adapt their speed to roadway geometry and nearby traffic. This study presents a longitudinal speed-planning method based on a modified Social Force Model (SFM) that reproduces speed-profile characteristics observed in human-driven roundabout trajectories. The driving-force component combines a geometry-conditioned desired-speed profile with a bounded harmonic term, and the repulsive-force component accounts for direction-dependent interaction with a nearby vehicle. The model was calibrated by nonlinear least squares using 336 retained trajectories from the rounD naturalistic driving dataset. Relative to calibrated constant-speed baselines, the in-sample Root Mean Square Error (RMSE) decreased by up to 65.5% (to 0.698 m/s) for straight maneuvers. The corresponding reduction for left-turn maneuvers was 64.0% (to 0.413 m/s). These results show improved agreement with observed longitudinal speed profiles at the studied site, while broader behavioral validity and cross-site generalization remain to be established.