Drag torque can cut down the efficiency of the vehicle transmission system. So it should be reduced on the premise of normal vehicle lubrication and heat dissipation. The purpose of this paper is to analyze the variation rule of drag torque in single-plate wet clutch from the perspective of flow rate, density, and viscosity. In the theoretical model of drag torque, the flow field is divided into oil film area and cavitation zone. Based on the mass flow conservation and viscosity–pressure equation, the equivalent density and viscosity of each node in each region are obtained. Then the total drag torque is calculated through the sum of each node’s torque. Finally, the curves of drag torque with rotational speed under different working conditions are obtained by numerical calculation, and they have been tested and verified. Through this research, the following conclusions can be reached: the rotational speed of the peak of drag torque is near the maximum speed where full oil film lubrication is realized. While temperature declines, it will lead to higher viscosity, then the speed of the lubricant along the radial direction will decrease, resulting in the increase in maximum speed of full oil film lubrication under the same oil supply; accordingly, the peak of drag torque will rise. If the flow rate of oil supply is increased, the maximum speed where full oil film lubrication is realized will be higher; consequently, the maximum drag torque will be improved. When clearance becomes wider, the cross-sectional area of the radial flow will be larger, then the maximum speed of full oil film lubrication under the same oil supply will be decreased, thus the peak of drag torque will decline.

The PointPillars algorithm can detect vehicles, pedestrians, and cyclists on the road, and is widely used in the field of environmental awareness in autonomous driving. However, its feature encoding network only uses a minimalist PointNet network for feature extraction of point cloud information, which does not consider the global context information of the point cloud, and the local structure features are not sufficiently extracted, and these feature losses can seriously affect the performance of the object detection network. To address this problem, this paper proposes an improved PointPillars algorithm named TGPP: Transformer-based Global PointPillars. After the point cloud is divided into several pillars, global context features and local structure features are extracted through a multi-head attention mechanism, so that the point cloud after feature coding has global context features and local structure features; the two-dimensional pseudo-image generated by this feature is used for feature learning using a two-dimensional convolutional neural network. Finally, the SSD detection head is used to achieve 3D object detection. It is demonstrated that the TGPP achieves an average accuracy improvement of 2.64% in the KITTI test set.

Most existing Path-Following Algorithms (PFAs) are developed for single-unit vehicles (SUVs) and rarely for articulated vehicles (AVs). Since these PFAs ignore the motion of the trailer, they may cause large tracking deviations and ride stability issues when cornering. To this end, an Adaptive Articulation Angle Preview-based Path-Following Algorithm (AAAP-PFA) is proposed for AVs. Different from previous PFAs, in this model, a simple linear vehicle dynamics model is used as the prediction model, and an offset distance calculated by an articulation angle is used as part of the preview distance. An adaptive posture control strategy is designed to trade off the trajectory tracking performance and lateral stability performance during the path-following process. Considering a large prediction mismatch caused by using a linear vehicle dynamics model, a feedback correction method is proposed to improve the robustness of the steering control. In the comparison simulation experiment with SUV-PFA, it is confirmed that the novel PFA has better adaptability to the contradictory relationship between tracking performance and lateral stability and has strong steering control robustness.

To address the issue of incorrect recognition in the automatic emergency braking (AEB) systems on curved roads, a target recognition model is proposed to obtain the road curvature and to calculate the relative lateral distance. Based on the information from the ego vehicle and the preceding vehicles, the accurate selection of the hazardous target is accomplished. After identifying the dangerous target, a control strategy based on the fusion algorithm is proposed, because the safety distance model and the Time-to-Collision (TTC) model both have their limitations and cannot ensure driving safety and comfort simultaneously. The TTC model is optimized according to the actual relative distance between two vehicles on curved roads, the graded warning strategy and braking intervention time are established by the TTC model. And then the graded braking strategy is designed according to the safety distance model. The simulation platform is built based on Carsim and Simulink for verification and analysis. The results demonstrate that the proposed AEB control strategy on curved roads can accurately and efficiently identify the target vehicles on curved roads, avoid false triggering issues, and improve the AEB system’s reliability. And effectively avoid collisions with target vehicles that are in the same lane, improving driving safety and comfort.

The wet clutch, a crucial component of the vehicle transmission system, experiences high-frequency impact and friction between the friction plate and the steel plate under high-speed separation conditions. This leads to a significant increase in drag torque, which is detrimental to the transmission efficiency and reliability of the high-speed wet clutch. This article aims to reduce the high-speed drag torque in a no-load wet clutch by optimizing the oil groove shape on the surface of the friction plate. Initially, a theoretical model of high-speed drag torque for the friction plate with arbitrary oil groove shape is established. Subsequently, a low-drag oil groove shape optimization model is proposed by establishing design parameters, constraint conditions, and an objective function, combined with an experiment design method, approximation modeling, and a global search optimization method. Finally, the stiffness and damping coefficients of the clearance flow field and the drag torque with the optimized oil groove shape are calculated. The simulation results and the proposed optimization method are validated through experiments. The experiment indicates that a friction plate with the optimized oil groove shape can significantly decrease high-speed drag torque, demonstrating strong practical engineering value.