Traditional 3D object detection methods in Cartesian coordinate systems often overlook the symmetry and continuity of the target from different perspectives to some extent during camera image encoding due to the fixed wedgeshaped imaging geometry of invehicle cameras. To address this, in this paper, PolarDet, an innovative endtoend 3D object detection method in polar coordinates based on position and semantic information weighting is proposed. This method generates BEV (Bird's Eye View) position and semantic information in polar coordinates through polar coordinate queries and predefined polar grid, which then interacts with the BEV information from the previous frame to incorporate temporal information. When outputting the final detection results, PolarDet performs a weighted sum of position and semantic information to enhance information utilization efficiency, allowing the network to achieve higher detection accuracy. Extensive experiments on the challenging BEV object detection nuScenes dataset show that the optimal model of PolarDet achieves a mAP (mean average precision) of 0.469 and an NDS (nuScenes detection score) of 0.56, significantly outperforming Cartesian coordinatebased BEV detection methods.

Commercial platoon cruise control is an effective method to improve transportation efficiency, but existing research is mostly based on homogeneous platoon control with one single vehicle following optimization objective, while using a simple architecture to cope with communication time delay, which is not universally applicable in practical scenarios. Therefore, based on heterogeneous electric commercial vehicle fleets, in this paper a distributed model predictive control strategy is proposed to achieve multiobjective control that takes into account of requirements of vehicle following, economy, and comfort. Delay buffers and compensators are designed for delay prediction models, effectively solving the problems of excessive tracking distance error caused by nonideal communication conditions. Matlab/Simulink simulation shows that the proposed control algorithm can achieve multiobjective optimization control of heterogeneous commercial vehicle fleets. Compared with traditional model predictive control (MPC), it significantly reduces the tracking distance error, energy consumption, and jerk, effectively improving performance of the platoon in terms of tracking, economy and comfort and significantly reducing the adverse effect of time delay.

In unstructured scenes, there are often obstacles of various sizes, and the path planning process that only considers obstacle avoidance methods such as detours will lead to decrease in vehicle traffic efficiency. For these problems, in this paper an intelligent vehicle path planning method with multiple obstacleavoidance modes is proposed by integrating a layered collision detection strategy into the traditional Hybrid A* algorithm. Firstly, a doublelayer grid map is constructed based on the vehicle chassis height, and a layered collision detection strategy is designed using the body contour and fourwheel contour. Then, through a welldesigned heuristic function and cost function calculation method, the Hybrid A* algorithm can efficiently search for paths in multi obstacle scenes. Finally, the gradient descent method is used to smooth and optimize the path. Simulation and real vehicle experiment results demonstrate the effectiveness of the proposed algorithm in improving path search efficiency and significantly enhancing path smoothness. Moreover, the planned paths consider both crossing and bypassing strategies for obstacle avoidance, enabling vehicles to have better passability in multiobstacle scenarios.

In this paper, an adaptive wheelbase preview robust H control method is proposed based on vibration based road roughness recognition to address the impact of unknown road surface input on the control effect of active suspension. By collecting the vibration acceleration response of the wheels through real vehicle experiments, the longitudinal road surface roughness information is identified based on the vibration based road surface roughness detection method of the front wheels. A speed adaptive wheelbase preview method is designed to obtain the delay relationship of the road surface excitation received by the front and rear wheels of the vehicle, providing real vehicle data for the wheelbase preview control of the rear wheel suspension. On this basis, a multiobjective speed adaptive wheelbase preview robust H. control method considering motion constraints is designed, and the optimal solution of parameters in linear matrix inequality (LMI) is achieved through multiobjective genetic algorithm (MOGA) to improve control accuracy. The experimental and simulation results show that the method proposed in this paper can accurately identify road roughness information and effectively improve suspension performance indicators and vehicle vibration frequency, effectively suppress vibration within the frequency range sensitive to motion sickness, and balance passenger driving experience while meeting driving smoothness requirements. Meanwhile, this method also provides a new approach for vertical vibration control of multi axle vehicles.

To solve the problem that a singlestage reduction hub drive system cannot meet the performance specifications of specialized vehicles, and that a twostage reduction would require the addition of extra control mechanisms, a novel coupled dual motor hub drive system scheme which is composed of two motors, a planetary gear mechanism, and a oneway clutch is proposed in this paper. The system is designed to operate in two coupling modes: torque coupling mode at low speed and speed coupling mode at medium and high speed with an autonomous mode switching capability as a functional requirement. Parameter matching is conducted with the objective of maximizing power utilization rate, and a control strategy for autonomous mode switching is developed. Under the same simulation initial condition, compared to the single motor two speed hub drive system, the coupled dual motor hub drive system exhibits an 81.25% reduction in maximum vehicle speed fluctuation and an 81.58% decrease in maximum acceleration during the mode switching process. An instantaneous optimal control strategy is established. Under the same operating conditions, the coupled dual motor hub drive system demonstrates a 21.42% reduction in energy consumption compared to the single motor twospeed hub drive system. Experimental tests are conducted using a prototype model to further validate the functionality and feasibility of the novel coupled dual motor hub drive system.

In order to meet the requirements of large output force value, high working frequency and good linearity of forcedisplacement of electromagnetic actuator for active mounting, a multiobjective parameter hierarchical optimization method is proposed to solve the problems of different influence of different structural parameters on optimization objectives, difficulty of expression of dynamic electromagnetic force by analytical formula, and difficulty of realization of optimal characteristics at the same time of the output force value, working frequency and forcedisplacement. In the upper layer, Taguchi algorithm is used to preliminarily optimize parameters, screen sensitive parameters and update the optimization range of high sensitivity parameters. In the lower layer, the backpropagation (BP) neural network prediction model is used to characterize the dynamic electromagnetic force, and the multiobjective genetic algorithm (NSGAII) is used to search and optimize the dynamic electromagnetic force. Through simulation and experiments, the results show that the parameters of electromagnetic actuator obtained by the optimization method in this paper have better comprehensive performance, which verifies the effectiveness of this method.

The accurate acquisition of tire body deformation has a crucial influence on the simulation accuracy of theoretical model, so the deformation rules and expression accuracy of different cord are studied by beam body model and finite element model. Firstly, a detailed theoretical model considering the flexible deformation characteristics of the beam carcass is established, and the expressions of tire cornering stiffness and driving/braking stiffness are obtained. Secondly, the tire finite element model is established, and the tire rubber and cord material parameters are accurately obtained to complete the comparison between the simulation results and the test data. On this basis, the finite element model of smooth tire with isotropic tread stiffness distribution is established, and the lateral stiffness, torsional stiffness and steadystate glide stiffness are simulated to obtain the lateral deformation of the tire under the action of lateral force and aligning moment, and the superposition principle of lateral deformation of different cord lines is verified. Then, the lateral deformation of different cord lines is fitted according to the established beam matrix model. Finally, the tread stiffness obtained by different cord lines is compared and verified by combining the flexural stiffness and slip stiffness models. The results show that the principle of deformation superposition is satisfied for different tire cord. The beam matrix model has a better expression precision for the lateral deformation of cord. The bending stiffness of cord shows a nonlinear decreasing trend with the increase of load, and the difference is small under large load. The calculation accuracy of tread stiffness obtained by different cord positions is different. The calculation accuracy of crown cord is the lowest at 93.6%, and the calculation accuracy of body 2 cord is the highest at 97.3%. The research position of the beam body model in the theoretical model is clarified in the study, improving the simulation accuracy of the theoretical model, and providing the reference for the study of tire dynamics.

For the problems of difficult and uncontrollable data acquisition, as well as limited quantity of available rainy day scene samples in the process of unmanned driving perception performance training, a multimodal fusion-based algorithm for constructing rainy day traffic scenes is proposed. Firstly, the rainy day scenes are analyzed and categorized into two models of rain line models and raindrop models for reconstruction. Secondly, a stochastic multisource fusion-based rain line model is proposed, which integrates rain effect from multiple directions and densities. Next, a heterogeneous mapping-based raindrop model is proposed to achieve realistic convex transparency mapping for individual raindrops, coupled with collision prevention design to mitigate cumulative errors of multiple raindrops in the same area. Finally, the two models are integrated to realize reconstruction of rainy day scenes by using various foundational forms. The experimental results show that as rainfall intensity increases, detailed information in the constructed scenes becomes richer initially, with metrics such as entropy and average gradient showing an initial increase followed by a decrease, while image quality continuously decreases, approaching realistic rainy day conditions. With higher rainfall intensity, both interference and detail in the images notably increase, with higher entropy and average gradient, as well as decreased PSNR and SSIM parameters, indicating significant image quality degradation.

The lateral, longitudinal, and yaw motions of corner module vehicles can be planned and controlled relatively independently. However, the impact of the trajectory on the vehicles' yaw motion is not adequately considered by traditional trajectory planning methods. A polynomial-based pose trajectory planning method for corner module vehicles is proposed in this paper. Firstly, a quintic polynomial-based pose trajectory parameter model is established to generate pose trajectory clusters, Then, considering the road adhesion state constraint, kinematic model constraint, and sideslip angle constraint, the evaluation functions including lane-changing efficiency, lateral performance, yaw angle deviation, and yaw performance are established to generate the optimal polynomial pose trajectory as well as the optimal classical position trajectory. Finally, the two optimal trajectories are compared in high-way lane-changing scenarios, and the traceability of the polynomial pose trajectory is verified using MATLAB/Simulink and CarSim co-simulation. The simulation results show that the efficiency of lane-changing can be increased by the polynomial pose trajectory, and the vehicle's yaw comfort and stability can be substantially improved.

Taking into account of factors such as core magnetic saturation，torque fluctuation，and component flexibility，an electromechanical coupling dynamic model for the switched reluctance motor-planetary gear electric drive system suitable for unsteady state conditions is established，with translational and angular displacements as generalized coordinates，which is validated through experiments. Through simulation analysis，the dynamic characteristics of the system under acceleration and variable load conditions are studied. The results show that under acceleration conditions，the speed at which the electric drive system is most prone to resonance is 3 900 r/min，at which multiple excitation frequencies cross the natural frequency of the system. Among them，the vibration energy generated by the excitation of the 15th order natural frequency at the gear mesh frequency is the largest，and the vibration energy is mainly concentrated in the θ_y direction of the planet carrier. At the moment of sudden load change，the system produces low-order free vibration dominated by the 5th order natural frequency，with vibration energy mainly concentrated in the θ_x and θ_y directions of the inner gear ring and gear housing.