With acceleration of the urbanization process, pedestrianvehicle conflicts have become a significant issue that modern society urgently needs to solve. In complex traffic scenarios, pedestrian crossing behavior leads to frequent traffic accidents. Accurately and timely anticipating pedestrian crossing intentions is crucial for avoiding pedestrianvehicle conflicts, improving driving safety, and ensuring pedestrian safety. An Efficient ActionConditioned Interaction Pedestrian Crossing Intention Anticipation Framework (EAIPF) is proposed in this paper to anticipate pedestrian crossing intention. EAIPF introduces in a pedestrian action encoding module to enhance the representation ability of multimodal action patterns and discover deep skeletal context information. At the same time, the scene object interaction module is introduced to explore interaction information with objects and understand advanced semantic clues in traffic scenes. Finally, the intention anticipation module fuses pedestrian action and object interaction features to achieve robust anticipation of pedestrian crossing intentions. The proposed method is verified on two public datasets, JAAD and PIE, achieving the accuracy of 89% and 90%, respectively, indicating that the proposed method can accurately anticipate pedestrian crossing intentions in complex traffic scenarios.

The shared cars have the characteristics of standardized vehicle models, diverse users, and high frequency of usage. To enhance the efficiency of shared car usage and the driving experience, the development of an intelligent seat that can automatically adjust the comfortable sitting posture based on the driver's characteristics has significant importance. For the issue of subjective and arbitrary seat adjustments that lead to discomfort for drivers, in conjunction with the intelligent seat project for shared cars, bench experiments are carried out taking the four variables of Hpoint foreaft position, Hpoint vertical position, seat cushion angle, and backrest angle as the research factors in this paper. The study examines the impact of seat parameters on seat comfort for drivers at the 5th, 50th, and 95th percentiles. Firstly, three experimental levels are chosen for each research factor within the reference vehicle range. Orthogonal experimental design is employed to simplify the number of experiments required. The required number of orthogonal groups is determined, and orthogonal bench experiments are conducted. Experimental participants subjectively evaluate the seat comfort for each group. Additionally, pressure mapping on the seat cushion and angle measurements using a digital angle gauge are employed to collect information of participants' pressure distribution and joint angles. Secondly, clustering analysis is conducted to determine the subjective and objective comfort evaluation system employed in this study, using joint angle as the objective parameter. Through the analysis of the orthogonal experiments, it is concluded that different seat parameters have varying effect on seat comfort. Furthermore, the parameters that affect seat comfort also vary with changes in the driver's percentile. Finally, the data obtained from the orthogonal experiments are adjusted and fitted to establish the prediction models for the optimal Hpoint position and backrest angle of the seat.

In order to enhance the comfort and user acceptance of intelligent connected vehicles, a cognitive process model for passenger carsickness mitigation is established in this paper and an olfactory stimulationbased mitigation strategy is proposed. Firstly, a screening experiment of olfactory stimulation materials for carsickness relief is designed and carried out, fully considering occupant satisfaction as well as the functional requirements of carsickness relief, to select the odor type and concentration of olfactory stimulation materials. Then, a carsickness mitigation experiment based on a real car is carried out to investigate the mapping relationship between different degrees of carsickness and physiological representations and further validate the efficiency of the olfactory mitigation method. The results show that the subjective carsickness degree is positively correlated with GSR activation and negatively correlated with EEG asymmetry. Moreover, releasing 20% ginger blossom odor for 10 seconds can effectively relieve carsickness symptoms, with an average relief effect of 22.17%, which is verified in terms of subjective and objective dimensions.

In order to comprehensively review the current status and clarify the future trend of fault diagnosis in the electric drive system of pure electric vehicles, this paper first introduces the basic structure, functions and development history of the electric drive system of pure electric vehicles; then summarizes in detail the types and causes of faults of crucial components of the electric drive system of pure electric vehicles, and analyzes the main research status quo of fault diagnosis methods for key components of the electric drive system of pure electric vehicles. Then the domestic and international research progress and development of the diagnosis methods of the pure electric vehicle electric drive system are reviewed in detail from the four aspects of expert knowledgedriven, modeldriven, signaldriven and datadriven, with the advantages and disadvantages of different methods compared. Finally, the problems faced by the fault diagnosis of electric drive system of pure electric vehicles and the development direction are analyzed and foreseen, and it is further discussed and pointed out that the future research on the fault diagnosis of electric drive system of pure electric vehicles can be focused on variable condition coupled fault diagnosis, microfault and prefault diagnosis, realtime online fault diagnosis, intelligent operation and maintenance, unknown fault diagnosis and system selfhealing technology, etc.

The traffic scheduling problem in timesensitive networking (TSN) of automotive electrical and electronic architecture is investigated in this paper. To meet practical application requirements, a method for establishing the topology of invehicle TSN network is proposed. To address the multitype traffic scheduling problem in the network, a traffic scheduling strategy based on the TimeAware Shaper (TAS) mechanism is proposed, and the corresponding mathematical model is established, to reduce the total network delay while considering both the time sensitivity of highpriority traffic and the data integrity of lowpriority traffic. To solve the problems of unstable solution efficiency caused by the complex information flow forwarding process in the model and the difficulty of optimization caused by numerous traffic scheduling solutions, an improved genetic algorithm (IGA) is proposed which is optimized from the aspects of setting adaptive crossover probability formula, introducing in taboo search mutation, and combining multiple populations. The experimental results show that the proposed algorithm improves the optimality by 43.47% in endtoend latency optimization and the solution generation stability by 76.96%. The algorithm can obtain lowlatency and highreliability traffic scheduling solutions for invehicle TSN. The research findings of this paper provide insights for the study of intelligent connected vehicles and the optimization of invehicle network communication algorithms.

For the problems of dense targets, severe edge occlusion, and blurred foreground and background that intelligent vehicles face in actual traffic environments, a lightweight object detection algorithm based on image saliency feature fusion is proposed in this paper. Firstly, salient feature maps are extracted based on grayscale images, and input into convolutional neural networks with color images. Secondly, a lightweight fusion network is constructed using the Ghost Model, and the EIoU is used to optimize the model's border localization loss. In order to enhance the detection accuracy of similar occluded targets, nonmaximum suppression algorithm is improved on the backend of the network. Finally, the KITTI dataset is used for training and testing. The experiment shows that the improved detection mAP value of the network reaches 92.7%, with an average accuracy improvement of 3.8% compared to the original network YOLOv5. The accuracy and recall rates are increased by 3% and 6.2%.

In order to realize effective vibration mitigation of selfpowered semiactive suspension under uncertain factors, the suspension mechanicalelectrical coupling dynamic model is established. The influence of electrical parameters on energy conversion efficiency is explored. The adaptive fault tolerant control gain is deduced, and then the vibration isolation capability of the suspension is investigated in time and frequency domain respectively. The robust index of adaptive optimal fault tolerant control algorithm is obtained by constructing Lyapunov equation to stud the influence of key parameters on robust index. The results show that the electrical parameters have obvious influence on the energy conversion efficiency, with the suspension having higher energy conversion efficiency at the second natural frequency. The proposed adaptive optimal fault tolerant control strategy can realize effective vibration suppression in both the time and frequency domain, with better vibration isolation performance compared to passive control and selfpowered mode. The control robust index is affected by the inductance of generator and outer diameter of permanent magnet most significantly.

In order to eliminate the influence of parameter uncertainty and external uncertain disturbances on the handling stability of fourwheel steering vehicles, a nonlinear integral sliding mode control method based on kalman filter extended state observer for fourwheel active steering is proposed. Firstly, the kalman filter extended state observer (KFESO) is designed to realize the vehicle state observation and external disturbance estimation, which overcomes the disadvantage of traditional kalman filter algorithm's dependence on highprecision models. Secondly, to reduce the tracking control error of the target ideal state of the vehicle caused by disturbances, the disturbance observed by KFESO is compensated to the control input. In order to realize global robust control and suppress integral saturation, a nonlinear integral sliding mode control method based on exponential convergence is designed. Finally, hardware in the loop test results indicate that the KFESO has high observation accuracy in the presence of internal and external uncertain disturbances in the system, and the KFESOISMC method has excellent antiinterference performance in controlling the stability of fourwheel active steering compared to LQR and ISMC methods.

Conventional braking safety detection usually uses long and extreme working conditions but may result in a loss of accurate working range. To address this deficiency, firstly, a shorttime test cycle for stabilizing pedal mode test method is developed, which incorporates existing test standards , not limited to a single extreme braking mode but taking into consideration of fast steadystate operation of electric vehicles. Then, running fragments based on machine learning are regressed, and the shorttime test cycle is constructed by fusion and splicing. Also, an improved braking safety detection method is proposed with shorttime test cycle, which reduces the dimension of the characteristic parameters of the braking segments by the principal component analysis, while the hidden danger is judged by calculating the repeatability distance of braking segments based on the characteristic parameters. Finally, the effectiveness of the proposed shorttime test cycle and detection method is verified by means of following test on a test bench.

In the early design stage of the vehicle body, in order to assess the impact of the extreme pothole road conditions on the vehicle body structure, according to the related universal global pothole road test standard, for the extreme impact of the pothole#3, the body structure failure of a vehicle in the road test of pothole#3 is taken as the research object in this paper, to find out the shortcomings of the traditional sheet metal failure criteria CAE method, which can't reproduce the problem of test failure. The Fracture Forming Limit Diagram (FFLD) is established through a large number of sheet metal coupon tests as a new CAE method for sheet metal failure criteria. Then, based on this new CAE method, the Virtual road load data of pothole#3 calculated by the vehicle dynamics discipline is taken as load input to carry out finite element simulation analysis on the body structure, and the test failure is successfully reproduced. According to the analysis results of the new CAE method, the body structure is improved, and the pothole#3 road test certificate is finally passed. The test and the finite element analysis have high correlation. It is proved that the method can accurately predict the real damage condition of sheet metal under complex deformation conditions in the early stage of body development, thus reduce the risk of body structure failure in the later test.