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.

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%.

To effectively evaluate the takeover risks of L3 autonomous vehicles under different cognitive secondary tasks, a study on the risk assessment model for driving takeover is carried out. The urban expressway emergency takeover scenario is designed and driving simulation experiments under different cognitive secondary tasks are carried out. The takeover risk assessment model considering trajectory field, potential field and behavior field is established. The validity of the proposed model is verified by adopting the takeover risk index method. Combined with the measured data, the influence of different cognitive secondary tasks and avoidance operation types on the strength of takeover risk field is quantized. The results show that the MW test and KS test for the distribution of the takeover risk index between 1 and 9 s after the takeover operation by the participants are both with the result of p<0.05, indicating that the model can effectively assess the takeover risk of the vehicle during the takeover process. In addition, the root mean square error of the takeover risk index (0.062) is smaller than the root mean square error of the inverse timetocollision (0.098), indicating that the model is better than the inverse timetocollision in accurately describing the risk. The research results can provide reference for vehicle operation risk assessment and collision avoidance design in takeover process.

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.

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.

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.