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.

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 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.

In recent years, the thermal runaway problem of lithiumion battery has become the main bottleneck restraining the development of power battery of new energy vehicles. In this paper, a comprehensive review of the research on the thermal runaway problem of the power battery of new energy vehicles is carried out, with the inducment of the thermal runaway of lithiumion battery expounded and the thermal runaway process of lithiumion battery and the characteristics of the thermal runaway of lithiumion battery under different variable conditions introduced. Based on the characteristic parameters of thermal runaway of lithiumion battery, the early warning methods and fire suppression methods applicable to lithiumion battery fire are reviewed, and the shortcomings and development trend of the current research on thermal runaway of power battery of new energy vehicles are summarized, providing certain reference for the development of power battery of new energy vehicles.

Finite Element Analysis (FEA), as an important Computeraided Engineering (CAE) technology, plays a significant role in the area of automotive part development. However, it costs too much time when solving complicated problems, which affects the development cycle. In this paper, a neural network method is proposed, in which sample data is provided by finite element simulation and the mapping relationship between finite element input and output is established by graph network technology. The graph network method is used to predict the stress field of the seat frame assembly. The prediction method simulates the connection relationship between nodes in the finite element model using graph nodes and graph edges, which can effectively express the topological relationship between elements in the finite element model. The prediction results are compared with the results of the finite element simulation. The results show that the method can precisely predict the maximum stress and its corresponding location of the seat frame assembly, with strong predictive capabilities for stress distribution consistency. Additionally, the model has a significant computational advantage, with a calculation speed three orders of magnitude faster than that of the corresponding finite element solver.

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.

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%.