In order to extend the application scenario of model predictive control and improve the trajectory tracking accuracy of intelligent vehicles in extreme wind environment，an adaptive horizon control method considering crosswind stability is proposed. Firstly，taking the process of car overtaking on the sea-crossing bridge as the research object，the crosswind stability analysis model of car overtaking is established by using the coupling method of vehicle aerodynamics and system dynamics. Then，the safety risk model of vehicle lateral motion is established，and the adaptive horizon regulator is designed taking into consideration of lateral motion risk level，vehicle speed and lateral error，so as to realize the dynamic adjustment of prediction horizon and control horizon. Finally，CarSim and Simulink are used to build a joint simulation scenario，and the overtaking trajectory is planned by quintic polynomial to verify the tracking accuracy and robustness of the controller. The results show that compared with the fixed horizon and variable weight model predictive controller，the improved controller can better resist the aerodynamic interference of ' wind-vehicle-bridge' and improve the vehicle trajectory tracking accuracy at a lower real-time cost，with significant improvement in vehicle crosswind stability.

To consider both stability and trajectory tracking performance of autonomous vehicles operating in extreme conditions，a trajectory planning and control method based on autonomous drift is proposed. A neural network tire dynamics model is designed based on neural network to improve the accuracy of the traditional magic tire formulation. In order to further expand the stability boundaries under the extreme working conditions of autonomous vehicles，the drift stability boundaries are designed based on the tire saturation and maximum sideslip characteristics combined with the center-of-mass lateral deflection angle-transverse swing angular velocity phase plane constraints during drift，and the nonlinear model predictive control （NMPC） is used to plan a safe drift trajectory within a wider stability range，and the drift tracking control is carried out for the planned trajectory. The results of the joint simulation of Simulink/CarSim show that the method can fully utilize the advantages of drift motion to ensure that the vehicle does not go out of control under extreme working conditions，while accurately tracking the desired trajectory.

As global emission regulations and energy-saving policies become increasingly stringent， gasoline engines are facing significant challenges. The urgent technical challenge is to achieve high efficiency and ultra-low emission of gasoline engines. The pre-chamber turbulent jet ignition is one of the most promising technologies for improving the thermal efficiency of gasoline engines and reducing pollutant emission. In this paper， the influence of lean combustion limit expansion and ignition timing on the optimization of thermal efficiency is investigated systematically through three-dimensional flow simulation analysis coupled with a detailed chemical reaction mechanism. The results show that the passive pre-chamber can effectively expand the lean combustion limit， improve the thermal efficiency and reduce the pollutant emission of the engine in comparison with the spark ignition. At an excess air factor of 1.5， the maximum indicated thermal efficiency is 47.24%， which is 11.89% higher than that of the original engine， with the NO _x and Soot reduced by 29.27% and 98.76%， respectively.

To address the problem of speed trajectory deviation of connected vehicles （CVs） caused by human driver error， a real-time eco-driving strategy for connected mixed platoons considering human driver error is proposed in this paper. Firstly， real vehicle tests are conducted to collect human driver error data of different drivers to establish the human driver error model based on Markov chain so as to predict the human driver error for a period of time in the future. Then， with the optimization objective of minimizing the fuel consumption of the entire platoon， the platoon speed trajectory optimization problem is formulated as an optimal control problem. Fast stochastic model predictive control （FSMPC） is employed to calculate the optimal speed trajectories of the connected vehicle in the mixed platoon. Both the simulation and intelligent and connected micro-car test results indicate that， compared to the traditional eco-driving strategy based on fast model predictive control （FMPC）， the proposed eco-driving strategy can effectively reduce the speed trajectory deviation and fuel consumption of the whole platoon as well as meet the real-time requirements.

Current crash protection research for pedestrians has been conducted primarily on adults， with no classification of adults and children. In this paper， a numerical simulation model of human-vehicle collision is established according to the relevant crash provisions of Euro-NCAP， and the simulation analysis shows that universal airbags cannot effectively reduce the head injuries of adults and children at the same time. Therefore， an airbag energy absorption device for adult and child classification protection is studied according to the characteristics of airbags and the physiological differences between adults and children， and an improved YOLOv5 pedestrian target detection model is proposed to realize the classified recognition of adults and children. According to the classification results， the vehicle control module dynamically adjusts the parameters of the airbag energy absorption device， so that the device can be deployed to different states for adults and children respectively， realizing the classification protection of pedestrians. The results show that the designed target detection model is able to achieve the classification and recognition of pedestrians， with an increase of 3.11% and 4.32% in terms of adult and child category detection accuracy， respectively， compared with the initial model. After installing the airbag energy absorption device， the HIC value of the adult head can be reduced by a maximum of 63.4% and the peak acceleration can be reduced by a maximum of 61.7%， while the HIC value of the child head can be reduced by 31.4% and the peak acceleration can be reduced by 53.2%. The thesis research results can provide scientific theoretical support for the design of pedestrian active and passive safety protection devices.

With the rapid development of automated driving technology， ride comfort has become a key factor affecting user acceptance and overall experience with automated vehicles. In this paper， a comprehensive review of the current state of research concerning the evaluation of riding comfort in automated vehicles is presented. Firstly， the concept of comfort is thoroughly articulated， followed by an analysis of key factors influencing ride comfort. Subsequently， the quantitative indicators and evaluation models pertinent to automated vehicles are classified and elaborated in detail. The quantitative indicators are classified into four categories： subjective indicators， indicators derived from vehicle parameters， indicators based on physiological signals， and indicators related to driver behaviour. The evaluation models encompass psychophysical models， biomechanical models， statistical models， and learning-based evaluation models. Finally， prospective trends in the research of comfort in automated vehicles is brought forward， thereby offering a technical framework for further studies on the system design and user experience in this domain.

With the increase of new energy vehicles in the market and battery energy density， thermal runaway events gradually increase. Battery safety issues become particularly important， whereas leakage is one of the key factors inducing battery thermal runaway. In this paper， the influence of leakage on electrical performance and safety is studied by simulating leakage at the cells and modules. At the same time， based on experimental data and remote vehicle data， the characteristics of the leaked battery are extracted and the warning logic is established to achieve online monitoring of the leakage warning. For cells test， a comparative analysis is conducted on the test data of leaking and normal battery cells under cyclic and static states. It is found that compared with normal cells， leaking cells show mass reduction， thickness increase， capacity fade， DC internal resistance increase and dismantling characterization abnormity， which proves that leakage has certain impact on the electrical performance and safety. For module test， characteristics of the thickness and DC internal resistance changes from the parallel units with different leakage degrees are studied. It proves that the thickness and DC internal resistance increase with the increase of leakage degrees， which also augments the potential safety risk of the battery. For vehicle level big data， the pressure difference characteristics of the leaked battery during the starting and ending stages of charge are identified to establish warning and identification logic and conduct online monitoring.

Scenario-based simulation test method is an important means of automated driving vehicle safety verification； however， current test scenarios generation methods are mostly for independent scenarios. How to simulate the human real driving process to generate continuous interactive test scenario with challenges has become a problem that needs to be solved urgently in automated driving test evaluation. In this paper， an automated driving anthropomorphic continuous interactive test scenarios generation method is proposed. Firstly， the architecture for anthropomorphic continuous interactive test scenarios generation is established， and the vehicle motion behavior analysis is conducted based on the HighD dataset. On this basis， the current behavior of tested automated driving vehicle based on the trajectory similarity feature is analyzed， and the prediction of the future trajectory through the state transfer matrix is realized. Then， the type of the future behaviors of the traffic vehicles based on the trajectory interaction rules are determined， and the specific trajectory is generated by Transform network. Finally， the key performance indicators such as danger and anthropomorphism of the generated test scenarios are evaluated in simulation test environment， which proves the effectiveness of the method proposed in this paper.

In autonomous driving scene understanding task， accurate segmentation of drivable areas， dynamic and static objects is essential for subsequent local motion planning and motion control. However， the current general semantic segmentation method based on lidar point cloud cannot achieve real-time and robust prediction on vehicle-end edge computing devices， and cannot predict the motion state of objects at the current moment. In order to solve this problem， a multi-task segmentation network MultiSegNet for driving areas and dynamic and static objects is proposed in this paper. The network uses the depth map output by the lidar and the processed residual image as the representation of the encoded spatial features and motion features to input to the network for feature learning， so as to avoid directly processing disordered high-density point clouds. For the large difference in the number of target distributions in different directions of the depth map， a variable resolution grouping input strategy is proposed， which can reduce the amount of network computation and improve the segmentation accuracy of the network. In order to adapt to the size of the convolutional receptive field required for targets at different scales， a depth-value-guided hierarchical dilated convolution module is proposed. At the same time， in order to effectively correlate and fuse the spatial position and attitude information of objects in different time domains， a spatiotemporal motion feature enhancement network is proposed. The effectiveness of the proposed MultiSegNet is verified on the large-scale point cloud driving scene datasets SemanticKITTI and nuScenes. The results show that the segmentation IoU of driving area， static object and dynamic object reaches 98%， 97% and 70%， respectively， which is better than that of mainstream networks， with real-time inference realized on edge computing devices.

For the problems of path planning on curved roads， a path planning fusion algorithm based on global oriented artificial potential field method is proposed in this paper. Considering the curved road conditions， a grid map based on deformed grid is constructed. Considering the driving risk in the road environment， the heuristic function of A* algorithm is optimized based on the driving risk field theory. To improve the limitation and inherent defects of the traditional artificial potential field method， in view of the outline shapes of the subject vehicle， environment vehicles and obstacles， the artificial potential field method is improved as the local path planning method by introducing in the globally guided path. Taking the path planned by the improved A* algorithm as the global optimal guided path， the path planning fusion algorithm is designed based on the improved artificial potential field method. The simulation results show that the proposed fusion algorithm can generate effective and reasonable driving path， which is close to the real vehicle path extracted from the dataset. Moreover， the path planned in the environment with obstacles is safe and efficient， meeting the driving requirements of the vehicle.