The precision of speed tracking in the longitudinal motion control of intelligent vehicles is affected by multiple sources of disturbances，such as model mismatch and changes in external environments. In this paper，a longitudinal motion anti-disturbance control method that combines disturbance observation and Model Predictive Control （MPC） algorithm is accordingly proposed. Firstly，the relationship between the longitudinal acceleration of the vehicle and various forces is analyzed according to the longitudinal dynamics model of the vehicle，and then it is simplified into a particle motion model with multiple sources of disturbance and a model predictive controller is designed as the upper controller. Secondly，for the internal unmodeled dynamic disturbances and external random disturbances，a linear extended state observer （LESO） is designed to perform real-time estimation and compensation is implemented through a feedforward loop. The closed-loop stability of MPC and the convergence of LESO are analyzed，and finally a model predictive optimal regulation control law of disturbance compensation and state feedback is formed. Furthermore，in order to ensure efficient execution of the control strategy，a first-order anti-disturbance controller is proposed as the lower controller to convert the desired acceleration into engine torque，thereby realizing closed-loop control of the vehicle speed. Finally，the algorithm is deployed on a in-vehicle Microcontroller Unit （MCU） and tested on a real vehicle under multi-speeds and road conditions. The results show that the proposed strategy can quickly and accurately track the target vehicle speed，with excellent anti-disturbance ability.

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.

The road friction coefficient is a significant factor that impacts the decision-making control strategy of the autonomous driving system. To achieve prospective and high-precision perception of the road friction coefficient，a novel estimation method for road friction coefficient based on the LiDAR equipped in vehicles is proposed in this paper. Firstly，a road dataset is constructed by collecting data from dry asphalt，concrete，wet asphalt，icy，and snowy road surface. Then，road point cloud is extracted using cloth simulation filtering and RANSAC algorithms，and abnormal noise points are removed based on Gaussian filtering. The road surface is divided into different regions according to the variation of point cloud reflectivity with distance and incident angle，and features are extracted accordingly. A road recognition model is constructed based on the deep neural network and trained by the collected dataset. Finally，the friction coefficient of the road ahead is determined based on the statistical experience of road material and peak friction coefficient. The test results show that the proposed algorithm achieves road type recognition accuracy of over 99.3%，with an average running cycle of 55ms，enabling real-time and high-precision estimation of the road peak friction coefficient.

In order to enhance the ability of the intelligent control system to predict the driver's steering intention during the co-driving，an indirect shared lane-keeping robust control algorithm is thus proposed in this paper. Firstly，a driver steering model that mimics the driver’s steering behavior is introduced，with the parameters identified offline by the immune genetic algorithm （IGA）. Then a linear time-varying human-vehicle-road model for derivers in the loop is established. Secondly，considering factors such as road curvature disturbance under complex working conditions，insufficient adaptation of the linear model，and time-varying characteristics of the model parameters，an output feedback \gamma suboptimal H_∞ robust controller based on T-S fuzzy control theory is designed. Then，by fully taking into account both the driver’s steering behavior and the vehicular comprehensive lateral error，a human-vehicle control allocation strategy is designed to realize dynamic smooth allocation of driving control rights. Finally，the robust control algorithm is validated and studied based on the driver in the loop integrated platform. The results show that the robust control algorithm using the human-machine control allocation strategy has good disturbance suppression effect and can effectively enhance cooperation during the co-driving process，improving the friendliness of human-machine cooperation.

Accurate prediction of the future trajectory of surrounding vehicles is crucial to the decision-making and motion planning of autonomous vehicle. Existing research tends to use Recurrent Neural Networks （RNN） to model the time interaction of vehicles，but its interpretability of vehicle interaction modeling is poor，ignoring the actual lane structure，and there are deficiencies in capturing the interaction between vehicles and the environment. To address this problem，in this paper，a vehicle trajectory prediction model based on graph convolutional interactive networks that considers lane topology constraints is proposed. The vehicle interaction relationship extraction module adds edge weights when constructing the spatial relationship of vehicles to consider their neighboring interaction，making the interaction more interpretable. The driving scene representation module aims to improve the accuracy of vehicle trajectory prediction by extracting lane topology from high-precision maps. The trajectory prediction module integrates the output of the above two modules and outputs the predicted future trajectory. This integration allows for more precise modeling of the interaction between road structures and vehicle driving trajectories. The experimental results show that compared with mainstream methods，this model has achieved good performance on the Argoverse dataset，improving the accuracy and rationality of vehicle trajectory planning under complex road structures.

In order to enhance the economic performance of pure electric vehicles （EVs） while maintaining better dynamic performance，a real-time shifting strategy based on driving cycle recognition is proposed for the self-developed two-speed dry dual clutch transmission （2DCT） for EVs. A radial basis neural network is adopted to predict the vehicle speed and the optimal shifting points are extracted by dynamic programming for seven types of driving cycle. Then，a driving cycle recognition model based on similarity comparison is constructed to recognize vehicle-driving conditions so as to achieve real-time shifting. The simulation based on MATLAB/Simulink and the 2DCT bench experiments are completed. The results demonstrate that the proposed real-time shifting strategy based on condition recognition can simultaneously meet the requirements of economic performance and shift frequency.

The module arrangement of passenger car power battery packs usually has three different forms： single-layer arrangement，duplex arrangement and local duplex arrangement. The local duplex arrangement，which combines the characteristics of the other two methods，is more widely used. However，the study of electric vehicle fire accidents shows that battery packs with local duplex arrangement account for a higher proportion of spontaneous combustion accidents，which indicates that this arrangement may adversely affect the thermal uniformity of the battery module. In view of this，in this paper taking an electric vehicle battery pack with local duplex arrangement as the research object，a three-dimensional numerical model of the battery pack is established and the accuracy of the model is verified by comparing the experimental data with the simulation results. Using the validated model，the module temperature distribution characteristics of the battery pack under fast charging and three discharge rates are analyzed by numerical calculation methods，revealing the thermal uniformity of the local duplex arrangement of the modules under these conditions，especially the with the temperature difference of the double-layer module in the local duplex module larger than that of the single-layer module. In addition，the effect of coolant inlet temperature and flow rate on the thermal uniformity of the modules is explored respectively. It is found that attempting to reduce the coolant inlet temperature to improve the thermal uniformity of the module has limited effect，while increasing the coolant inlet flow rate can only reduce the temperature difference of the module under high discharge rate conditions，without obvious effect under low discharge rate conditions. This study provides a meaningful reference for the development and design of the battery thermal management system with local duplex modules.

Accurately predicting the remaining useful life （RUL） of lithium-ion batteries is important for the efficient and safe operation of energy storage systems. For the deficiencies of existing data-driven methods for RUL estimation，which extract aging features non-comprehensively enough and need to predict health state changes before estimating RUL，a RUL estimation method using multi-dimensional and multi-scale features is proposed in this paper to directly estimate the RUL of a battery using constant-current charging voltage segment data. The model maps RUL by extracting aging features of voltage segments using different scale convolution operation after dimensionally transforming the data. The model is validated using publicly available datasets from the University of Oxford，NASA，and the University of Maryland. The validation results show that the model can directly estimate the RUL of the batteries using the voltage segment data without the need of the current historical SOH of the batteries as the training data，which has higher accuracy and universality compared to fixed scale features based on a single dimension.

The biomechanical human body model with detailed anatomical characteristics is urgently needed computational tool for the intelligent and digital development in the field of automotive safety. In this study，based on the latest Chinese adult body size standards，a biomechanical model of the 50^th percentile Chinese male anthropometry car passenger （TUST IBMs M50-O） is developed with independent intellectual property rights. By reconstructing 3 sets of frontal blunt impacts，5 sets of side blunt impacts，and 3 sets of sled cadaver or volunteer tests，the C-NCAP deformable moving barrier side impact test is simulated to verify the effectiveness and application value of the developed model from multiple angles and all directions. The results show that all the 11 sets of reconstructed experimental data are within the corresponding cadaveric and volunteer corridors，with an average difference of approximately 10%，verifying the effectiveness of the model. The TUST IBMs M50-O model and WorldSID 50^th model exhibit the same kinematic responses in the side impact simulation. However，due to the compression from the upper extremity and elbow joint of the TUST IBMs M50-O model，the peak resultant accelerations of T4 and T12 thoracic spine reach 43.5g and 47.3g，higher than the values of 38.5g and 41.2g of the WorldSID 50^th model. The TUST IBMs M50-O model is also used to analyze the human tissue level injury risks based on stress and strain. In conclusion，the TUST IBMs M50-O model exhibits high biofedility and can be used to investigate the impact of injury mechanisms and virtual testing. As a reliable computational tool，this model can offer technological support to the research and development of safety protection devices in intelligent vehicles and intelligent high-end equipment.

Driver posture in frontal crash conditions with and without autonomous emergency braking （AEB） has a significant impact on kinematic response and injury risk. In this paper，the THUMS （Ver.6.1） human finite element model is used to establish three driving postures，including standard，rearward recline，and forward recline，and a frontal collision constraint system model is established to conduct six sets of 50 km/h simulation tests for comparative analysis of the kinematic response of different driver postures with and without AEB，as well as the injury parameters of driver’s head and chest. The results show that the risk of head injury is highest in the recline posture with and without AEB intervention，with the HIC₁₅ of 817.5 and 626.9 with and without AEB，respectively. The intervention of AEB has the greatest effect on the driver's chest compression，which is increased by 89%，115%，and 22% for the three postures，respectively. The chest compression in the reward recline posture suffers the most serious injury. The results clarify the effect of driving posture and AEB on driver kinematic response and head and chest injuries，providing a reference value for the development and design of automotive restraint systems and AEB.