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.

To improve the lateral stability of four-in-wheel-motor-drive vehicles，in this paper a chassis coordinated control strategy that integrates torque coordination and active rear steering is proposed. The strategy aims to track the ideal yaw rate and sideslip angle while effectively reduce the body roll motion. Based on the characteristics of the vertical reaction force generated by the in-wheel motor，the decoupled control for the longitudinal，yaw and roll motions of the vehicles is designed based on torque coordination. To decrease the effect of ignored nonlinearity and uncertainty in modeling lateral dynamics on the control performance，a disturbance observer-based model predictive control for chassis cooperative control is designed to estimate and compensate the nonlinearity and uncertainty. To verify the effectiveness of the proposed method，a hardware-in-the-loop test is conducted for the double lane change maneuver. The results show that the proposed control strategy can improve the lateral stability and reduce the roll motion of the vehicle body. Furthermore，compared to the control without disturbance compensation，the disturbance observer-based control reduces the tracking errors of the desired yaw rate and sideslip angle by 56.9% and 27.3%，and the body roll angle and roll rate by 8.9% and 12.5%，respectively.

For the uncertainty of the shift control model and parameters and the existing unknown disturbances of the two-speed dual clutch transmission （2DCT） of pure electric vehicles， a linear active disturbance rejection controller （LADRC） method is proposed. Firstly， the dynamic model of the 2DCT shifting process is established， and the shifting process is analyzed. Then， considering the uncertainty and unknown disturbance of the control model， the LADRC controller is applied to the shifting process to track the desired rotational speed， and the Extended State Observer （ESO） is used to estimate the disturbance in real time and compensate for it. Finally， it is compared with the PID control. Simulation and experimental results show that the controller has smaller tracking error and strong robustness， which ensures good gear shifting quality.

Based on the current assessment status of the differential system of new energy vehicles， combined with the invalidation problem of the differential system installed on the cross-country type/sport sedan type， in the process of cross-country scene/racing track scene driving test， the application scenes， working principles， damage mechanisms and invalidation forms of the differential system installed on the cross-country type are analyzed in this paper. The Archard damage model is used for damage verification. Based on clustering analysis， the massive real automobile testing data collected from road test are screened and extracted， and then transformed into powertrain bench verification test conditions. The fault of the differential system of the road test automobile is reproduced， forming the reliability assessment ability of the differential system in the sand off-road scenario of new energy vehicles， which provides support for the selection and optimization of differential systems and the development of new automobile models. Meanwhile， this article also has certain reference value on how to improve the performance and reliability of differential systems.

Air conditioning system as a key subsystem of environmental regulation within the entire vehicle system， the carbon emission of air conditioning system throughout its entire lifecycle is crucial for meeting the environmental protection and emission requirement of electric vehicle. Combining the life cycle climate performance （LCCP） model of electric vehicle air conditioning system with relevant data， the LCCP values in different provinces of China are analyzed in this paper. Besides， the LCCP values under two different heating schemes and different carbon intensities of electricity are compared. The results show that low-GWP mixed refrigerant RE170/R134a （RE170 to R134a mass fraction ratio 90∶10） can lead to a decrease in LCCP values for electric vehicle air conditioning system by 11.2% to 28.1% in China. Replacing the PTC heater with the heat pump results in a decrease in LCCP values by 0 to 33.1%. In addition， considering the future changes in China's carbon intensity of electricity and the proliferation of electric vehicles， it is anticipated that the LCCP values for an individual vehicle by 2035 will decrease by 31.7% to 39.3%， while the gross electric vehicle LCCP values in China will increase significantly.

For the problem of difficulty in balancing accuracy and real-time performance of deep learning models for intelligent cockpit driver expression recognition， an expression recognition model called EmotionNet based on attention fusion and feature enhancement network is proposed. Based on GhostNet， the model utilizes two detection branches within the feature extraction module to fuse coordinate attention and channel attention mechanisms to realize complementary attention mechanisms and all-round attention to important features. A feature enhanced neck network is established to fuse feature information of different scales. Finally， decision level fusion of feature information at different scales is achieved through the head network. In training， transfer learning and central loss function are introduced to improve the recognition accuracy of the model. In the embedded device testing experiments on the RAF-DB and KMU-FED datasets， the model achieves the recognition accuracy of 85.23% and 99.95%， respectively， with a recognition speed of 59.89 FPS. EmotionNet balances recognition accuracy and real-time performance， achieving a relatively advanced level and possessing certain applicability for intelligent cockpit expression recognition tasks.

A trajectory planning method based on graph search and optimization is proposed for intelligent vehicle trajectory planning in dynamic unstructured environments. Firstly， the graph search method is employed to search for motion primitives for intelligent vehicles to obtain initial trajectories that conform to kinematic characteristics. Then， based on nonlinear model predictive control methods， the trajectory is optimized to obtain smoother and safer trajectories. In order to achieve rapid and secure expansion of primitives in dynamic unstructured environments， a method for primitive collision detection is proposed. This method uses obstacle expansion and grid discrete motion elements to perform static collision detection on irregular obstacles， and introduces in the concept of velocity obstacles to perform dynamic collision detection on dynamic obstacles in velocity space. The proposed algorithm is compared by simulations in ROS/Gazebo environment， and is evaluated by field tests. The results show that compared to the TEB algorithm， the proposed trajectory planning method improves the average obstacle avoidance success rate by 18% while meeting the real-time computing requirements， demonstrating higher safety obstacle avoidance ability and feasibility.

Complex disturbances such as external interference， model uncertainty and parameter perturbation directly affect the accuracy and driving safety of intelligent vehicle path tracking control. Commercial vehicles are more susceptible to complex disturbances during driving because of their load characteristics. A hybrid path tracking control method is proposed in order to improve the accuracy and smoothness of commercial vehicle path tracking. Firstly， a robust sliding mode controller based on extended observer and an incremental LQR controller with stable changes are established. Particle swarm optimization algorithm is used to tune the parameters of the incremental LQR. Then， in order to improve robustness while weakening chattering， a fuzzy controller is used to adjust weight coefficient between them according to vehicle speed and lateral error. Finally， simulation analysis and vehicle experiments are conducted. The experimental data shows that SMC+LQR has good control performance to cope with complex external disturbances.

The pre-chamber ignition system can enhance the lean mixture combustion， which will then improve the thermal efficiency significantly. Based on a self-developed active pre-chamber and its fuel supply system， the effect of premixed gas injection pressure and fuel ratio on combustion characteristics at the lean boundary of an engine with a compression ratio of 16 is respectively investigated. The experimental results show that among the three kinds of premixed gas injection pressure of 0.19，0.14 and 0.09 MPa， the indicated mean effective pressure is the highest， and the ignition delay time and combustion duration are the lowest at the injection pressure of 0.09 MPa， with the most stable combustion. When the proportion of premixed gas fuel increases from 0.54% to 2.69% gradually， the engine's indicated mean effective pressure （IMEP） increases first， and then decreases， reaching its maximum at a ratio of 1.61% when the combustion is most stable and both the ignition delay period and combustion duration are the shortest. The active pre-chamber can realize the stable combustion with a λ of 1.8. Indicated thermal efficiency increases from 32.9% to 39.4%， a relatively increase of 19.8% compared with that of spark plug ignition.

The road noise problem in the frequency range of 35-40 Hz generated by a certain vehicle model on rough asphalt pavement at a speed of 30 km/h is analyzed by simulation and testing methods. Based on structural noise transfer path analysis （TPA） and node contribution analysis， it is determined that the problem frequency noise is mainly caused by low-frequency vibration of the vehicle body sheet metal parts. Therefore， based on the partial resonance principle， a resonance structure that can be industrialized according to the shape and local space of the sheet metal is designed， which is composed of a metal bracket and a metal block. By attaching this resonance structure to the sheet metal area with a significant contribution， simulation results show that the structure reduces the vibration of the sheet metal， thereby optimizing the interior noise. The peak noise levels in the front and rear seats are reduced by 8.6 and 6.4 dB， respectively. Meanwhile， the vehicle test results indicate that the interior noise has been improved by 7.0 and 5.8 dB. This provides a new research method and optimization scheme for controlling low-frequency noise caused by vibration of sheet metal.