The safety of automated vehicle running on the real road is related to traffic factors，driver status，and vehicle status. One major challenge faced by automated driving is that the actual traffic environment is characterized by spatial-temporal randomness of road morphology，natural environment，traffic participants and events. And the difference in complexity of testing scenarios results in the irreproducibility of the automated driving testing process and the incompatibility of testing results，which means that the evaluation of automated driving lacks a unified and quantified testing environment benchmark. In this paper，a scenario complexity calculation model for real road test based on operational design condition （ODC） is proposed. Considering the impact of network connectivity，driver perception ability，and vehicle execution ability on the complexity of automated driving vehicles facing relevant scenarios on actual roads，a complexity calculation model element database for autonomous driving actual road testing scenarios based on the eight major categories of road level，traffic facilities，temporary traffic changes，traffic participants，natural environment，network information，driver status and vehicle status. A scenario complexity computational model of real road test based on operational design condition and analytic hierarchy process （AHP） is established，The effect transmission mechanism based on intelligent and connected technology is adopted to calculate the weight coefficient of scenario elements，and the feasibility and rationality of the proposed method are validated in the real road tests.

The microstructure evolution and deformation behavior of resistance spot-welded joints of the two layer plates of 1500HS hot-formed steel sheets are studied in this paper. Through metallographic analysis，heat input distribution map，and alloy material property map，the microstructural changes at various positions relative to the weld nugget are analyzed. As the distance from the center area of the weld nugget increases，the microstructure of the welded joint can be divided into columnar crystal martensite，coarse-grained martensite，fine-grained martensite，ferrite-martensite dual phase microstructure and tempered martensite microstructure. Combined with Vickers hardness analysis，the differences in hardness under different organizational characteristics are clarified. The results show that the hardness decreases significantly in the ferrite martensite dual phase structure and tempered martensite region，which are the weak areas of the welding joints. Based on the experimental results of fusion size，maximum failure load，fracture surface macro-morphology，initial fracture location，and Vickers hardness of different plate thickness combinations，the influence of plate thickness and plate strength on the fracture mode，initial fracture location，and maximum failure load of the spot welded joints is explained.

The vibration characteristics of pure electric vehicles differ significantly from traditional internal combustion engine vehicles. In this paper，a research method suitable for optimizing the vibration isolation rate of mounting systems is proposed to address the insufficient vibration isolation rate of pure electric vehicle suspension. The vibration isolation rate in all directions of the engine mounting system and main factors affecting the vibration isolation rate of the engine suspension are analyzed. The vibration isolation rate of the rear suspension is defined as the optimization object，and the method of optimizing the jog stiffness of the passive side bracket installation of the suspension is proposed to improve the vibration isolation rate. Taking the optimal isolation rate and minimum mass ratio change as the optimization objectives，and using the NSGA-II multi-objective optimization algorithm，the target value of the jog stiffness of the passive side bracket installation is optimized，and the passive side bracket structure is adjusted according to the optimization results. The test results show that the optimized rear suspension Y-direction vibration isolation rate increase from 5.61 to 18.13 dB，and the driver's right side-ear noise decreases by 9.76 and 5.03 dB（A） in the 24th and 48th orders，indicating a significant improvement in driving comfort.

Suspension control requires good balance between ride comfort and driving stability，while considering system uncertainties，which is a complex task. In this paper，a disturbance observer-based suboptimal-nonsingular terminal sliding mode switching control algorithm （DOB-SNTSM） is proposed，with considerations of suspension dynamic performance indicators，algorithm robustness，and cost factors. Firstly，using spring mass acceleration information as input and by Kalman filter design，effective estimation of suspension deflection and spring mass velocity is achieved. Subsequently，a disturbance observer is devised to estimate uncertainties within the suspension system，with the disturbance estimation serving as feedforward compensation. Next，based on the sliding mode surface function，a suboptimal-nonsingular terminal sliding mode switching control algorithm is proposed，integrating with the feedforward compensation from the disturbance observer to formulate a novel active suspension control strategy. Finally，simulation and bench tests are conducted on both convex road surfaces and smooth random road surfaces. The results show that the introduction of disturbance observers can significantly improve the ride comfort index of the suspension. Compared to the SNTSM algorithm with the classical sky-hook control，the ideal state LQR method and without disturbance observer，the new algorithm not only effectively balances various suspension performance indicators but also achieves control effect close to the ideal state LQR using solely spring mass acceleration information. Additionally，the controller switching scheme significantly enhances algorithm robustness.

The vehicle dynamics during turning-braking maneuver are more complex than those on the straight lanes due to tire sideslip，load transfer and other factors. In depth investigation of the braking allocation strategy for enhancing the vehicle tracking performance in this maneuver is of great significance for driving safety. In this regard，a dynamic braking allocation strategy within electro-mechanical brake （EMB） is further investigated in this study. Firstly，the 2-DOF-vehicle dynamics model is taken as a reference，and the minimum lateral force requirements for stable driving of the front and rear axles are solved based on the model predictive control （MPC） algorithm. Then，the maximum longitudinal force available for braking each wheel is obtained by solving the friction circle online. Moreover，the braking allocation ratios are calculated according to the obtained maximum longitudinal force to realize the optimal braking allocation，The simulation and test results show that the proposed strategy enhances the vehicle tracking performance in turning-braking by dynamically adjust the braking force allocation ratios according to the driving conditions，load status and road adhesion conditions of the vehicle.

The axle and suspension are critical components of vehicles. To achieve real-time simulation of the solid axle and various suspension structures of a commercial vehicle，the property-based modeling technical route is adopted in this paper. The axle's movement is decoupled into motion kinematics and ride dynamics，while the suspension characteristics are divided into coupled carrying characteristics，RC/PC guiding characteristics，and coupled K&C kinematic characteristics. Innovatively considering the pitch dynamic effect of the axle，the nonlinear dynamic coupling relationship of suspension between axles，and the K&C coupling relationship of suspension between axles，a dynamic model for commercial vehicles is developed to trigger the negative phenomenon of brake vibration. Additionally，a K&C testing method for the coupled suspension and a method for model parameter identification are proposed. Finally，the accuracy of the model is validated at the system level by comparing K&C test data with TruckSim model results. Inputting vehicle parameters into the UniTruck software for simulation and comparing the results with TruckSim simulation，the model’s effectiveness is verified at the vehicle level.

Path tracking control is a key technology for intelligent vehicles. However，the existing vehicle tracking control methods mostly rely on more accurate vehicle control models，while actual vehicle control systems mostly have modeling errors，parameter perturbations and external disturbances，which significantly affect path tracking control accuracy. In this paper，a learning path tracking control method for intelligent vehicles considering unmodeled dynamics of vehicles is proposed. Firstly，a nominal model of the vehicle is established and a linear prediction model is used to approximate the compensation for the unmodeled dynamics of the vehicle to improve the accuracy of the vehicle model. Then，learning and updating of the parameters of the unmodeled dynamics are realized based on the principle of Extended Kalman Filtering. Next，learning Model Predictive Controller （LMPC） considering the unmodeled dynamics of the system is established. Finally，the effectiveness of the proposed method in improving the path tracking accuracy is verified by designing a joint simulation test with Carsim and Matlab/Simulink for multiple operating conditions and multiple groups.

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.

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.

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.