The scenario-based simulation testing method has become the core idea for automated vehicle performance verification， which splits the continuous vehicle driving process to obtain non-repetitive and independent scenario segments and tests them in virtual environment. Fitting in with the test process， a comprehensive evaluation method for automated vehicle simulation testing in multi-logical scenarios is proposed in this paper. Firstly， a comprehensive evaluation method for automated vehicle in multi-logical scenarios is established and the scenarios weighting analysis process considering both the scenario's own characteristic information and simulation test process information is defined. Then， the logical scenario's own characteristic information weighting is built by exposure degree， control loss degree and hazard degree. The simulation test process information weighting is built by simulation accuracy information， element type information， parameter space information and discrete step information. Finally， the information of front braking， front left cut-in and front right cut-in scenarios is extracted based on HighD dataset and the scenario weight is calculated through the method proposed in this paper and base algorithm test results， and the two tested algorithms’ comprehensive evaluation results are obtained in multi-logical scenarios.

Trajectory planning for intelligent vehicles in dynamic environment needs to have good comfort and safety. Discrete sampling trajectory planning algorithms have been widely studied and applied due to high real-time performance and multi-objective optimality. However， it is found in simulations and real vehicle tests that the results of local trajectory planning using typical methods such as polynomial optimization suffer from poor consistency during transient processes like lane changing. In this paper， a "splice+rigid planning" trajectory planning algorithm that considers consistency evaluation is proposed. Specifically， historical trajectories are spliced with the current cycle trajectory based on the vehicle's state. Polynomial-based candidate trajectory clusters are generated by combining the spliced trajectory with sampled points from the trajectory end state， which serves as the rigid planning phase. Then， a trajectory consistency evaluation function is designed based on the lateral deviation of the trajectory to select the optimal driving trajectory with higher consistency from the trajectory cluster. The results of simulation and real road scenario tests show that the proposed trajectory planning method improves the overall consistency of the intelligent driving vehicle's trajectory while meeting requirements for trajectory safety， smoothness， and comfort.

Vehicle trajectory prediction is one of the key technologies for autonomous driving. In view of the problem that previous prediction models rarely consider multiple types of participants other than vehicles in urban road scenarios， a multimodal vehicle trajectory prediction model considering multiple types of traffic participants is proposed in this paper，. The historical trajectory information is encoded by gated recurrent units， and the features of multiple types of traffic participants are mapped into the driving scene expressed by a graph structure through the attention mechanism， with the context feature extracted by the graph attention network， so that the model can perceive different traffic participants in the environment. In addition， the final multimodal trajectory prediction results are output through the node trajectory prediction module and the coordinate trajectory prediction module. Experiments on nuScenes， a dataset under urban road scenarios， show that， compared to similar existing models， the model proposed has lower computational requirements and more accurate prediction， which is applicable to urban road driving scenarios with mixed human and vehicle traffic.

Considering the lack of extreme traffic scenarios in conventional vehicle trajectory prediction datasets， a novel adversarial attack framework to simulate such scenarios is proposed in this paper. Firstly， a threshold determination method is proposed to judge the effectiveness of adversarial attacks under different scenarios. Then， two adversarial trajectory generation algorithms are designed for different attack objectives， which generate more adversarial samples under physical and concealment constraints. In addition， three new evaluation metrics are proposed to comprehensively assess attack effect. Finally， different defense strategies are explored to mitigate adversarial attacks. Experiments results show that the Perturbation Threshold for Fast Attack （PTFA） algorithm and the Attack Algorithm Based on Dynamic Learning Rate Adjustment （DLRA） achieve shorter attack time and better perturbation effect compared to existing algorithms on the NGSIM dataset， discovering model vulnerabilities more efficiently. By simulating extreme cases， this research enriches trajectory samples， evaluates model robustness in-depth， and lays a foundation for further optimization.

The auto-lane change on highways is an important feature of the Advanced Driver Assistance System （ADAS）. The current algorithms are unable to balance lane-changing safety and smoothness under low computational hardware conditions. To solve the problem， a decision-making and planning method for auto lane-changing system on highways is proposed in this paper. Lane-changing decisions are made by dividing the lane changing collision risk into hierarchical danger zones， followed by the implementation of decoupled lateral and longitudinal planning. In the lateral planning， a two-stage fifth-order polynomial trajectory planning is designed to enhance safety and smoothness during the lane changing process. In the longitudinal planning， a PID-like algorithm is employed to enhance real-time planning for cruising conditions， while the Model Predictive Control （MPC） based on synchronized predictive time domains is employed for following conditions. By associating the lateral and longitudinal planning times to improve lane-changing smoothness， the design of cost function reduces computational complexity to meet low resource requirements. Through real vehicle comparison tests， this method has been validated to have high level of safety， smoothness， and user experience in various highway lane changing scenarios. Additionally， comparison results of static area storage and peak stack storage tests for the algorithm's resource utilization show a low hardware resource occupancy rate， meeting the requirements of low-computing-power controllers in terms of resource utilization.

With the emergence of automotive domain centralized architecture， the automotive domain controller equipped with AI chips has become the main control unit of the new generation of electrical and electronic architecture. The domain-centralized architecture relies automotive Ethernet connection， and computing nodes are widely distributed within AI chips of multiple domain controllers， which requires a distributed parallel computing model with service-oriented architecture for full release of the performance of computing nodes. In this paper， a lightweight communication middleware based on service-oriented architecture is developed， and a distributed parallel computing model based on this middleware is realized. The expression for calculating the partition coefficient of distributed computing is derived， and the effect of computing node step load fluctuations on the distributed computing model is studied. The result shows that the distributed computing model of domain controller service-oriented architecture has the ability of load balancing， which can achieve fast task reassignment for step load during the runtime of AI chips， ensuring the minimum computational cost and cycle time of cyclic tasks. The results provide important reference for design and development of distributed computing frameworks on automotive domain control architecture.

Torque control is the core function of the vehicle control unit （VCU）， which is crucial to ensure its safety. Therefore， for the problem of unexpected torque output anomalies in VCU， functional safety analysis is conducted in this paper based on the ISO 26262 standards， and a torque control three-layer monitoring strategy is proposed based on the EGAS architecture. Firstly， based on the definition of VCU items， the level of automotive safety integrity and safety objectives are determined through hazard analysis and risk assessment. Secondly， the fault tree analysis method is used to derive functional safety requirements and technical safety requirements. Once again， a functional safety mechanism based on the AURIX TC275 three-core main control chip and TLF35584 power monitoring chip is designed for safety objectives. In addition， CPU resources are allocated through a three-layer monitoring strategy to achieve the separation of basic torque control functions and monitoring functions. Finally， processor in loop testing is conducted， including UDE debugging， UDS diagnosis， and TLF35584 security state control testing. The results indicate that this three-layer monitoring strategy can achieve the basic function of VCU torque control and enter a safe state in a timely manner in case of faults， thereby achieving safety goals.

The evident frame flexibility of the commercial vehicle will affect the handling stability and ride comfort of the whole vehicle， especially during resonance when the frame flexibility amplifies the impact on the vehicle’s motion quality. The property-based modeling technical route is adopted in this paper to simulate the frame motion of the tractor-semitrailer in real time. Firstly， the movement of the frame is decoupled into low-frequency rigid movement and high-frequency flexible movement according to the frequency domain. Then a rigid frame module based on multi-body dynamics and a flexible frame module based on the modal synthesis method is modularly built. Afterward， the results of the two modules are superimposed to form a rigid-flexible combining frame model. Finally， the frame model is embedded into the tractor 89DOF-semitrailer 73DOF vehicle model to conduct simulation on a certain commercial vehicle. The model's effectiveness is verified by comparing it with the field test results of an actual vehicle.

For complex and uncertain situations related to the powertrain mounting system （PMS） of electric vehicle where some parameters are probabilistic variables， and some parameters are discrete data， a study on the reliability optimization design for the PMS of electric vehicles is conducted based on the probabilistic model and data-driven model. Firstly， based on the arbitrary polynomial chaos （APC） expansion and generalized maximum entropy principle， an efficient method is derived for solving the uncertainty and reliability of the PMS response under the aforementioned complex uncertain situation. Then， based on the Monte Carlo sampling， a reference method is proposed for performing the uncertainty and reliability analysis of PMS. Next， a sensitivity analysis method based on APC expansion method is proposed， and an optimization method of PMS is further put forward considering the uncertainty and reliability of responses. Finally， a numerical example is used to verify the effectiveness of the proposed method， and the sensitivity analysis and reliability optimization of the system are carried out. The results show that the proposed method can effectively handle the complex and uncertain situations where some parameters of the electric vehicle PMS are probability variables and some parameters are discrete data and can optimize the PMS inherent characteristics reliably with good computational accuracy and efficiency.

This paper focuses on the screening of parallel hybrid power system configurations employing the Ravigneaux planetary gear mechanism and traditional multi-gear transmission. A comprehensive evaluation method， integrating gear design rules， dynamic characteristics rules， working mode rules， and manufacturability rules based on the lever method， is applied to analyze and screen the hybrid power system configurations. The configuration scheme and screening process of this method are determined. Subsequently， in line with the vehicle's design objectives， parameters of the engine and motor are selected. The vehicle's longitudinal dynamics model is established using GT-SUITE software. On this basis， transmission parameters are optimized using the Pareto frontier method. Finally， upon determining the hybrid configuration， the design parameters of the hybrid power system are optimized and determined. The feasibility of the scheme is validated through simulation， providing a theoretical basis for the research of parallel hybrid power systems using the Ravigneaux planetary gear mechanism and traditional multi-gear transmission.

Flash boiling spray， as a form of phase-changing spray， plays an important role in the combustion of alternative fuels and zero-carbon fuels. However， most of the current researches focus on the spray morphologies， and there are few studies on the fundamental combustion mechanism. Therefore， the constant volume combustion chamber （CVCC） is used in this paper to analyze the atomization and combustion process of flash boiling spray under lean conditions， focusing on the characteristics of flash boiling spray， mixture ignition and flame kernel development， flame propagation and heat release characteristics. The results show that flash boiling spray can effectively reduce ignition delay and increase flame propagation speed， which is beneficial to energy conversion thus achieving higher heat release rate and larger cumulative heat release. Moreover， the flame front shape of flash boiling combustion is more regular， which suggests more stable flame propagation which is helpful to improve engine thermal efficiency as well as combustion stability under lean burn conditions.

The effect of different emission cycles （WLTC and CLTC） and catalyst states （fresh and aged） on NH₃ emission of a 1.5T SUV is studied. Experimental research shows that the original NH₃ emission of the engine is very low， but after the first ternary catalyst， the NH₃ emission significantly increases. After catalyst aging， the OSC decreases and the internal air fuel ratio fluctuates significantly. NH₃ emission from aged catalysts increases compared to that from fresh catalysts. The catalyst state， emission cycle， and engine air fuel ratio have a significant impact on NH₃ emission， which is of guiding significance for catalyst design and calibration optimization.

In order to better solve the online estimation problem of electric vehicle power battery health state （SOH）， reduce redundant samples in real-vehicle data collection， improve feature loss caused by unstable operating conditions， and enhance the accuracy of SOH estimation of real-vehicle batteries， a SOH estimation method based on incremental capacity analysis （ICA） method to extract features and dynamic time regularization （DTW） to optimize feature samples is proposed. Firstly， incremental capacity analysis is applied to extract the battery IC curve from the real battery charging cycle data， and shape features such as curve peak height are used as health factors. Then， dynamic time regularization is used as the similarity criterion to calculate the similarity of the battery charge cycle samples based on the IC curve shape， and the charge cycle data similar to the baseline charge cycle is retained to optimize the training samples. Finally， the fully connected neural network （MLP） model is used for SOH estimation. Comparative tests are conducted with real vehicle running battery data， and the results show that the method can significantly improve the training sample quality and enhance the battery SOH estimation accuracy.

In the human-machine co-driving stage， the drivers' hazard perception of driving environment at a high level is the core to ensure timely， effective， stable， safe takeover. In this study， the driving behaviors and electroencephalogram （EEG） responses of drivers in typical vehicle-to-powered two-wheeler crash scenarios are obtained by conducting the hazard perception simulation driving tests. From the perspective of driving behaviors， a quantitative model of drivers' hazard perception is built through quantile regression with the braking TTC （Time to Collision） and average acceleration as evaluation indexes. Through independent sample test， it is found that the driving experience and collision scenario types have a significant impact on drivers' hazard perception. From the perspective of EEG responses， it is found that the Alpha band is significantly correlated with hazard perception ability through double independent sample test and FDR correction. Furthermore， the drivers' hazard perception neural mechanism is proposed， including two stages of visual perception and cognitive processing. The research results contribute to improving the safety of human-machine co-driving vehicles.

In pedestrian-vehicle accidents， pedestrians mainly suffer injuries from contact with vehicles and the ground. A large number of previous studies have validated multi-body pedestrian models during the vehicle contact phase， but few have validated them during the ground contact phase. In this paper， four PC-Crash pedestrian models for predicting pedestrian motion and ground contact after vehicle impact are evaluated. Comparison of HIC injuries by vehicle and ground contact with data from six cadaveric experiments in recent studies shows that among them the PC2014 pedestrian model predicts pedestrian head injuries well， with an average error of 6.07% and 5.85% for vehicle and ground contact HIC， respectively， which suggests that the PC2014 pedestrian model can be used to reproduce pedestrian-vehicle crashes and to develop future ground contact injury protection countermeasures. The robustness study of the controlled braking protection method shows that under the disturbance of three pedestrian postures （running， walking and emergency）， the average HIC reduction ratio is 63.2%， 57.9% and 67.8%， respectively， which indicates that the controlled braking protection method has a very good resistance to the disturbance of pedestrian postures. Further analysis of pedestrian injuries in different postures reveals that there is a significant difference between the three sequences of gait. The controlled braking protection method has better resistance to the disturbance of pedestrian postures under the emergency gait； while in the running gait sequence， the control brake protection method often has the lowest head-to-ground collision injuries， which suggests that the effect of different postures needs to be taken into account in the protection of pedestrians by the use of the control brake protection method， in order to further improve the effectiveness of the ground injury protection of the control brake protection method.

With the continuous improvement of motor drive technology and aerodynamic technology， the trend of high-speed pure electric vehicles is becoming more and more obvious. At high speed， the peak value of low-frequency aerodynamic noise caused by chassis cavity may exceed 60 dB（A）， which seriously affects driving comfort. Taking the low-frequency noise problem of a pure electric vehicle under high-speed working conditions as a case， the investigation and analysis of low-frequency noise problems and the verification process of generation mechanism analysis are systematically expounded. Firstly， the type of high-speed driving excitation source is analyzed， and the excitation source separation test is carried out through the wind tunnel to lock the low-frequency noise as the type of aerodynamic noise. Secondly， the potential mechanism of low-frequency aerodynamic noise formation is inferred， and the test is designed to check and analyze the potential mechanism. It is determined that the vortex-acoustic coupling self-excited oscillation of the chassis cavity is the cause of low-frequency aerodynamic noise. Finally， the potential mechanism is verified by simulation analysis， semi-empirical formula calculation and real vehicle test， and the engineering scheme is designed to solve the problem. It has important engineering significance for the analysis， identification and solution of aerodynamic noise problems of pure electric vehicles at high-speed conditions.

For the unstable braking torque output of magnetorheological （MR） brakes， the genetic algorithm （GA） optimized Fuzzy PID control method is used to control the dual-coil MR brake in this article. Based on the Bingham model， a mathematical model for the braking torque of the dual-coil MR brake is established and a dynamic model of the brake is also derived. The torque experiment of the MR brake has been completed. When the coil current is 1.0 A， the maximum braking torque of the MR brake is 4.8 N·m. The least squares structural model is used to identify the transfer function parameters of the dual-coil MR brake. Based on genetic algorithm and Fuzzy PID control， a Fuzzy PID controller optimized by genetic algorithm for the brake is designed. An experimental platform is established to carry out experimental research on the control of the MR brake. The research results indicate that the dual-coil MR brake can achieve better control effect under GA optimized Fuzzy PID control compared to traditional Fuzzy PID control. The rising time of the step response of the braking torque is 0.63 seconds， with an overshoot of 4.17%， and a tracking error of the braking torque smaller than 0.2 N·m. It has a faster response speed， a smaller overshoot， and a smaller torque tracking error.

In order to study the flow field characteristics of the vehicle in environmental wind tunnels， the numerical models of the environmental wind tunnel and the vehicle are established. Considering the blocking effect of the nozzle and contraction section of the environmental wind tunnel， the boundary layer suction and the interference effect of the test facilities on the vehicle flow field， the flow fields in front of the vehicle， around the vehicle body and wheels， in the cooling module and cabin are numerically simulated. The static pressure on the surface of the vehicle， the wind speed around the vehicle body and the bottom of the vehicle are tested. Comparison of the numerical simulation results with the experimental results shows that the numerical wind tunnel can accurately predict the flow field characteristics of the vehicle in the environmental wind tunnel. The results show that the velocity distribution of the front-end airflow of the vehicle changes significantly with the airflow direction， with the uniformity of the air velocity deteriorating as it getting closer to the front cooling module. The airflow at the bottom of the vehicle changes regularly under the joint influence of the ground， the bottom of the vehicle and the rotation of tires， with the air velocity at the bottom of the vehicle increasing first and then decreasing along the longitudinal direction of the vehicle body. This method provides a new idea and reference for studying the thermal aerodynamic performance of vehicles in the environmental wind tunnel and developing the numerical wind tunnel.

Carbon fiber composite materials have advantages such as lightweight and high strength， making them excellent lightweight materials. However， the commonly used carbon fiber composite is based on thermosetting resins such as epoxy resin matrix， facing the challenge of recycling for large-scale application. Thermoplastic carbon fiber composite materials have the advantage of easy recycling， but how to improve the interfacial adhesion of carbon fiber/thermoplastic resins and the efficiency of part forming are the industry challenges. In this paper， taking thermoplastic carbon fiber/nylon 6 composite materials as the research object， for the problem of poor interfacial compatibility of commercialized carbon fiber/nylon 6， a new soluble co-polyamide sizing agent is innovatively designed， which increases the interfacial strength of carbon fiber/nylon 6 by 74.2% and significantly improves the comprehensive performance of carbon fiber/nylon 6. At the same time， the process parameters of prepreg preparation and continuous hot pressing forming are optimized to increase the hot pressing production efficiency of carbon fiber top cover crossbeams to 3.4 min/piece， meeting the requirements of mass production in the automotive industry. Meanwhile， the carbon fiber/nylon 6 top cover crossbeam has extremely high bending strength and modulus， with a weight reduction rate of 68.8% compared to steel parts. In summary， this article provides an innovative solution for mass application of thermoplastic carbon fiber composites （easily recyclable） in automobiles.