To address the problem of high communication overhead and low security of key distribution in the near-field communication of VANETs，an efficient quantum group key distribution scheme based on quantum random numbers is proposed in this paper. In this scheme，Firstly，the anonymous credentials of the vehicle are jointly generated by quantum random numbers at the vehicle side and the cloud side，and zero-knowledge proof is used to achieve mutual identity recognition between the vehicle and the road side，which protects the privacy of the vehicle. Then，a two-stage group key achieves the update of the group key，i.e. two key parameters at the roadside and the cloud side. The group key reduces the signaling overhead by half and greatly shortens the group key issuance time while ensuring forward and backward security. Finally，the security of the scheme is demonstrated by security analysis and performance analysis.

In the context of real-world driving environments，due to the perturbation of perception data and the unpredictable behavior of other traffic participants，rational decision-making in highly interactive and intricate driving scenarios considering the impact of uncertainty factors is one of the main concerns that decision-making and planning systems for autonomous vehicles must address. A behavioral decision-making method for autonomous vehicles navigating in uncertain environments is proposed in this paper. To mitigate the impact of uncertainty，the behavioral decision-making process is transformed into a partially observable Markov decision process （POMDP）. Furthermore，to tackle the computational complexity of the POMDP model，the complex network theory is applied for the first time for dynamically modeling the microscopic driving environment surrounding the autonomous vehicle，which allows for the effective characterization of interaction relationship between vehicle nodes and the scientific selection of significant vehicle nodes，guiding the autonomous vehicle's decision-making process，enabling precise identification of critical vehicle nodes，and pruning the decision space. The effectiveness of the proposed method is verified in a simulation environment，and the experimental results show that the proposed method has higher computational efficiency，superior performance，and enhanced flexibility in comparison to existing state-of-the-art behavioral decision-making methods.

The weak cold-start capability of fuel cells with graphite plates for vehicles is an important bottleneck that affects the large-scale promotion of fuel cell vehicles in the cold regions of northern China. Starvation self-heating is a common cold-start strategy whose basic principle is to increase overpotential by reducing the supply rate of reactants，and generate a large amount of heat inside the cell in a short period of time to achieve rapid heating. This approach is simple，but it requires a high degree of consistency in the initial water content of the stack monomers and is prone to single-chip reverse polarity and excess hydrogen concentration emission，which can affect the safety and durability of the fuel cell. To solve the above problems，the research group has developed a multi-channel AC impedance measurement device，proposed an optimized purging strategy for single cell impedance consistency，and established a constant voltage and variable air flow control method for cold-start of fuel cells，to achieve multi-objective and multi-parameter coupled coordinated control that provides high heat production，high safety，and high dynamics for voltage，current，and inlet/outlet air flow in the low-temperature start transient process. The bench test results show that the maximum impedance deviation of fuel cells is decreased from 0.7 to less than 0.2 mΩ，and the fuel cell engine system can achieve a fast start at -40 ℃ within 124 s，with good repeatability. The relevant technology is applied in the fuel cell demonstration at the 2022 Winter Olympics，with its effectiveness verified.

In view of the complex and changeable vehicle environment of electric vehicles，which affects the measurement accuracy of current sensors，and the worse situation will lead to the failure of one-phase or multi-phase current sensors in the motor drive system，therefore，a non-current-sensor control algorithm based on extended Kalman filter is proposed in this paper. The stator current of the motor is reconstructed by using the stator voltage，rotor position and speed information of the permanent magnet synchronous motor，and the feed forward compensation is designed to improve the dynamic performance of the system regarding the system delay caused by the non-current-sensor algorithm. The acceleration and deceleration and robustness tests of the proposed algorithm are carried out. The effectiveness of the proposed method is verified by the simulation and experimental results.

In shared road space，human driving interaction behavior has the social characteristics of considering the impact on surrounding vehicles. Lacking the understanding of such social characteristics，autonomous vehicles often struggle to estimate the potential impact of their behavior on surrounding vehicles，thus falling into over conservativeness of decision-making dilemma. A game-theory-based social driving interaction model is constructed by introducing in the behavioral characteristics of drivers considering the impact on surrounding vehicles to capture the action dependencies among road users. With this model，a generalized measurement，utility term of interaction activeness （UTIA），is proposed to quantify the potential impact of the host vehicle's anticipated behavior on its interactants. By introducing the UTIA into the planning objective，the interaction activeness of motion planning algorithm can be directionally adjusted. The results of highway exit experiments show that without compromising safety，enhancing interaction activeness can improve the success rate of the exit task within a given distance by 3.9% and 5.2% for optimization-based and sampling-based motion planning algorithm，respectively.

To address the impact of sparsity and disorder of point clouds on target detection accuracy，a two-stage multimodal fusion network VPC-VoxelNet based on virtual point clouds is proposed in this paper. Firstly，virtual point clouds are constructed using image detection target information to increase the density of point clouds，thus improving the performance of target features. Secondly，the dimensionality of point cloud features is increased to distinguish real and virtual point clouds，and a voxel with confidence encoding is used to enhance the correlation of point clouds. Finally，the scale factor of the virtual point clouds is adopted to design the loss function to increase the supervised training of image detection and improve the training efficiency of the two-stage network，and avoid the cumulative model error problem of the two-stage end-to-end network model. The target detection network，VPC-VoxelNet，is tested on the KITTI dataset，and the detection accuracy is better than that of the classical 3-dimensional point cloud detection network and certain multi-sensor information fusion networks，with a vehicle detection accuracy of 86.9%.

In the process of electrification and intellectualization of the automobile industry，the automotive safety testing and evaluation technology has also been extended and expanded from simple passive safety to active and passive safety integration. In this paper，the differences between the world's mainstream automotive safety assessment procedures are compared and analyzed from three aspects： occupant protection in the vehicle，vulnerable road user protection outside the vehicle，and active safety. The key technical points of vehicle safety development for each evaluation condition are summarized and the development trend of safety evaluation procedures for new energy and intelligent networked vehicles is discussed. The research concludes that the mainstream automotive safety evaluation procedures are becoming more and more stringent in passive safety evaluation，with the proportion of active safety evaluation conditions gradually increasing，and the development focus of the future evaluation procedures will focus on the integration of active and passive safety and virtual evaluation for complex working conditions. In addition，the battery safety test for new energy vehicles has been relatively perfect，and the future research focus can be expanded to the direction of electronic control system testing，chassis stability testing，and unified standardization certification of charging and swapping facilities and supporting equipment. In the medium and long term，the construction of reasonable and reliable evaluation methods such as OTA （over the air） testing of intelligent networked vehicles and HMI （human machine interface） safety and comfort will become a major difficulty concerned by the industry，and a composite evaluation system combining the virtual and reality can be built with the help of tools such as autonomous driving simulators.

Cell to body （CTB） is a key technology to improve the endurance mileage of electric vehicles. The VRB/OW-GFRP hybrid structure formed by the variable-thickness rolled blanks （VRB） structure and the orthotropic woven GFRP （OW-GFRP） through the bonding process is an innovative structure，which can reduce the weight of the CTB battery pack，thus improve the endurance mileage of electric vehicles. Taking an electric vehicle as the research object，a CTB battery body integrated structure is designed to realize the integration of the upper cover of the battery pack and the floor of car body. Furthermore，the cover assembly of the uniform thickness （UT） CTB battery pack are replaced by the VRB structure，UT/OW-GFRP and VRB/OW-GFRP hybrid structure. The lightweight design of the upper cover assembly of the three types of CTB battery pack are carried out based on the multi-stage optimization method. The results show that the weight of VRB structure is reduced by 6.4% compared with that of UT structure when the stiffness performance of the CTB battery pack is met. The lightweight level of the upper cover assembly of the CTB battery pack based on the VRB/OW-GFRP hybrid structure is about three times that of the metal structure，with the weight of the VRB/OW-GFRP reduced further by 4.2% compared with that of the UT/OW-GFRP hybrid battery pack upper cover assembly. Thus，the VRB/OW-GFRP hybrid structure is the inevitable trend of the development of automotive lightweight technology in the future，showing a great application prospect in CTB battery pack cover assembly.

The shared cars have the characteristics of standardized vehicle models, diverse users, and high frequency of usage. To enhance the efficiency of shared car usage and the driving experience, the development of an intelligent seat that can automatically adjust the comfortable sitting posture based on the driver's characteristics has significant importance. For the issue of subjective and arbitrary seat adjustments that lead to discomfort for drivers, in conjunction with the intelligent seat project for shared cars, bench experiments are carried out taking the four variables of Hpoint foreaft position, Hpoint vertical position, seat cushion angle, and backrest angle as the research factors in this paper. The study examines the impact of seat parameters on seat comfort for drivers at the 5th, 50th, and 95th percentiles. Firstly, three experimental levels are chosen for each research factor within the reference vehicle range. Orthogonal experimental design is employed to simplify the number of experiments required. The required number of orthogonal groups is determined, and orthogonal bench experiments are conducted. Experimental participants subjectively evaluate the seat comfort for each group. Additionally, pressure mapping on the seat cushion and angle measurements using a digital angle gauge are employed to collect information of participants' pressure distribution and joint angles. Secondly, clustering analysis is conducted to determine the subjective and objective comfort evaluation system employed in this study, using joint angle as the objective parameter. Through the analysis of the orthogonal experiments, it is concluded that different seat parameters have varying effect on seat comfort. Furthermore, the parameters that affect seat comfort also vary with changes in the driver's percentile. Finally, the data obtained from the orthogonal experiments are adjusted and fitted to establish the prediction models for the optimal Hpoint position and backrest angle of the seat.

In order to comprehensively review the current status and clarify the future trend of fault diagnosis in the electric drive system of pure electric vehicles, this paper first introduces the basic structure, functions and development history of the electric drive system of pure electric vehicles; then summarizes in detail the types and causes of faults of crucial components of the electric drive system of pure electric vehicles, and analyzes the main research status quo of fault diagnosis methods for key components of the electric drive system of pure electric vehicles. Then the domestic and international research progress and development of the diagnosis methods of the pure electric vehicle electric drive system are reviewed in detail from the four aspects of expert knowledgedriven, modeldriven, signaldriven and datadriven, with the advantages and disadvantages of different methods compared. Finally, the problems faced by the fault diagnosis of electric drive system of pure electric vehicles and the development direction are analyzed and foreseen, and it is further discussed and pointed out that the future research on the fault diagnosis of electric drive system of pure electric vehicles can be focused on variable condition coupled fault diagnosis, microfault and prefault diagnosis, realtime online fault diagnosis, intelligent operation and maintenance, unknown fault diagnosis and system selfhealing technology, etc.