PEMS is used to conduct the realworld emission tests on six typical ChinaVI diesel vehicles. Based on the workbased MAW (China and the EU), NTE method (U.S. EPA) and 3BMAW method (U.S. CARB), the realworld NO emission characteristics of heavy diesel vehicles are studied, and the characteristics and applicability of different analysis methods are discussed. The results show that the NO, emission results based on the workbased MAW method can meet the regulatory requirements of China and the EU, but the NO emission compliance based on the NTE method and 3BMAW method is uncertain. The low utilization of NO, emission data leads to the inability of NTE method to effectively analyze the realworld NO, emission characteristics. And the 3BMAW method is worthy of reference for NO, emission classification management. Coldstart NO, emission accounts for 47.3%80.7% of the PEMS test, and the coldstart NO, emission of heavyduty diesel vehicles should be paid attention to. However, the current realworld NO, emission analysis methods for heavyduty vehicles in China, the EU and U.S. are unable to effectively evaluate coldstart NO, emission. Therefore, the supervision of coldstart NO emission in the next stage of emission regulations should put forward the specific test cycle, analysis methods and emission limit, to effectively reduce the actual NO, emission of heavy diesel vehicles.

Nondriving behavior identification is one of the important ways to improve the safety of driving. The current recognition method based on skeleton sequence and image fusion has the problems of large model calculation and the difficulty of feature fusion. To address the above problems, the skeletonimage based behavior recognition network (SIBBRNet) is proposed in this paper, which is based on the multiscale skeleton graph and the local visual context. SIBBRNet fully extracts motion and appearance features through a graph convolution network based on multiscale skeleton graphs and a convolutional neural network based on local vision and attention mechanisms, and better balances the relationship between model representation capabilities and model calculation. The feature bidirectional guided learning strategy based on hand motion, an adaptive feature fusion module and an auxiliary loss on the static feature space can guide mutual guidance and updating between motion and appearance features to achieve adaptive fusion. SIBBRNet is finally tested on the Drive & Act dataset, and the average accuracy is 61.78% for dynamic labels and 80.42% for static labels. The Floatingpoint Operations per Second (FLOPS) of SIBBRNet is 25.92G, which is 76.96% lower than that of the optimal method.

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.

In recent years, the thermal runaway problem of lithiumion battery has become the main bottleneck restraining the development of power battery of new energy vehicles. In this paper, a comprehensive review of the research on the thermal runaway problem of the power battery of new energy vehicles is carried out, with the inducment of the thermal runaway of lithiumion battery expounded and the thermal runaway process of lithiumion battery and the characteristics of the thermal runaway of lithiumion battery under different variable conditions introduced. Based on the characteristic parameters of thermal runaway of lithiumion battery, the early warning methods and fire suppression methods applicable to lithiumion battery fire are reviewed, and the shortcomings and development trend of the current research on thermal runaway of power battery of new energy vehicles are summarized, providing certain reference for the development of power battery of new energy vehicles.

The integrated precision casting technology of aluminum alloy is one of the important ways to achieve lightweight of automobiles. Firstly, lightweight aluminum alloy material, investment vacuum suction casting process, and topology optimization are adopted to carry out an integrated design of "material, process and structure" for the front subframe and dashboard crossbeam of the white body in this paper. Secondly, the weight, research and development cost, and development cycle of integrated aluminum alloy structural components are compared to the original steel components, which are significantly reduced. Finally, finite element simulation analysis and bench tests are conducted on the stiffness and modal of the white body with integrated precision cast aluminum parts. The results show that after the integrated design, the front subframe and dashboard crossbeam has reduced weight by 36.6% and 30.8% respectively, while the performance of the white body meets the design requirements.

A DTC decoupling method for complex coupling faults of vehicles is proposed in this paper. Firstly, by analyzing the complex association of DTCs through the principle of vehicle fault selfdiagnosis and the propagation process of fault signals, the strong association relationship between DTCs is mined combined with the association rule technology and the multidimensional association rules of DTC are defined. Secondly, the FPGrowth algorithm for DTC multidimensional association rule mining is improved by the characteristics of the DTCs dataset. Finally, the DTC association knowledge graph is constructed by multidimensional association rules to realize complex DTCs decoupling by combining graph theory. The results show that this method can effectively reduce the number and complexity of DTCs, and improve the efficiency of troubleshooting faults based on DTCs.

Finite Element Analysis (FEA), as an important Computeraided Engineering (CAE) technology, plays a significant role in the area of automotive part development. However, it costs too much time when solving complicated problems, which affects the development cycle. In this paper, a neural network method is proposed, in which sample data is provided by finite element simulation and the mapping relationship between finite element input and output is established by graph network technology. The graph network method is used to predict the stress field of the seat frame assembly. The prediction method simulates the connection relationship between nodes in the finite element model using graph nodes and graph edges, which can effectively express the topological relationship between elements in the finite element model. The prediction results are compared with the results of the finite element simulation. The results show that the method can precisely predict the maximum stress and its corresponding location of the seat frame assembly, with strong predictive capabilities for stress distribution consistency. Additionally, the model has a significant computational advantage, with a calculation speed three orders of magnitude faster than that of the corresponding finite element solver.

For the problems of single mode of car navigation interaction, inability to dynamically change interaction strategies according to contextual differences, and inadequate user experience, a context engine model that supports adaptive car navigation interaction strategies based on different contextual situations is proposed in this paper. Combining the usercentered design (UCD) and context awareness theory, automotive humancomputer interaction contexts are defined, and key contextual elements are extracted through followup surveys of typical drivers. The adaptive interaction strategies and principles of the context engine model in navigation contexts are elaborated, and users' different information needs are mapped to the detailed level of interaction performance at the presentation layer. The navigation HMI information frameworks and components that fit the different levels of driver needs are summarized and designed for interface design and analysis of two typical navigation application scenarios. The results show that the context engine can adaptively interact with driver' needs in various driving situations, providing a reference for optimizing and developing intelligent cockpit driving interaction experiences.

With acceleration of the urbanization process, pedestrianvehicle conflicts have become a significant issue that modern society urgently needs to solve. In complex traffic scenarios, pedestrian crossing behavior leads to frequent traffic accidents. Accurately and timely anticipating pedestrian crossing intentions is crucial for avoiding pedestrianvehicle conflicts, improving driving safety, and ensuring pedestrian safety. An Efficient ActionConditioned Interaction Pedestrian Crossing Intention Anticipation Framework (EAIPF) is proposed in this paper to anticipate pedestrian crossing intention. EAIPF introduces in a pedestrian action encoding module to enhance the representation ability of multimodal action patterns and discover deep skeletal context information. At the same time, the scene object interaction module is introduced to explore interaction information with objects and understand advanced semantic clues in traffic scenes. Finally, the intention anticipation module fuses pedestrian action and object interaction features to achieve robust anticipation of pedestrian crossing intentions. The proposed method is verified on two public datasets, JAAD and PIE, achieving the accuracy of 89% and 90%, respectively, indicating that the proposed method can accurately anticipate pedestrian crossing intentions in complex traffic scenarios.

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.