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.

In order to enhance the comfort and user acceptance of intelligent connected vehicles, a cognitive process model for passenger carsickness mitigation is established in this paper and an olfactory stimulationbased mitigation strategy is proposed. Firstly, a screening experiment of olfactory stimulation materials for carsickness relief is designed and carried out, fully considering occupant satisfaction as well as the functional requirements of carsickness relief, to select the odor type and concentration of olfactory stimulation materials. Then, a carsickness mitigation experiment based on a real car is carried out to investigate the mapping relationship between different degrees of carsickness and physiological representations and further validate the efficiency of the olfactory mitigation method. The results show that the subjective carsickness degree is positively correlated with GSR activation and negatively correlated with EEG asymmetry. Moreover, releasing 20% ginger blossom odor for 10 seconds can effectively relieve carsickness symptoms, with an average relief effect of 22.17%, which is verified in terms of subjective and objective dimensions.

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.

For the problem of high temperature and uneven distribution affecting the power and safety of the electric vehicle during the battery management systems slave control board's service, a commercial BMS slave control board thermal analysis model is built and verified using the CFD theory and Icepak software. For the first time under vehicle service conditions, temperature field analysis and thermal uniformity optimization research are carried out based on the thermal analysis model. The BMS slave control board thermal simulation analysis shows that the balancing and power supply modules exceed the BMS's design temperature limit of 60 °C due to local heat accumulation, with the maximum temperature difference of the entire BMS slave control board being 21.0 °C. A heat dissipation path analysis of the BMS slave control board is further carried out, and heat dissipation optimization design is realized by altering the distance, layout of the balancing resistor, PCB substrate and adding thermal pads. By increasing the heat dissipation capacity of the BMS slave control board, the highest temperature of the BMS slave control board can be controlled below the design limit of 60 °C, and the temperature difference of the entire circuit board can be reduced to 6.9 °C, which enhances the safety and reliability of the BMS slave control board under actual vehicle service conditions, providing theoretical methods for the thermal design and optimization of the BMS slave control board.

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.

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.

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.

In order to realize effective vibration mitigation of selfpowered semiactive suspension under uncertain factors, the suspension mechanicalelectrical coupling dynamic model is established. The influence of electrical parameters on energy conversion efficiency is explored. The adaptive fault tolerant control gain is deduced, and then the vibration isolation capability of the suspension is investigated in time and frequency domain respectively. The robust index of adaptive optimal fault tolerant control algorithm is obtained by constructing Lyapunov equation to stud the influence of key parameters on robust index. The results show that the electrical parameters have obvious influence on the energy conversion efficiency, with the suspension having higher energy conversion efficiency at the second natural frequency. The proposed adaptive optimal fault tolerant control strategy can realize effective vibration suppression in both the time and frequency domain, with better vibration isolation performance compared to passive control and selfpowered mode. The control robust index is affected by the inductance of generator and outer diameter of permanent magnet most significantly.

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.

In order to eliminate the influence of parameter uncertainty and external uncertain disturbances on the handling stability of fourwheel steering vehicles, a nonlinear integral sliding mode control method based on kalman filter extended state observer for fourwheel active steering is proposed. Firstly, the kalman filter extended state observer (KFESO) is designed to realize the vehicle state observation and external disturbance estimation, which overcomes the disadvantage of traditional kalman filter algorithm's dependence on highprecision models. Secondly, to reduce the tracking control error of the target ideal state of the vehicle caused by disturbances, the disturbance observed by KFESO is compensated to the control input. In order to realize global robust control and suppress integral saturation, a nonlinear integral sliding mode control method based on exponential convergence is designed. Finally, hardware in the loop test results indicate that the KFESO has high observation accuracy in the presence of internal and external uncertain disturbances in the system, and the KFESOISMC method has excellent antiinterference performance in controlling the stability of fourwheel active steering compared to LQR and ISMC methods.