In order to solve the problems of low efficiency and insufficient system scalability of single-machine test platforms in the vehicle-road-map collaborative simulation environment， a distributed autonomous driving simulation platform architecture for vehicle-road-map cooperative simulation is proposed in this paper， named VIMS （Vehicle-Infrastructure-Map System）. The VIMS platform uses CARLA as the virtual simulation engine. By introducing in real high-definition maps and connecting the hardware-in-the-loop devices such as driving simulators and signal machines to VIMS， the virtual-real traffic scene is formed. Considering the interaction of functions， the VIMS platform is divided into four modules， namely， the main world， the intelligent vehicle， the intelligent roadside， and the high-definition map， adopting ROS distributed architecture to realize the relative independence of the modules and interconnection between the modules. Considering the computational reliability and availability of the platform， distributed computing is used to realize independent computation among the four modules. Through the lane-keeping and vehicle-road-map collaborative positioning algorithm as examples for application validation， data acquisition， transmission and algorithm validation tests and evaluation are realized through the platform. The results show that the platform proposed in this paper can realize the real-time simulation of vehicle， road， and map collaboration to ensure that the modules operate organically and that the system architecture is highly scalable.

In order to reduce fuel consumption and transportation cost of heavy-duty truck， this paper coordinates the human-vehicle-road interaction system， integrates multi-dimensional information of vehicles and intelligent network environment， and proposes an adaptive range-domain predictive cruise control strategy （ARPCC） based on iterative dynamic programming （IDP）. Firstly， by combining the vehicle status and multi-dimensional information of the front environment， an adaptive distance domain model is established based on the longitudinal dynamics of the vehicle to reconstruct the road network， simplify the number of grids， and obtain the global optimal speed sequence by IDP. Secondly， on the basis of the global optimal speed sequence， the segmented optimal speed sequence taken from the adaptive distance domain is obtained to realize the fast solution of vehicle control state. Finally， Matlab/Simulink is used to verify the results， and the results show that the algorithm can effectively improve the computational efficiency and vehicle fuel economy by reducing the grid several times.

The road adhesion coefficient has an important impact on the vehicle dynamics control performance. In order to accurately obtain the road adhesion coefficient in real time and improve the estimation accuracy and convergence speed of the algorithm under different road surfaces and driving conditions， an interactive multiple model adaptive unscented Kalman filter （IMM-AUKF） based on the seven-degree-of-freedom vehicle dynamics model and Dugoff tire model is proposed in this paper for the distributed four-wheel-drive vehicles. The algorithm first introduces the improved Sage-Husa noise estimator into the UKF algorithm to construct the AUKF observer， which updates the measurement noise in real time and ensures the positive characterization of its covariance matrix， improves the weight of the new observation data， and enhances the real-time tracking accuracy and stability of the algorithm. Afterwards， the algorithm selects different observation variables to construct the longitudinal driving condition AUKF observer and the lateral-longitudinal coupling driving condition AUKF observer. And the IMM algorithm is also used to switch the observer model， so as to realize the algorithm's accurate estimation of the road adhesion coefficient under different driving conditions. The results of simulation tests on high/low attachment， joint and u-split roads and real vehicle road tests show that the proposed IMM-AUKF algorithm has higher estimation accuracy and faster convergence speed than the traditional UKF algorithm， and it can adapt to the real-time and accurate estimation of the road adhesion coefficient under different driving conditions.

The intelligent vehicle cyber-physical system （IVCPS） is a complex large-scale system with cross-fusion features across multiple fields. When designing domain-specific modeling languages （DSML） for IVCPS， the application of existing modeling languages has problems such as poor scalability， low maturity， and high learning cost. Therefore， based on the V-model and innovative architecture methods， this article studies the DSML for the IVCPS from two aspects： the top-level design process of IVCPS and the definition of language elements in the IVCPS meta-language knowledge set. The normalization of the IVCPS system-level meta-language and component-based meta-language is defined， and the components that express physical implementation， dynamic characteristics， and cyber computation in cyber-physical components are studied， so as to provide reference for the encapsulation and creation of cyber-physical components.

In shared road space， there are path conflicts between different road users moving in various directions. Road users must negotiate right-of-way through driving interactions to avoid collision risks， thus resolving potential conflicts. The description and modeling of interactive behaviors is crucial for accurately understanding and predicting the dynamic environment. Therefore， a semantic-level representation and extraction method for multi-vehicle interactive behaviors is proposed in this paper， taking interactive trajectory primitives as analysis units. Firstly， a nonparametric Bayesian method is utilized to segment interactive behaviors， obtaining interaction segments with significant behavior patterns. Then， the sticky hierarchical Dirichlet-Hidden Markov Model is employed to extract interaction primitives from these interaction segments. Finally， unsupervised clustering is applied to the normalized interaction primitives to obtain semantic-level behavioral features of interaction scenarios. An empirical study based on 20 797 pairs of multi-vehicle interaction data from the NGSIM highway dataset shows that the method proposed in this paper can extract and analyze complex interactive scenarios involving multiple participants， breaking through the limitation of existing research that only constructs interaction primitives for two vehicle interaction scenarios， and supporting the analysis of interaction among multiple traffic participants. The experimental results show that the proposed method can segment continuous driving behaviors into discrete interaction primitives. The clustering results correspond to actual interaction scenarios and can be used to characterize the interaction behaviors among vehicles in different interactive trajectory primitives. Furthermore， the method can enhance performance of downstream driving tasks in complex scenarios. In multi-step vehicle trajectory prediction， by integrating with baseline prediction methods， the proposed method can reduce the average prediction error and final position error by 19.3% and 14.6%， respectively， compared to baseline methods.

For the data imbalance problem of the current automotive CAN network intrusion detection algorithm due to the lack of attack samples， a CAN intrusion detection data enhancement method based on BEGAN is proposed， which introduces in one-hot coding to image the CAN message features and combines with the constructed Generative Adversarial Network to generate valid samples with the same format as the real attack and with different content. The practicality of the generated enhanced dataset is verified from the perspectives of feature maps， t-SNE visualization， statistical analysis and classifier validation by collecting real vehicle data as real samples for training， which can improve the intrusion detection classifier accuracy. With higher accuracy compared with the traditional oversampling algorithms including Random Oversampling （ROS）， Synthetic Minority Oversampling Technique （SMOTE）， SMOTE combined with Edited Nearest Neighbors （SMOTE-ENN） and Adaptive Synthetic Oversampling （ADASYN）.

For the characteristic of significant load variation in urban logistics autonomous light trucks and to meet the needs of low computational load and high stability， a path-tracking control method based on Linear Parameter-Varying Model Predictive Control （LPV-MPC） is proposed in this paper. Firstly， a linear parameter-varying model is constructed， and nonlinear mapping rules between the model and scheduling variables - speed and load - are established， to improve driving stability and mitigating system sensitivity to parameter fluctuations. Then， for the rolling optimization stage， a trajectory reconstruction method is designed to reconcile disparities between the discrete trajectory points provided by the planning layer and the demand of the control module's prediction layer. A smooth trajectory sequence tailored to the temporal scale of the prediction layer is constructed to effectively decrease the deviation between predicted and actual states. In addition， a multi-point state deviation prediction method is used instead of the traditional single-point prediction， fully leveraging reference trajectory information for improved tracking accuracy. Finally， the effectiveness of the proposed control strategy is verified through combined simulation and empirical vehicle tests.

Accurate prediction of collision risk is crucial for ensuring the driving safety of intelligent vehicles. However， the risk differentiation among heterogeneity vehicle types and its coupled effect in longitudinal and lateral directions has rarely been considered in existing driving risk assessment methods. Therefore， firstly， the behavior patterns of drivers of heterogeneous vehicle types are explored to analyze the influence of vehicle types on drivers' sensitivity to risk in this paper. Secondly， the heterogeneous risk thresholds for different combinations of vehicle types are identified， and the risk differentiation in such traffic surroundings is further quantified based on two-dimensional indicators. Finally， the coupled two-dimensional collision risk prediction model considering vehicle types is proposed， and the effectiveness of the model is validated through comparative analysis. This research helps to enhance the driving safety of intelligent vehicles， which also can provide a theoretical foundation for the development of collision warning systems for human-driven vehicles.

For the problem that existing numerical simulation methods are unable to accurately reflect the probabilistic temperature variations caused by thermal runaway in lithium batteries， s a modeling method for the thermal runaway propagation of lithium battery modules based on probability function triggering is proposed. This method calculates the triggering probability of thermal runaway in each temperature range by statistical analysis of the temperature range and distribution of actual thermal runaway events in lithium batteries. Based on the proposed probabilistic trigger simulator， the simulation process is probabilistically triggered. The effectiveness of the method is verified by comparing simulation results with experimental data， showcasing a high degree of correlation. Then， the thermal spread paths and their probabilities under different triggering conditions of the probability function are analyzed， revealing multiple potential routes for thermal runaway， including the jump thermal runaway event. The sequential thermal runaway path is identified as the most probable， while the jump phenomenon is deemed least likely. The proposal of this method further improves the consistency between numerical simulation and actual process of thermal runaway in lithium batteries， providing an effective research tool and analysis method for studying the probability of thermal runaway propagation in lithium battery modules.

To thoroughly review the current state and identify future trends of the Interior Sound Quality of Electric Vehicles （EVs）， the research progress and distinctive features of interior sound quality of electric vehicles are introduced firstly in this paper. Then， the limitation of the A-weighted sound level in evaluating the interior sound quality of EVs is introduced in detail， alongside objective evaluation approaches that incorporate psychoacoustic parameters and several unconventional parameters. Following this， a summary of subjective evaluation methods for the interior sound quality of EVs is made， including the advantages and disadvantages. Then， the objective quantification models for the interior sound quality of EVs both at home and abroad are classified and summarized. Finally， a summary and outlook are made on the evaluation of the sound quality of electric vehicles. It is believed that in the future the evolution of this field will likely pivot towards adopting high-precision objective models over traditional subjective methods， aiming to diminish evaluation time and cost while improving accuracy.