To meet the requirements for testing and evaluating ADAS systems in lane change cutin scenarios, the paper proposes a method for generating such scenarios and an objective, comprehensive evaluation model considering the scenario risk coefficients. By collecting natural driving data, the threshold method is used to automatically extract lane change cutin function scenarios and deeply analyze the lane change cutin behavior characteristics. The correlation between scenario risk coefficients and scenario elements is jointly analyzed using oneway ANOVA and Pearson's correlation test to identify key scenario elements. Furthermore, by applying the Kmeans clustering method to the parameters of discrete logic scenarios, five typical test scenarios are obtained. Based on the scenario risk coefficient, the AHP and CRITIC methods are used to construct a multilevel comprehensive evaluation model. The ADAS system is objectively evaluated using the gray correlation theory. Finally, the VTD simulation software is used to create a virtual test scene library for lane change cutin scenarios for simulation testing and validation. The results show that correlation analysis reduces the dimensionality of scenario elements by 60%. The generated test scenarios can effectively validate the comprehensive performance of the ADAS system. Moreover, the comprehensive evaluation model can objectively and effectively evaluate the performance of the ADAS system, providing a valuable reference for the development of intelligent driving systems.

In order to promote the development of autonomous vehicle applications, conducting accurate and reliable safety testing and evaluation is essential. This paper proposes a safety evaluation method for autonomous vehicles tailored to highspeed ramp traffic scenarios using natural driving data. By analyzing the conflict characteristics in the confluence area, the models for calculating traffic conflict indicators such as TTC, PET and MSS are established to determine the safety evaluation indicators. The fuzzy clustering of natural driving indicator data is used to obtain the threshold ranges for these indicators. The autonomous vehicle simulation test has been built. The importance criterion weight distribution method based on interlayer correlation and the gray correlation scoring model are applied. The comprehensive evaluation scores regarding the safety of autonomous vehicles are calculated under different control algorithms. The results show a distinct correlation in the distribution of safety indices between the test vehicle's driving behavior and ideal driving behavior. By calculating the overall correlation degree, the scores can directly reflect the comprehensive safety performance of different autonomous driving systems.

The widespread adoption of electric vehicles has raised higher demands for the technology related to power batteries. Consequently, a thermal management system that keeps the battery within an optimal temperature range has become a core technical requirement for major manufacturers. In recent years, the focus has shifted towards lowtemperature thermal management technology, driven by the performance degradation and life decay of lithiumion batteries in winter's cold conditions. Based on the degradation mechanism of lithiumion batteries in cold conditions, the paper provides a comprehensive overview of the development status of lowtemperature thermal management systems. Additionally, in conjunction with the latest research progress, it summarizes a set of evaluation methods for lowtemperature thermal management of electric vehicles.

As one of the most commonly used modes of transportation, the majority of automobiles are in the L2 to L3 stage of humanmachine codriving technology development. Before the emergence of L5level autonomous driving technology, “humanmachine codriving” remains the dominant driving method, with its various invehicle systems and interaction methods continuously being improved. The integration of multimodal interaction with automotive technologies is bound to ignite new "sparks" as a future design trend. This paper firstly summarizes the research on multimodal interaction design for invehicle systems, covering directions such as fatigue warning, collision warning, lane departure warning, intelligent takeover reminder, and intelligent parking. Subsequently, it analyzes the natural interaction methods of invehicle AI multimodal interaction design, including multiscreen interaction, touch interaction, gesture interaction, voice interaction, facial expression interaction and eye movement interaction. The article uses literature research and case studies to explore how to improve the driver's comfort in the context of safety and emotional aspects, and anticipates the applications and future trends of multimodal interaction design for invehicle systems. Finally, it is concluded that the integration of appropriate and effective interaction methods will improve the safety and driving comfort of various invehicle systems and applications. The introduction of multimodal interaction is destined to become a trend in automotive development.

With the rapid development of the electric vehicle industry, numerous challenges must be addressed in the dose evaluation of electromagnetic radiation inside vehicles. This paper expounded the research progress on this topic. And based on the relevant international and domestic standards for electromagnetic radiation exposure limits, it compared the similarities and differences of the current electromagnetic radiation standards for electric vehicles. Additionally, the paper introduced the simulation method for calculating radiation, and evaluated the human exposure doses in the vehicle through both simulation calculation and measurements. The simulation and evaluation of electromagnetic radiation in electric vehicles, as well as the radiation impact on human health, require further exploration and study.

Based on the indepth investigation data from 135 pedestrian accidents in the FASS database, the paper statistically analyzes the sources of pedestrian head injuries and the impact of vehicle speed on these sources. According to the Spearman correlation coefficient test method, a regression model between the vehicle speed interval and the average MAIS for head injuries is established. The results show that the primary source of pedestrian head injuries is vehicles, which account for approximately 58%, followed by the ground, which accounts for around 40%. In pedestrian accidents, the vehicle speed significantly affects the distribution of sources for pedestrian head injuries. When the vehicle speed is lower than 30 km/h, the ground tends to be the main source of pedestrian head injuries. When the vehicle speed is between 30 km/h and 50 km/h, the risk of the injuries caused by both vehicles and the ground is comparable. When the vehicle speed exceeds 50 km/h, the main source of pedestrian head injuries is vehicles. Therefore, in the study of traffic injury epidemiology and during the development of traffic injury accident databases, attention should be paid to the risk of head injuries arising from the ground, especially in the midtolow speed collisions.

The battery pack, as the power source for new energy vehicles, is one of the most important components. The battery shell not only plays a vital role in protecting the battery package, but also contributes significantly to the vehicle's lightweight design, accounting for 2% to 6% of the total weight of the vehicle. Based on the global automotive industry's development goals for energy conservation and emission reduction, this paper discusses the development status quo of the new energy vehicle battery case industry, focusing on three aspects of safety, lightweighting and reliability. The common key technical challenges in this field are discussed, and the future development trends are proposed.

Current research show that female passengers have a lower capacity for injury compared with males when subjected to the same level of harm. Additionally, safety protection for rearseat passengers is less effective than for those in the front, posing a greater safety risk for smaller individuals in the rear seats during collisions. This study proposes a corresponding injury optimization solution. Firstly, a collision analysis model was constructed based on the CNACP frontal collision conditions. The reliability of the model was verified through a comparison with data from a frontal collision test, which involved a 100% overlap with a rigid barrier using the Hybrid III 5th female dummy. Subsequently, optimization designs were conducted based on benchmark results, comparing and analyzing the impact of different optimization configurations on passenger safety. Finally, an optimization plan was determined, which included adding collision locking tongues, configuring linear pretensioners, increasing seat stiffness, and adjusting seat belt force limit values. In comparison to the original design, the overall score for the rearseat female dummy during the collision process improved by 84%. According to the CNCAP star rating criteria, the score for the rearseat female dummy now exceeds 94%, indicating an excellent performance and validating the effectiveness of the optimization. The methods in this study provide a reference for research on injury optimization for smallsized rearseat dummies.

To solve the complex challenge of response calculation for the coupling system between the infinitelength road and vehicle, the elastic characteristics of foundation and road roughness are considered in the analysis, and a vehicleroad vibration coupling system is established based on an infinite length EulerBernoulli beam model. Then, the moving coordinate system was set up using the vehicle as the reference point. The analytical solution of vibration response of the coupled system was derived by integral transformation. The numerical calculations were carried out by applying the residue theorem, and the semianalytical solutions for the vehicle's vertical displacement, acceleration and road vibration response were obtained. Compared to the traditional modal superposition method used for the coupling response of finite

To address the challenge of multiwaypoint delivery by unmanned vehicles in scenarios such as industrial parks, the paper proposes a lanelevel global path planning, generation and tracking control method based on vectorized highdefinition maps. Considering the influence of delivery point sequencing on the total path length, the A* algorithm is used based on highdefinition maps to calculate the optimal path between each delivery point. And then, the dynamic programming algorithm is employed to obtain the globally optimal path that passes through multiple delivery points. The planned path is smoothed using Bezier curves, and the reference driving speed is set according to the road curvature at different points along the path, thereby creating a lanelevel target trajectory suitable for tracking. Subsequently, a model predictive controller based on a twodegreeoffreedom vehicle model is designed for trajectory tracking to achieve autonomous control of lowspeed logistics delivery vehicles. The proposed planning and control method is tested on a joint simulation platform of CarSim, PreScan and Simulink, as well as on a real vehicle platform. The results show, compared with the traditional path determined based on the nearest delivery point strategy, that the path length determined by the proposed method is reduced by an average of 6.15%. The developed trajectory tracking controller ensures that the lateral deviation of the experimental delivery vehicle from the target trajectory is maintained within 0.25 m and the yaw angle deviation is kept within 5°.