Aiming at the current industry issue of inadequate testing and evaluation indexes for the driving experience of intelligent parking assist system users, the paper proposes both subjective and objective evaluation indexes for driving experience. These indexes are experimentally validated and analyzed for correlation. Firstly, based on the functional logic of the intelligent parking assist system, a driving experience closedloop control system is established. Subsequently, combined with the closedloop system, the subjective evaluation index system is constructed using the experience ladder pyramid model. Then, the objective indexes are developed by using the GSM model. Finally, realvehicle tests were conducted on seven car models and analysis was performed using Pearson correlation coefficients. The test results show that the proposed evaluation indexes are suitable for assessing the driving experience, with all subjective and objective correlation coefficients above 0.5, which provides guidance for the design and evaluation of intelligent parking assist systems.

The high casualty rate among passengers in cars rearending trucks has aroused a great deal of attention. However, neither domestic nor international standards and regulations have published AEB tests specific to rearend collisions between passenger cars and trucks. The paper proposes a new typical AEB test scenario for passenger cars rearending trucks, based on real traffic accident data in the future mobile traffic accident scenario study(FASS) database. The Kmeans clustering algorithm is used to identify the test target color that represents actual trucks, and the characteristic parameters of the truck's rear are extracted based on accident data analysis. A new AEB test target is designed and produced, resembling a red, heavy and boxtype truck, with reflective characteristics and machine vision features similar to those of real trucks. Finally, the feasibility and effectiveness of the test target were verified by real vehicle tests, where the truck target remained stationary while the test vehicle speeds were set at 45, 50, 55, 60 km/h, with a 100% overlap. This study provides data support for the development of relevant standards and regulations of vehicle active safety and promotes the improvement of vehicle active safety testing technology.

Aiming at the problem that drivers are prone to traffic accidents under fatigue, this paper proposes a driver fatigue awakening method based on olfactory and auditory stimuli, and studies the awakening effect of peppermint gas and alpha brainwave music as sources of stimulation. The effectiveness of fatigue awakening was evaluated using subjective fatigue questionnaires, along with ECG, PPG and RESP physiological signals as indicators. The results show that the physiological data from ECG, pulse and respiration in both schemes can effectively intervene in driver fatigue. These outcomes agree with the subjective fatigue questionnaire results, confirming the effectiveness of the driver fatigue awakening methods based on olfactory and auditory stimuli. Notably, the awakening method using auditory stimuli exhibits superior effectiveness.

To improve the effectiveness of intrusion detection systems against tampering attacks in the power domain of new energy vehicles, a power domain protection model is established, including both association rule detection and outlier detection. By collecting the power domain messages from the actual vehicles and establishing a rule base using the association rule algorithm, this model aims to detect tampering attacks. On the basis of association rule detection, complex types of tampering attacks are identified through outlier detection. The simulation results show that this method improves the detection accuracy by 5.83% compared to traditional association rule methods, effectively detecting tampering attacks in the power domain of new energy vehicles.

The diagnosis of power battery faults is crucial for the normal operation of electric vehicles. In response, this paper proposes a power battery fault diagnosis method using local mean decomposition and the local outlier factor, aimed at fault recognition and localization within battery packs. Firstly, the voltage signal is preprocessed through local mean decomposition, followed by the reconstruction of the voltage signal according to the correlation coefficient. Furthermore, the kurtosis factor of the reconstructed signal is extracted as the fault feature input to the local outlier factor algorithm, which then identifies the faulty battery based on an adaptive threshold. Finally, the proposed method is validated on a real vehicle, effectively and accurately detecting faults while demonstrating the reliability and robustness of the method.

The skeleton is a multiscale hierarchical structure composed of minerals, collagen proteins, and other constituents. The complex nonuniformity and anisotropy of bones are attributed to their adaptive characteristics under physiological loads. To characterize the biomechanical properties of bones in various fields such as mechanical engineering, biomechanics, medicine, aerospace, and forensics, it is necessary to employ a range of material testing methods to accurately obtain the material constitutive parameters of the skeleton. The article comprehensively introduces the preservation, preparation, testing, and identification methods for material constitutive parameters of bone specimens, and analyzes the characteristics of various mechanical testing methods and their requirements for bone samples. Additionally, the article discusses the application of new testing technologies in characterizing the biomechanical properties of bones. Finally, based on the threepoint bending testing method, this article proposes a process for acquiring material parameters of human bones. The methods and technologies discussed provide theoretical and methodological references for the systematic characterization of bone biomechanical properties.

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 biological fidelity of the advanced Pedestrian Legform Impactor(aPLI) in crash testing largely depends on its geometric structure and the hyperelastic mechanical properties of synthetic rubber used to simulate leg muscle. Based on the quasistatic uniaxial compression test data of rubber, both Ogden and Mooney Rivlin constitutive models are fitted to characterize the hyperelastic behavior of rubber. Following this, the material parameters are obtained and fitting curves are compared with the experimental curves to assess the accuracy of different constitutive models. The results show that the outcomes of the secondorder Ogden model better match the experimental data. To increase the accuracy of muscle rubber material parameters in the finite element model, a compression test finite element model is reconstructed. Taking the material parameters of the fitted secondorder Ogden constitutive model as the initial values, an optimization of the material parameters, μ₁, α₁, μ2 and a₂ in the model is performed using the adaptive response surface method combined with finite element analysis and optimization strategies. This yields a set of optimal material parameters for the material under quasistatic compression.

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.

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.