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.

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.

In response to the insufficient heat dissipation and poor surface temperature uniformity of the battery pack in traditional liquid cooling systems, this study proposes a novel battery pack structure based on a hybrid cooling strategy combining air cooling and liquid cooling. A threedimensional model of the designed structure is established using Catia software, and its cooling simulation performance is analyzed using Fluent software. The research results indicate that compared to a single liquid cooling structure which exhibits overheating issues at 2 C and 2.5 C, the hybrid air and liquid cooling structure can effectively control the maximum temperature and the maximum temperature difference within 45 °C and 5 °C, respectively, across different discharge rates. Furthermore, the influence of different fluid inlet velocities on the battery pack cooling performance is investigated. By selecting the optimal combination of wind speed at 5 m/s and coolant flow rate at 0.5 m/s, and then implementing targeted optimization of the flow channel, the maximum temperature of the battery pack is further reduced from 28.12 °C to 27.45 °C under the same operating conditions. This novel structure provides an innovative direction for subsequent approaches in battery thermal management design.

The HMI intelligent system in the vehicle cabin significantly influences the user's intelligent experience. Testing and researching HMI leads to the establishment of a complete set of HMI testing and evaluation methods, which can guide the design and development of HMI, achieving iterative improvement and optimization of HMI systems. The increasing number of features in the car cabin provides users with a richer experience. However, even with identical functionalities, different HMIs can result in vastly different driving experiences for users. This article, based on typical usage scenarios, objectively analyzes the differences in the system response, driver's eye movements, and hand activities across different vehicle models under the same scenarios. The evaluation parameters are further quantified to form assessment indicators. Additionally, a subjective evaluation form is designed based on the five senses and perceptions to evaluate the HMI. The evaluation results indicate that this method is practical and reasonable, and can comprehensively evaluate the advantages and disadvantages of the HMI from both objective and subjective perspectives.

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.

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.

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.

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.

Electrification has emerged as the main trend in new energy vehicles over the past decade. With the increasing number of EVs, reducing their energy consumption aligns with the national strategy for energy conservation and emission reduction, and also improves user's driving experience. The paper first analyzes actual road driving data from an electric SUV. Through dimensional reduction and correlation analysis, the acceleration variance is found as the key factor influencing energy consumption. Furthermore, three typical realroad operating conditions, i.e, constant speed driving, tip in/tip out, and ramp, are chosen for a parametric study by using a 1D model. The tradeoff between energy and time consumption is also discussed. Finally, several principles are established for ecodriving considering the acceleration and speed of vehicles, which include reducing speed during highway cruising, ensuring smooth driving in urban congestion, and utilizing vehicle inertia and minimizing regenerative braking on ramps.

During the actual road driving test process, strictly controlling the driving state of heavyduty trucks can be challenging, which makes it difficult to directly evaluate the energy consumption levels from the test results. Therefore, this paper proposes an energy consumption evaluation method based on the division of driving characteristic intervals. Firstly, by analyzing the distribution characteristics of test points in the China Heavyduty Commercial Vehicle Test Cycle for Truck (CHTCHT), a division scheme for the number and boundaries of speed and acceleration intervals for heavy duty trucks is proposed. Then, considering the impact of road slope, the actual road test data is filtered and used to calculate the average fuel consumption across different driving characteristic intervals. Finally, based on the cumulative driving mileage within each interval of the CHTCHT, the energy consumption evaluation results are obtained. The validation is conducted using three chassis dynamometer drum tests and two actual road driving tests. The results show that the proposed method improves the reproducibility of actual road energy consumption evaluation results for heavyduty trucks.