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.

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.

To solve the problem of invalid data during the dew point protection phase of NO_x sensors in the remote monitoring of heavyduty vehicles, the paper used the PEMS tests on a China VI heavyduty vehicle to investigate the high NO_x, emissions during this protection period. Furthermore, the feasibility of using a neural network algorithm to repair the data and improve the utilization rate of remote monitoring data was verified. The results show that the dew point protection leads to more than 30% NO_x, emissions not being recorded. During this protection phase, over 90% of the data revealed that the vehicle speed was below 54 km/h, the engine coolant temperature was below 82 °C, the SCR inlet temperature was below 245 °C, and the SCR outlet temperature was below 225 °C. The neural network algorithm effectively repaired the invalid NO_x, measurements during dew point protection, with errors of less than 4%.

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 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.

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.

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.

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 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.