Automated Valet Parking (AVP) system is a comprehensive platform integrating intelligent driving environment perception, decision planning and motion control technologies. Trajectory planning is directly related to the efficiency, energy consumption, safety and comfort of the valet parking process. To outline the development status of autonomous parking trajectory planning technology, this paper first reviews the development history of parking technology, then investigates trajectory planning during parking, and analyzes the progress in AVP research. Recognizing that the transition from singlevehicle intelligence to multivehicle cooperation reveals greater potential for system optimization, this study subsequently outlines the fundamental methods and current research status of multivehicle cooperative trajectory planning, with a special focus on cooperative planning in parking scenarios. Finally, this paper analyzes existing issues and future development trends in AVP trajectory planning.

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.

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.

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.

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.

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.

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.

This paper takes a light commercial truck as the subject of research, developing a carbon emission calculation model based on the life cycle theory. The model sets its boundaries at the stages of raw material acquisition, production and transportation, parts manufacturing and vehicle assembly in the automobile production process. The paper also explores the differences in the life cycle carbon emissions of the materials involved in the lightweighting measures, and compares the carbon emissions of the vehicle before and after lightweighting. The results show that the life cycle carbon emissions of the substitute materials such as aluminum, magnesium, and carbon fiber reinforced plastic are significantly higher than those of the substituted materials, steel and cast iron. The emissions are quantified as 6.23 kg/kg for forged aluminum, 6.92 kg/kg for cast aluminum, 14.76 kg/kg for magnesium products, 20.2 kg/kg for carbon fiber reinforced plastic, 2.85 kg/kg for ordinary steel, 0.67 kg/kg for stainless steel, and 0.81 kg/kg for cast iron. After lightweighting, the carbon emissions from the powertrain system, driveline system, chassis, and body parts increased by 0.57%, 525.51%, 11.57%, and 33.29%, respectively, leading to a total increase in the vehicle's lifecycle carbon emissions by 36.22%. Both steel and aluminum have lower lifecycle carbon emissions, which results in more significant carbon reduction effects in the vehicle body parts before and after lightweighting.

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%.

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.

This study focuses on a smallsized electric passenger vehicle equipped with a heat pump system, conducting a driving range test under lowtemperature CLTCP cycle conditions. By comprehensively examining the test data and analyzing the vehicle's energy flow, potential avenues for improving the driving range are explored. A comprehensive model of vehicle dynamics and economics, including the thermal management system, is established on the Amesim platform. After calibration, different optimization schemes are simulated and compared to develop a combined optimization scheme. Experimental results show that the combined optimization scheme can improve the lowtemperature driving range by 12.6%. Among them, the contribution of the thermal management system optimization scheme significantly surpasses that of the vehicle resistance optimization scheme and the control strategy optimization scheme. This study provides reference ideas and methods for improving the driving range of pure electric passenger vehicles under lowtemperature environments.

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.