In order to study the optimal posture angles and body-pressure distribution for passengers in an automotive zero-gravity seat, 30 subjects were recruited for subjective comfort assessments and static body pressure distribution tests. They adjusted the seat to their most comfortable position based on personal preference. Cameras recorded the resulting posture angles of each subject in the zero-gravity posture. Meanwhile, the pressure-sensing equipment captured the interface pressures between the human body and the seat. Non-parametric statistics was used to examine the influence of gender, stature percentile, and body mass index (BMI) on those posture angles and pressure distributions. The results show that gender significantly influences only the hip angle. Variations in stature percentile significantly affect the hip angle, the knee angle, and the mean pressure at the left shoulder. Changes in BMI significantly alter the mean pressure at the left shoulder region of the backrest, the lower back, and the entire backrest.

In this paper, based on the 2024 C-NCAP evaluation regulations, a finite element model of an SUV front end impacting a pedestrian's leg was established using ANSA software. Numerical simulations were carried out using LS-DYNA, and test data were employed to validate the model. Evaluation of the injury values obtained from the simulation and testing shows that the thigh bending moment and knee ligament elongation comply with the high performance limits of the 2024 C-NCAP, whereas the calf bending moment T1 does not. Further analysis shows that the lower front-end grille presses against the knee, occupies the X-direction energy-absorbing space, limits the deformation of the front bumper to absorb energy, and thus increases the calf bending moment. Two targeted structural improvements were made: the solid structure in the center of the energy-absorbing foam was replaced by an open-cell structure with a small groove at the upper end, and the license-plate mounting bracket was lowered by 10 mm along the X-direction. After these modifications, the calf bending moment T1 drops to 265.1 Nm, meeting the 2024 C-NCAP high-performance limit, and the thigh bending moment and the knee ligament elongation continue to satisfy the same criteria.

To address the issues of high pressure drop and poor temperature uniformity in traditional channel-type battery liquid cooling plates, a multi-objective topology optimization method was employed for the design optimization of the liquid cooling plate. An experimental model of battery heat generation was established, and a topology optimization model of the liquid cooling plate was constructed based on the variable density method. The impact of different inlet and outlet arrangements on the performance of the optimized liquid cooling plate was investigated, and the best-performing liquid cooling plate was selected and compared with the traditional straight-channel liquid cooling plate. The results indicate that the topology channels obtained under different inlet and outlet arrangements exhibit significant differences in temperature and pressure drop performance. When the inlet and outlet are arranged along the central symmetry line of the long edge of the liquid cooling plate, the topology-optimized liquid cooling plate demonstrates the best overall performance. Compared to the straight-channel liquid cooling plate, it exhibits stronger flow and heat transfer performance, with the maximum temperature, temperature standard deviation, and pressure drop reduced by 1.38%, 22.35%, and 28.36%, respectively, at an inlet flow rate of 5 g/s. This novel liquid cooling plate can provide new insights for the thermal design of future battery thermal management system.

In order to ensure the safety, comfort and fuel economy of hybrid electric vehicle platoon, a hierarchical control strategy based on intelligent transportation systems is proposed. The upper controller used vehicle-vehicle(V2V) communication technology and used Nonlinear Distributed Model Predictive Control (NDMPC) to optimize the speed control and calculate the optimal speed. The lower controller obtained the upper vehicle speed, used the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm for energy management, and embedded the engine best economic curve and battery characteristic curve as expert experience to improve the convergence and stability of the algorithm. The results show that under this strategy, the maximum relative error of vehicle distance is 4.83%, and the average acceleration of the platoon is 0.331 m/s². Compared with the Deep Q-Network (DQN) algorithm used in the lower layer, the fuel consumption is reduced by 14.25% on average, and the maximum is reduced by 15.30%. Compared with the DP algorithm, the average increase is 8.31%. It not only ensures the safety and comfort, but also effectively improves the economy of the platoon.

To address the lack of objective and quantitative data support in the user-experience evaluation of in-vehicle software usability, the paper proposed an evaluation method that blends subjective and objective data. The subjective psychological responses and objective physiological signals captured during the user-experience evaluation were used as evaluation indices of vehicle software. Twenty target users were recruited for the study. A head-mounted eye tracker, a finger-trajectory tracking system, and a satisfaction questionnaire were used to collect the eye-movement data, finger-movement data and subjective ratings while participants evaluated three kinds of in-vehicle software. Eye-movement and hand-operation data were processed with D-lab and EthoVision XT software, and a multi-dimensional comprehensive evaluation model combining psychological and physiological indices was established. The evaluation model was then validated. The results show that the navigation software provides the best user experience, the air-conditioning software ranks second, and the media-player software performs the worst.

To address the rapid particle convergence and the decrease of particle diversity during map construction, as well as the tendency of the traditional DWA to become trapped in local optima during the path planning, the paper proposes two improvements for intelligent vehicles. The first improvement is an enhanced Gmapping algorithm based on K-Means hierarchical re-sampling. The particle set is clustered into high-, medium- and low- weight groups by using K-Means algorithm, and the weights are adjusted to slow down the decline in particle diversity, thereby improving mapping accuracy. The second improvement is an enhanced DWA path planning algorithm that fuses A* global guidance with turn-stability awareness. The adaptive velocity evaluation function considering the angular velocity magnitude, and a separate angular velocity evaluation function are added. The A* global path turning points serve as the key points to integrate the A* and DWA algorithms. Together, these two efforts improve the global optimization ability of the DWA algorithm. The simulation and real vehicle testing results show that the improved Gmapping algorithm increases the average number of effective particles by 4.6% during grid-map construction. The improved DWA algorithm reduces the number of global path turns by 67% and the search nodes by 37.5% under the set scenario, effectively improving the turning stability of intelligent vehicles.

Virtual simulation testing has become the industry-wide norm for intelligent connected vehicles. Such testing demands a rich set of simulation scenarios. Based on data collected from naturalistic highway-driving scenarios, the paper develops an automated strategy for extracting lane-change cut-in events. A perception risk coefficient is introduced, and one-way ANOVA is used to analyze discrete factors while Pearson correlation analysis is employed for continuous factors, revealing the relationships between scenario elements and risk and identifying the key influencing factors. Typical scenarios are then derived with K-Means clustering and the elbow method, key parameters are retained, and each scenario is constructed on the Prescan simulation platform. The results show that this method can effectively extract critical lane-change cut-in scenarios from real-world data, cluster them into typical scenarios, and reconstruct these scenarios on the simulation platform, providing scientific support for autonomous-driving system testing.

When an autonomous vehicle (AV) is in motion, the driving risks caused by the external environment can lead to occupants' distrust, reducing their acceptance of AVs. Therefore, quantifying occupants' perceived risk is crucial for designing and evaluating AV behavior, as it provides theoretical support for mitigating that risk. The paper quantifies the relationship between objective scenario-level risk factors and subjective perception in overtaking scenarios using a logistic regression model. Firstly, based on 92 overtaking segments of data collected in real-world driving experiments, 7 candidate risk factors are identified. Then, a logistic regression model is established in which the 5 risk factors that passed the hypothesis test are used as the independent variables and the binary classification of occupants' perceived risk serves as the dependent variable. The model analysis indicates that three factors, i.e. risk in adjacent areas (S), time to collision (t_{\mathrm{c}\mathrm{o}\mathrm{l}}) and time headway (t_{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}}), are significantly related to occupants' perceived risk, with t_{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}} being the most influential factor. To classify whether occupants perceive risk, the cut-off value of the prediction model is set at 0.462, which is calculated from the Receiver Operating Characteristic (ROC) curve. By using the HighD dataset, the cut-off value is verified and the accuracy of the prediction model is found to be 89.1%. On this basis, three optimized driving strategies are formulated to mitigate high perceived risk in overtaking scenarios. These three strategies are compared in driving-simulator tests in terms of traffic efficiency and perceived risk, confirming the validity of the model's analysis conclusions.

The rapid development of connected and intelligent vehicles is accelerating the exploration and commercialization of artificial intelligence (AI) technologies. Yet the broader and deeper application of AI in automated driving also brings increasingly prominent safety risks. Thus, developing safety testing and assessment methods for AI-applied automated driving systems is crucial for balancing technological innovation with safety concerns. From a system-safety perspective, this paper proposes a safety assessment method covering three stages: design and development, testing and evaluation, and deployment and operation. The method integrates the life cycle of AI system, safety requirements, verification and validation methods, and continuous risk assessment and safety analysis. Furthermore, the measures for development, design, testing, and optimization to ensure system safety are proposed, providing a reference for future testing and safety assessment of AI-based automated driving systems.

The strong uncertainty of acoustic material parameters leads to the poor stability of its sound absorption and insulation performance. In this paper, a method based on evidence theory is proposed to analyze the stability of acoustic materials. The uncertainty of acoustic material parameters is described by evidence theory, and the focal element interval and the basic confidence assignment of each parameter are determined. Using the interval perturbation method, the upper and lower bounds of sound absorption and insulation performance corresponding to all focal element interval combinations are calculated. The reliability and plausibility of uncertainty problems are calculated according to the identification framework. Reliability and plausibility are taken as optimization objectives, and particle swarm optimization is used to improve the stability of acoustic material's sound absorption and insulation performance. Taking an inner front wall as an example, the stability analysis and optimization of the acoustic insulation performance of the acoustic material were carried out. After optimization, the quality of the part was reduced by 18%, and the stability of the acoustic insulation performance was greatly improved in the whole frequency band, especially in the middle and low frequency band.