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.

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.

Aerodynamic design is one of the key elements in Formula SAE (FSAE) car design, which not only has a decisive impact on the car's handling performance but also affects the cooling performance of the radiator. In this paper, the method of Computation Fluid Dynamics (CFD) is adopted to carry out the analysis and optimization of aerodynamic performance and heat dissipation performance of FSAE racing car considering the characteristics of the rear compartment structure. By analyzing the trend of changes in heat dissipation performance and aerodynamic performance, four representative schemes were selected for comparison. The results indicate a strong positive correlation between the optimized local aerodynamic performance and heat dissipation performance. After optimization, the average volume temperature of the radiator decreased by 29.82%, and the negative lift of the wing increased by 26.92%. This study provides new ideas for the CFD simulation of the full-car model of FSAE race cars, and also provides guidance for the layout design of the cooling device for open-wheel race cars with the radiator placed at the rear.

To address the difficulty of balancing safety and ride comfort, this paper proposes a new safety model considering both factors, and introduces a corresponding early-warning braking strategy. First, the second-order time-to-collision (TTC) model is constructed by incorporating the accelerations of both vehicles into the classical TTC model. Then the safety distance model based on braking process analysis is modified according to road conditions. Combining the safety and comfort requirements, the paper employs the second-order TTC model during the warning phase, and the modified safety distance model during the braking phase to judge dangerous states. The hierarchical AEB control system is designed, and co-simulation and real-vehicle test platforms are built for verification. The results show that, under various test scenarios, the proposed car-following control strategy for AEB systems successfully avoids collisions, initiates braking without disrupting normal driving, provides warning times that give the driver ample reaction time, keeps the minimum inter-vehicle distance after braking within a reasonable range, and achieves effective hazard avoidance even on wet roads.

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.

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.

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.

To address the inefficiency and weak robustness of existing rollback methods when a remote update of automotive ECU fails, this paper designs an efficient and stable upgrade rollback strategy. Firstly, an optimized rollback backup method and a comprehensive security-verification strategy are proposed to enhance rollback stability. Then, to avoid the long duration of traditional full rollback, a differential rollback technique based on an improved Bsdiff algorithm is proposed. The improved algorithm can significantly increase rollback efficiency by using the higher-ratio LZMA2 (Lempel-Ziv-Markov chain-Algorithm 2) compression and optimizing the patch format. Finally, the proposed strategy is tested on real-vehicles. The results show that the rollback strategy using the improved differential algorithm reduces rollback time by 84.69%, and achieves a 100% test-case pass rate. The proposed strategy preserves vehicle functionality when an ECU upgrade fails while improving rollback efficiency.

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.

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.