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.

In the context of carbon peaking and carbon neutrality, integrated electric drive axles have emerged as a key pathway for commercial-vehicle electrification. Firstly, the paper introduces the typical configurations and layouts of integrated e-axles for commercial vehicles. Given the complexity and diversity of vehicle segments, it analyzes the suitability of electric drive axles for passenger cars, light trucks and pickups, as well as medium-and heavy-duty trucks. Next, the paper focuses on the motor, inverter, and transmission, which are the three core components, and summarizes recent advances in the key technologies supporting commercial-vehicle e-axles. Finally, the paper discusses the challenges these technologies still pose and describes their future prospects, providing a reference for the development and broader adoption of integrated electric drive axle systems in commercial vehicles.

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.

Existing driver-assistance systems often deliver late or inaccurate alerts when a vehicle cuts in suddenly from an adjacent lane. To address this issue, the paper develops a collision-warning model that detects the lane-change intention of the lead vehicle. The vehicle-vehicle communication is utilized to send that intention to the following vehicle, which then predicts the cut-in path and performs collision detection. The collision time TTC-S is proposed, the avoidance time TTA is re-examined, and a tiered warning strategy is designed. In order to verify the effectiveness of the collision warning system, a joint simulation platform is built based on Simulink and PreScan. The results show that the collision-warning model achieves an average true-positive rate of 90.32%, outperforming the Mazda model by 8.44% and the traditional TTC model by 11.66%. The system also provides earlier alerts, extending the average warning lead time by 1.42 s and 1.9 s, respectively, which provides a larger safety margin.

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.

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.

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.

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 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 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.