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.

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.

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 European New Car Assessment Programme (Euro NCAP) is an important reference for consumers choosing vehicles, and a leading indicator for global advances in automotive safety technology. This paper provides an in-depth interpretation of the latest trends in Euro NCAP testing protocols and compares the latest assessment results. Focusing on the segmentation of the safety-protection assessment systems, the paper reviews research progress in safety assessment techniques throughout the entire process, from safe driving and collision avoidance, to crash protection and post-crash safety. It also summarizes the current status of mainstream assessment systems, discusses the performance and characteristics of leading models, and offers practical guidance for improving China's vehicle-safety evaluation system and supporting the overseas expansion strategies of domestic brands.

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 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 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.

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.

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.

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.

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.

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 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.

To resolve the difficulty of identifying representative data and the poor fatigue-damage consistency in low-sampling-rate online data when constructing electric vehicle load spectra on big data platforms, the paper proposes a method with strong user association for compiling the load spectrum of an electric vehicle drive system. First, on the big data platform, user characteristics are described from five dimensions: road type, driving style, load capacity, vehicle speed, and torque. Based on these user profiles, the paper proposes a global-optimal-pairing filter that selects a representative online user dataset, and applies a constraint-based fragment stitching method to join the data segments in order, establishing a multi-feature association between the load spectra and users. To improve damage consistency in low sampling rate online data, high-sampling-rate offline data collected from real vehicles are incorporated to enhance damage equivalence between the load spectra and users. The feature matching results show that the filtered data set deviates from the target user by only about 0.05 for each feature parameter, with no deviation exceeding 0.15. Fatigue-damage calculations confirm that the fusion of low-rate online data with high-rate offline data effectively enhances the damage equivalence between the load spectrum and the users.

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 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.