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.

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.

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.

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.

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.

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.

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.

At present, the vehicle side impact safety evaluations rarely consider the effect of pre-crash braking on occupant posture. In order to study the occupant displacement induced by braking during the pre-crash phase and its impact on occupant kinematics and injury outcomes under side impact conditions, the paper combines volunteer experiments with CAE simulations. Three side impact models, including a standard posture model, a muscle-tensed model, and a muscle-relaxed model, were developed to compare differences in occupant kinematics and key injury indicators across the models. The results show that, during braking, the volunteers experienced greater displacement under the relaxed muscle state, with the maximum displacements of the head and first thoracic vertebra (T1) reaching 225 mm and 145 mm, respectively. This displacement significantly changed the contact between the upper body of the dummy and the side restraint system during the side impact, creating a risk that the dummy's chest could move outside the effective protection zone of side airbag. The peak Y-acceleration of the head was increased by 163.98 m/s²(79.4%), and the maximum abdominal compression was increased by 13.53 mm (64.0%) during the side impact. These results provide valuable insights for the development of advanced restraint systems and integrated safety testing methods.

Biodiesel is a good substitute for petroleum diesel, because of its good environmental performance, good engine starting performance, good fuel performance, wide source of raw materials, renewable and so on, it has a wide application prospect. However, the fat composition and content of different raw materials are different, and the performance and emission characteristics of the prepared biodiesel also show different trends during combustion. Based on the types of raw materials for biodiesel production, this paper classifies biodiesel and summarizes the characteristics of different kinds of biodiesel. Firstly, the physical and chemical properties and the preparation process of biodiesel was introduced in detail. Secondly, this paper is different from the classification of biodiesel based on the technological iteration of biodiesel production, but classifies biodiesel according to the source of raw materials, which provides a basis for the summary of the table and the horizontal comparison of biodiesel produced from various raw materials. Then, the use characteristics and changing trends of biodiesel prepared from different kinds of raw materials are summarized and sorted out in the table, and the summarized experimental results are visually presented, the characteristics of biodiesel produced from different raw materials were evaluated, in all the studies on biodiesel, it has been shown that in terms of pollution emissions, the engine found that the emission of CO and carbon oxides was reduced when using most biodiesel, and most studies showed that the emission of hydrocarbons was also reduced, but the emission of NO_x was increased, which is almost a common feature of all different types of biodiesel. Finally, according to the limitations and shortcomings of different types of biodiesel in the use process, the future direction of biodiesel use characteristics optimization was proposed.

Amid the accelerating electrification of the global automotive industry, power batteries, as the core components of new energy vehicles (NEVs), have become a strategic focal point in the global competition to achieve green and low-carbon automotive development. Currently, China leads the world in power battery technology, industrial scale, and supporting ecosystem. This paper investigates the carbon footprint accounting methods for power batteries, comprehensively studies the current development status and challenges of China's power battery carbon footprint, and deeply analyzes the experiences and insights gained from the EU's carbon footprint management. It is proposed to accelerate the improvement of the top-level design for carbon footprint management, establish a comprehensive carbon footprint accounting framework, facilitate international mutual recognition of accounting systems, explore the market-oriented operation of power battery carbon footprints, and promote mutual recognition of international standards and regulations.