To address issues such as the current imbalance between supply and demand of charging facilities, the increasing pressure on the grid due to unregulated charging and insufficient collaboration among multiple stakeholders, the paper clarifies the path of multi-value co-creation and the matching rules between supply and demand in the integrated operation of "vehicle-pile-grid-source" within the EV charging service network. It systematically elaborates on the current development status of this integrated operation. Firstly, the matching rules between EV charging supply and demand are analyzed, and the current research status of the value co-creation model is expounded from the perspective of "source-vehicle". Secondly, from the perspective of"vehicle-pile", the impact of the co-construction model of charging piles and charging scheduling strategies on grid stability is analyzed. Meanwhile, technologies related to network structure layout optimization under the interactive response of "pile-grid" are analyzed, and the strategies for efficiency improvement and quality enhancement under the collaborative operation of "grid-vehicle" and the current status of economic optimization and scheduling of the grid are further elaborated. Finally, the challenges faced by the integrated operation of "vehicle-charging pile-grid-source" in the EV charging service network are summarized, and future developments are prospected.

To address the problems of strong subjectivity in subjective evaluation and disconnection between objective data and subjective perception in the drivability assessment of commercial vehicles equiped with Automated Mechanical Transmissions (AMTs), a comprehensive drivability evaluation method based on the AVL-Drive test system is proposed. By designing test schemes for typical driving conditions, the AVL-Drive system is used to accurately collect multi-dimensional objective data such as vehicle speed, acceleration, and engine speed. Combined with a standardized subjective evaluation scale, a quantitative correlation model between subjective perception and objective indicators is established. The test results show that this method can realize the objective characterization of core drivability sub-items including starting smoothness and shift quality, effectively identify the drivability shortcomings of AMT commercial vehicles, provide a clear technical path for rearch and development optimization.

To investigate the influence of different traffic flow conditions on vehicle driving characteristics and urban road testing, a statistical analysis is conducted on dynamic parameters such as vehicle speed, gear position, throttle opening, accelerator pedal speed, brake master cylinder pressure, braking frequency, and steering wheel angle, based on real vehicle CAN bus data collected during peak and off-peak hours. The results show that during peak hours, vehicles operate for extended periods at low speeds and in low gears, with significantly higher frequencies of start-stop transitions, accelerating and braking operations, and more frequent activations of driver assistance systems. In contrast, during off-peak hours, vehicles predominantly operate at medium to high speeds and in medium to high gears, with relatively higher power loads and smoother operational stability. The high-frequency, high-load conditions during peak hours are more suitable for verifying the reliability and durability of the vehicle powertrain, braking system, and driver assistance functions in congested environments, while off-peak hours are more conducive to evaluating the overall performance of vehicles under medium-to high-speed conditions.

Aiming at the main problem of the imbalance between supply and demand of shared cars in terms of time and space, by collecting the usage data of shared car users in Beijing, the high-frequency usage stations and peak hours are analyzed and obtained. Design a questionnaire based on the three dimensions of socio-economic attributes, time of car use and space of car use, and go to the high-frequency stations during the high-frequency hours to carry out field surveys on the users. The results show that most of the car sharing users in Beijing are male office workers who own private cars; the time of using the car is concentrated in the weekdays during 7: 00~9: 00, during 17: 00~19: 00, and in bad weather; the location of using the car is mostly near the transport facilities and business districts, which is a clear feature of commuting; and the 5 qualitative factors, including age and occupation, and six quantitative factors, including frequency of use, affect the satisfaction of the users significantly. Strengthening vehicle scheduling during morning and evening peak hours and inclement weather, setting up more one-way return stations in inner-ring business districts, and lowering the cost of time-share models are the key solutions to the problem.

To systematically review the safety risks of autonomous driving and promote its safe market-oriented application, this paper summarizes and analyzes the characteristics of current autonomous vehicle accidents and relevant research from selected developed countries, identifying the core factors and deep-seated challenges affecting the safety of autoromous driving. The study reveals that the safety landscape encompasses 4 key domains: data security, technological safety, transfer of control between human and mahine, and risk control. Furthermore, future development faces profound challenges including the algorithm "long-tail problem", a crisis of trust in human-machine interaction, data security vulnerabilities, and regulatory lag. The research proposes that coordinated advancement across 4 dimensions—technological integration, regulatory framework construction, human-centered design, and data protection—is essential to establish a collaborative governance framework. This provides a systematic reference for the safe development and application of autonomous driving technologies.

Autonomous Emergency Braking (AEB), as a core safety function of Advanced Driver-Assistance Systems (ADAS), faces 3 major certification challenges: perception limitations, verification complexity, and human-machine collaboration. This paper systematically analyzes the technical architecture of AEB, including multi-sensor fusion for robust environmental perception, conservative decision-making logic to ensure intervention safety, and Operational Design Domain (ODD) definition for user expectation management. In terms of certification frameworks, the study explores the dual-pillar model based on ISO 26262 Functional Safety (FS) and ISO 21448 Safety of the Intended Functionality (SOTIF), highlighting the shift from regulatory testing to scenario-library-driven virtual validation. For human-machine collaboration, it examines mechanisms for establishing calibrated driver trust through transparent interaction design, and prospects the potential of neuro-symbolic AI in enhancing interpretability and localized innovation for China's complex traffic scenarios. This research provides a systematic reference for the industrial safe deployment of AEB systems.

To gain a deeper understanding of driving behavior and its influencing factors, experiments are conducted under various environmental conditions. Data such as driving videos and vehicle status are collected, and the NASA-TLX scale is used to obtain drivers'subjective evaluations. The study analyzes the variations in vehicle speed and driver responses under different driving conditions, as well as the influence of drivers'subjective cognition on the execution of driving behaviors. The results indicate that when vehicles pass through intersections during a yellow light or during overtaking, the speed variation exceeds 50%, while during car-following, the vehicle speed continuously fluctuates within the range of 30~50 km/h. These conditions tend to cause drivers to feel tense and irritable, with subjective ratings reaching up to 500 points. A significant negative correlation was found between vehicle speed and subjective evaluation scores, indicating that both can serve as effective indicators for assessing the state of drivers during the execution of driving behaviors.

In order to improve the testing efficiency and comprehensiveness of the Adaptive Cruise Control (ACC) system, this paper proposes a test scenario generation method based on the Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, typical driving scenarios are designed, and a test scenario model is constructed, which includes scenario initialization, the definition of state and control variables, the agent-environment interaction mechanism, and a reward function. Then, an agent is designed based on a reinforcement learning algorithm, and generate challenging test scenarios. a reward function integrating exposure rate, adversarial factors, and unreasonable collision penalties is developed, guiding the agent to generate scenarios that are not only highly risky but also consistent with real traffic rules. Finally, simulation experiments on left/right cut-in and cut-out scenarios are conducted to validate the effectiveness of the proposed method. The results demonstrate that this approach can automatically generate critical hazardous scenarios for ACC, improve test coverage and specificity, and provide significant value for ensuring system safety testing.

To improve the fusion accuracy of multi-modal data in the field of autonomous driving this paper proposes a spatiotemporal synchronization technology techinal scheme for Light Detection and Ranging (LiDAR) and camera. By adopt a method of estimating and adjusting the offset, it calulates the time of the trigger for the camera exposure, and adjusts the offset time of the trigger for the camera exposure thereby controlling LiDAR and camera to scan and the same target at the same time. The results show that the proposed scheme significantly improves the fusion accuraly of poirt cloud data and images, verifying the feasibility and effectiveness of the technical scheme.

This paper aims to enhance the effectiveness of risk management in the entire vehicle development process. Based on a Sino-foreign cooperative entire vehicle research and development project, the study analyzes the characteristics of the low-voltage harness in vehicles, identifies and categorizes risk factors, and utilizes fault tree analysis to model the causal relationships of various risks. These relationships are then mapped into a Bayesian Network (BN). By employing the fuzzy set theory, collected expert opinions are transformed into prior and conditional probabilities of node events, establishing a risk assessment model for vehicle low-voltage harness development based on fault tree-Bayesian network. This model quantifies the probability and impact of risk factors. For key and sensitive risk factors, corresponding risk response strategies are proposed, providing effective risk management for harness development.