With the growing demand for the integrated development of vehicle intelligence and connectivity, China has taken the lead in proposing the Vehicle-Road-Cloud integration development path, aiming to accelerate the deep integration and exploration of vehicle intelligence and connectivity. Grounded in systems engineering methodology, the paper adopts the Vehicle-Road-Cloud Integrated System (VRCIS) as its research framework. First, it explores the benefit of VRCIS development and evaluates the feasibility of application scenarios to address current technical problems. Secondly, it classifies the application scenarios, based on the connected capabilities of the VRCIS and summarizes the table of connected scenarios, by reviewing international research and practices. Thirdly, the paper analyzes the three developmental stages of cooperative connected autonomous driving and generates corresponding functional scenarios based on the summarized connected scenarios. Finally, the development path for the Vehicle-Road-Cloud integrated scenarios is proposed, guiding future technological evolution and implementation.

The technology of Simultaneous Localization And Mapping（SLAM）is the hot and difficult point of current research. It is one of the key technologies to realize unmanned driving. The paper introduction is centered on the principle, basic structure, sensors used and map types of SLAM and analyzes the advantages, disadvantages and scope of application of various fusion methods. Secondly, according to the different fusion methods used, it introduces the current status of research at home and abroad, and points out its worthwhile affirmation and continuation of the technology as well as shortcomings. Finally, it summarizes the current problems of multi-sensor fusion unmanned SLAM research, analyzes the research difficulties and puts forward suggestions on the possible development direction of future research, with the aim of providing references for the development of unmanned driving.

The application of digital projection technology promotes the intelligent development of automobile headlights. In order to explore the new generation of car headlight technology, this article outlines the development history of automotive headlights. By studying the current status of digital projection headlights, three digital projection headlight technology solutions, DLP, Mirco LED, and LCD display, are compared and analyzed. The development trend of array projection headlights is also discussed, in order to provide reference for related research.

To address signal crosstalk and reflection phenomena caused by the wiring layout of automotive Printed Circuit Boards (PCBs), a simulation software Cadence is utilized to model the reflection phenomena of high-speed signal lines. The causes of signal reflection are analyzed, and impedance matching schemes for reflection are employed in the termination design. The effects of 4 different types of terminations, namely series termination, Thevenin termination, RC termination, and diode termination, on the signal quality in transmission lines are compared. Additionally, enhancements are made to power integrity through optimized power plane design, which significantly enhances the Electromagnetic Compatibility (EMC) performance of the PCB. The study demonstrates that signal integrity, power integrity, and electromagnetic interference are interrelated and mutually constraining. Conducting reliability designs for these 3 aspects by combining stimulation data can significantly improve the EMC performance of PCBs.

At present, there is still a big gap between the performance of domestic air spring rubber capsules and foreign countries’, and the core technology is mainly in the hands of foreign companies. In order to study the core technical difficulties of air spring rubber airbags, the paper analyzes the application status and future development trend of air springs in the field of passenger vehicles. The rubber airbags are discussed from two aspects, materials and molding process. This paper finally summarizes the core technical problems of cystic skin, and the potential technological breakthrough paths to provide reference for the development of rubber skin technology in China.

Driving style recognition plays a crucial role in enhancing personalized driving experiences and optimizing energy utilization in smart connected vehicles. Considering the relationship between different road environments and driving styles, a cascade delivery framework is designed to fully utilize real-world natural driving data and segment the data into events with distinct physical meanings. Using driver IDs as pseudo-labels, an XGBoost model learns differences in driving styles, identifying the key features and weights critical for recognition. Following the principles of a hybrid expert system, the WK-means algorithm clusters driving styles under varying conditions, ultimately generating driving scores to evaluate driver performance. The statistical analysis of the clustered data shows that this method effectively recognizes drivers with diverse driving styles, which lays the foundation for the further development of intelligent networked vehicle technology.

With the rapid development of intelligent vehicles and new energy vehicles, there are higher requirements for the dynamic responsiveness, multi-working condition adaptability and safety of the suspension system. In order to break through the limitations of traditional suspension and semi-active suspension in body posture adjustment and complex road condition adaptation, this paper outlines the working principle of the full-active suspension and makes an in-depth analysis of the characteristics, laws and advantages of the full-active suspension. Combined with the domestic and foreign research status, the advantages and limitations of various control strategies are summarized. On this basis, the development direction of the vehicle full-active suspension technology and its control strategies is proposed, in order to provide a reference for the in-depth development and wide application of the full-active suspension technology.

Firstly, this paper reviews the definitions of the key states of lithium-ion batteries, including State of Charge (SOC), State of Power (SOP), State of Function (SOF), State of Energy (SOE), State of Health (SOH), Remaining Useful Life (RUL), State of Temperature (SOT) and State of Safety (SOS), and analyzes their coupling relationships. Then, it classifies and elaborates the methods for joint estimation of battery double states. In the future, multi-state joint estimation can further improve the estimation accuracy. Advanced sensor technologies, such as fiber-optic sensors, can more accurately measure the internal state quantities of batteries. At present, battery group state estimation is mostly focused on individual cells, and it is necessary to further explore the joint estimation at the battery module and group levels. Given the nonlinear characteristics of lithium-ion batteries, machine learning can achieve higher estimation accuracy with relatively low complexity. With the development of big data and cloud technologies, new-type battery state estimation will become a trend.

To systematically summarize the research status of multi-source fusion environmental perception technology for intelligent vehicles, this paper compares and analyzes the principles and characteristics of various sensors including cameras, Light Detection and Ranging (LiDAR), and millimeter-wave radar. The environmental perception technologies based on single-sensor approaches (such as camera-based object detection and LiDAR point cloud processing) and multi-sensor fusion strategies (data-level, feature-level, and decision-level) are reviewed with their technical bottlenecks and challenges. Typical algorithm cases are also discussed to explore their application effectiveness. The research findings indicate that: single sensors exhibit inherent limitations, such as cameras’ dependency on illumination conditions and LiDAR’s high cost with insufficient semantic information acquisition capability, as well as multi-sensor fusion technology significantly enhances environmental perception robustness through complementary advantages, yet challenges like data heterogeneity and insufficient real-time performance still remain unresolved. To meet the perception demands of complex scenarios, future development will focus on intelligent multi-modal fusion algorithms, cost-effective sensor integration, and V2X collaborative perception technologies.

Range-Extend Electric Vehicle（REEV) has stronger adaptability to user scenarios. To study the current market status of REEV, this paper discusses the advantages and disadvantages of REEV from the users’ perspective, specifically focusing on the analysis of the advantages of REEV in terms of driving range (especially low-temperature driving range) and user experience. Consequently, the user portrait of REEV is sorted out. The results show that REEV has great attraction to users with mileage anxiety in cold climates who prefers the pure electric driving experience, and have development space in the next decade.