With the development of intelligent transportation, vehicle motion state recognition has become a crucial method for enhancing the reliability of vehicle navigation and ensuring driving safety. Currently, machine learning is the main approach for recognizing vehicle motion states. The symmetry characteristics of sensor data have also been studied to better recognize motion states. However, the existing approaches face challenges during motion state changes due to indeterminate state boundaries, resulting in reduced recognition accuracy. To address this problem, this paper proposes a vehicle motion state recognition method based on the Hidden Markov Model (HMM) and Support Vector Machine (SVM). Firstly, Kalman filtering is applied to denoise the data of inertial sensors. Then, HMM is employed to capture the subtle state transition, enabling the recognition of complex dynamic state changes. Finally, SVM is utilized to classify motion states. The sensor data were collected in various vehicle motion states, including stationary, straight-line driving, lane changing, turning, and then the proposed method is compared with SVM, KNN (K-Nearest Neighbor), DT (Decision Tree), RF (Random Forest), and NB (Naive Bayes). The results of the experiment show that the proposed method improves the recognition accuracy of motion state transitions in the case of boundary ambiguity and is superior to the existing methods.

This study explores the transmission characteristics between the links of UV(ultraviolet)-network communication under mobile conditions. Utilizing the prevalent UV-network communication network topology as a foundation, a UV-network communication model tailored to mobile scenarios was developed. This model includes a method for calculating the impulse response of the system, focusing specifically on three common network topology structures: two parallel links, co-address of the originating link, and co-address of the receiving link. The simulation and analysis conducted in this study examine the impact of various factors on the system’s impulse response, such as receiver movement speed, geometric parameters of the receivers, link spacing, and the angle between links. The results indicate that receiver movement speed significantly influences pulse response fading, with faster speeds resulting in more severe fading. Additionally, in parallel links, smaller link spacing results in stronger impulse response. Furthermore, a smaller angle between the originating and receiving co-addresses results in increased inter-link interference. The study findings in this paper will lay the foundation for the study of UV mobile self-organizing networks.