Vehicle road cooperative warning information interaction method based on multi-sensor fusion

The interaction of vehicle road collaborative warning information is of great research significance for enhancing traffic information sharing and improving traffic safety. The traditional method of vehicle road collaborative warning information exchange has a low success rate and long response time. In this paper, a vehicle road cooperative warning information interaction method based on multi-sensor fusion is provided. This method leverages visual sensors, lidar sensors, millimeter-wave radar sensors, and attitude sensors to gather comprehensive vehicle-infrastructure data. The D-S evidence theory is then employed to fuse and refine this information from multiple sources. Leveraging the fused multi-sensor data and the establishment of a vehicle coordinate system, we derive a dynamics model tailored for vehicle-road cooperative warning information identification. Furthermore, a network for vehicle-road cooperative warning information interaction is established, facilitating information exchange through traffic allocation rules within the network. Experimental results show that provided method achieves a maximum success rate of 97.6% for vehicle-road cooperative warning information interaction, with a mean response time of 61.64ms and a peak satisfaction level of 9.81. These results underscore the ability of our approach to guarantee the quality and efficiency of vehicle-road cooperative warning information interaction.