Predictive V2X Communication and Data Sharing Using Future Path Information

As autonomous (automated) driving technology becomes increasingly prevalent, vehicles utilize onboard sensors to recognize their surroundings, including roads, pedestrians, and other vehicles. However, these sensors have inherent limitations, particularly in detecting objects in blind spots or acquiring precise motion information for distant entities. To ensure both safety and traffic efficiency, cooperative automated driving—which utilizes low-latency, high-reliability mobile communications to share data from other vehicles and roadside units—has become essential. Traditionally, vehicle movement has been treated as a source of uncertainty in network and data processing. In contrast, automated vehicles can provide highly accurate position and speed data, moving along deterministic routes based on pre-calculated future path information. This paper introduces our research on enhancing the performance of predictive V2X communication and data sharing by proactively leveraging this future path information to improve both latency and reliability. We present various methodologies from our prior studies and discuss related initiatives, demonstrating that a proactive communication paradigm based on driving plans can significantly surpass conventional reactive approaches in cooperative automated driving environments.