A Complementary Electromagnetic Paradigm for Collaborative Autonomous Vehicles

With the development of 5G communication and the continuous upgrading of AI algorithms, the realization of collaborative autonomous vehicles (CAVs) is gradually becoming possible. The perception layers of the current CAVs detect information mainly through simultaneous positioning and mapping (SLAM) and prior maps. However, SLAM requires exhaustive computing resources, and prior maps cannot cope with time-varying electromagnetic environments. Therefore, a complementary electromagnetic paradigm for CAVs is proposed, and a simple demo of this paradigm is created using commercial radio frequency identification (RFID) tags. Specifically, this paper takes vehicle localization as a specific application scenario in the absence of GNSS information, showing the great potential of applying this complementary electromagnetic paradigm to the decision layer of CAVs based on electromagnetic characteristic analysis.