Artificial Intelligence-Enhanced Scattering Modeling for Intelligent Transportation Systems: A New Approach

With the rapid development of the 6G technology, intelligent transportation systems (ITS) will be instrumental in achieving efficient traffic management and ensuring safety. The accurate electromagnetic (EM) characterization of scatterers will play a crucial role for improving their integrated sensing and communication (ISAC) capabilities. Toward this goal, this article considers the application of artificial intelligence (AI) techniques for enhancing the scattering modeling for ITS applications. First, a comprehensive review of existing methods for scattering modeling is presented. By identifying their strengths and limitations, potential enhancements brought by AI-based methods are discussed. Second, by considering typical scatterers in ITS—namely pedestrians, vehicles, and vegetation—the EM modeling methods to meet their diverse requirements are identified and categorized. Next the challenges in achieving cross-frequency consistency in EM characteristic representation, accurate modeling of complex multipath effects, and balancing real-time performance versus computational efficiency are identified. Future research trends and directions, which can be used in connection with AI-enhanced methods, are also discussed. Finally, a new approach for incorporating AI-enhanced methods to jointly model typical scatterers encountered in ITS is introduced. It is argued that the use of AI can lead to more accurate and computational efficient scattering modeling for ITS applications.