A Novel Transformer-Based Model for Motion Forecasting in Connected Automated Vehicles

As the realms of Connected Automated Vehicles (CAVs) and the Internet of Vehicles develop, the ability to accurately forecast vehicle motions takes center stage in shaping the future of intelligent transportation systems, integrating vehicles harmoniously into a connected, data-driven ecosystem. Never-theless, vehicle motion forecasting for CAVs faces considerable challenges, including handling multimodal behavior and effectively considering the complex interactions between surrounding agents. To mitigate these challenges, we design an innovative Transformer-based model for Motion Forecasting (TMF) that takes into account the uncertainty in human driving behavior and the complex interactions between agents. More specifically, we explic-itly integrate map constraints by extracting agent-lane temporal and spatial interrelated features. Our transformer-based encoder benefits from an attention mechanism to enable social interactions, effectively acquiring meaningful representations of these scene elements to attain precise predictions. The evaluation results over the extensive Argoverse Motion Forecasting dataset demonstrate that TMF achieves higher performance when compared to several state-of-the-art models.