EES: A Data-Driven End-to-End Escorting System via Spatiotemporal Feature Fusion

This letter presents a technique that allows unmanned vehicles to escort a human to their destinations. Current human-centered following methods depend solely on human movement, which presents significant limitations. The complexity of human movement during tactical maneuvers can lead to erratic vehicle motion. Additionally, the static relative positioning between the human and vehicle creates a rigid following pattern, thereby constraining the vehicle's ability to dynamically adjust its position for optimal coverage. To address these limitations, we propose a data-driven end-to-end escorting system (EES) that takes into account both environmental information and human movement to achieve adaptive escorting.We propose a soft-coding paradigm to replace the traditional hard-coding intent modeling to address the inconsistency of human intention and vehicle motion, and establish human-scene following through a crossmodal attention gating network.We conducted experiments in the CARLA simulation and the real world. The results demonstrate that the proposed EES reduces prediction errors by 41.2% during overall processes and by 54.5% during cornering. Additionally, EES can adapt to various positions and dynamically adjust the relative positions between humans and unmanned systems to adapt to complex scenarios.