For unmanned ground vehicles in squad mission support systems (SMSS-UGVs), tracking the entire squad as a group, rather than focusing on individual members, can effectively mitigate issues such as target loss caused by occlusion and environmental interference. However, most existing group target tracking methods are designed for extended targets, which typically assume a rigid and unchanging shape. In contrast, pedestrian groups in SMSS-UGV scenarios exhibit inconsistent motions among members, resulting in continuous changes in the overall group shape. To address this challenge, this paper proposes a group target tracking method specifically tailored for SMSS-UGVs in pedestrian tracking scenarios. We introduce a tracking framework that incorporates a data selection mechanism based solely on positional information, enabling robust handling of dynamic group composition through adaptive shape modeling. Furthermore, a novel group target tracking method based on multi-ellipse shape modeling (ME-CGT-UGV) is presented, which effectively captures complex and evolving group formations. The experimental results show that the proposed method reduces orientation error by 86.13% compared to single-target tracking and by 54.79% compared to shapeless modeling methods. It also maintains strong performance under challenging conditions, including occlusions, environmental disturbances, sharp turns, and formation changes. These findings indicate that the proposed approach significantly enhances the effectiveness and operational reliability of SMSS-UGVs in real-world applications.

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.