While powered wheelchairs reduce physical fatigue as opposed to manual wheelchairs for individuals with mobility impairment, they demand high cognitive workload due to information processing, decision making and motor coordination. Current autonomous systems lack social awareness in navigation and transparency in decision-making, leading to decreased perceived safety and trust from the user and others in context. This work proposes Socially Aware Autonomous Transparent Transportation (SAATT) Navigation framework for wheelchairs as a potential solution. By implementing a Large Language Model (LLM) informed of user intent and capable of predicting other peoples' intent as a decision-maker for its local controller, it is able to detect and navigate social situations, such as passing pedestrians or a pair conversing. Furthermore, the LLM textually communicates its reasoning at each waypoint for transparency. In this experiment, it is compared against a standard global planner, a representative competing social navigation model, and an Ablation study in three simulated environments varied by social levels in eight metrics categorized under Safety, Social Compliance, Efficiency, and Comfort. Overall, SAATT Nav outperforms in most social situations and equivalently or only slightly worse in the remaining metrics, demonstrating the potential of a socially aware and transparent autonomous navigation system to assist wheelchair users.

翻译：虽然电动轮椅能够减少行动障碍者的体力消耗，但因其涉及信息处理、决策制定和运动协调，对认知负荷要求较高。当前自主导航系统缺乏社会感知能力和决策透明性，导致用户及周围他人对感知安全性与信任度降低。本文提出面向轮椅的社会感知自主透明交通（SAATT）导航框架作为潜在解决方案。通过部署具备用户意图感知能力并能预测他人意图的大型语言模型（LLM）作为局部控制器决策主体，该系统可检测并导航社交情境（例如穿越行人或避开交谈中的两人）。此外，LLM在每个路径点以文本形式解释其推理过程以实现决策透明。本实验将该框架与标准全局规划器、代表性竞争性社交导航模型及消融研究在三个社交等级不同的模拟环境中进行对比，采用安全、社会合规性、效率与舒适性四类共八项指标评估。总体而言，SAATT Nav在多数社交场景中表现更优，其余指标上与其持平或略逊，展现了具备社会感知与透明性的自主导航系统辅助轮椅用户的潜力。