SAATT Nav: a Socially Aware Autonomous Transparent Transportation Navigation Framework for Wheelchairs

2026-03-13

AI Summary

This work addresses the limited social awareness and decision transparency in existing autonomous wheelchair navigation systems, which undermine user and bystander trust and safety. We propose the first framework that integrates large language models (LLMs) into social navigation for wheelchairs, enabling socially compliant local path planning through joint prediction of user and others’ intentions. To enhance system transparency, the framework generates natural language explanations at each waypoint. Experimental results in diverse simulated social scenarios demonstrate that our approach significantly outperforms baseline methods across key metrics—including safety, social compliance, efficiency, and comfort—thereby fostering more trustworthy and natural human-robot interaction.

Technology Category

• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• social awareness
• transparency
• autonomous navigation
• wheelchair
• trust

Innovation

• Socially Aware Navigation
• Large Language Model (LLM)
• Transparent Decision-Making
• Autonomous Wheelchair
• Human Intent Prediction