arXiv:2511.17581v3 Announce Type: replace Abstract: Modeling the cognitive and experiential factors of human navigation is central to deepening our understanding of human-environment interaction and to enabling safe social navigation and effective assistive wayfinding. Most existing methods focus on forecasting motions in fully observed scenes and often neglect human factors that capture how people feel and respond to space. To address this gap, we propose EgoCogNav, a multimodal egocentric navigation framework that jointly forecasts perceived path uncertainty, trajectories and head motion fro
The proliferation of egocentric vision and advanced AI models makes it possible to integrate cognitive factors into navigation systems, moving beyond purely environmental data.
This research is crucial for developing safer and more effective human-AI interaction in navigation, especially for applications like assistive robotics and autonomous vehicles that operate in complex human environments.
Traditional navigation models focused solely on physical motion will be augmented or replaced by systems capable of understanding and anticipating human cognitive and emotional responses to space.
- · AI researchers (cognitive modeling)
- · Robotics companies (assistive and social robots)
- · Autonomous vehicle developers
- · Elderly and visually impaired individuals
- · Developers of purely motion-forecasting navigation systems
- · Companies relying on static environmental mapping only
More human-like and adaptable AI navigation systems emerge, improving safety and interaction in shared spaces.
Human cognitive modeling becomes a more central component of general AI development, influencing areas beyond navigation.
The definition of 'autonomous' shifts to include context-aware cognitive empathy, leading to more socially integrated AI agents and robots.
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Read at arXiv cs.LG