arXiv:2606.17897v1 Announce Type: new Abstract: Long-term human path forecasting in crowds is critical for autonomous moving platforms (like autonomous driving cars and social robots) to avoid collision and make high-quality planning. Although the current research take into account social interactions for prediction, they don't reveal the exact kinds of social interactions happened among people and how the social interactions affect the decision-making process of pedestrians, which further limits its robustness. Social interactions in pedestrian walking are intuitively massive and hard to labe
Ongoing research in AI-driven autonomy necessitates more sophisticated understanding of human crowds to improve safety and efficiency in complex environments.
Improving the ability of AI to understand and predict human social interactions in real-time is crucial for the safe and effective deployment of autonomous systems in public spaces.
The ability of autonomous platforms to navigate human-populated environments will become safer and more efficient, reducing collision risks and improving planning quality.
- · Autonomous vehicle manufacturers
- · Robotics companies
- · AI developers
- · Urban planners
Autonomous systems will achieve higher levels of reliability and public acceptance in crowded settings.
New regulatory frameworks and ethical guidelines will emerge to govern the deployment of socially aware autonomous agents.
The definition of 'public space' and human-machine interaction in urban environments will evolve, potentially leading to new forms of infrastructure.
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