Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a
very challenging task. This is because human movement and interactions are very hard to …

Safe, socially compliant, and efficient navigation of low-speed autonomous vehicles (AVs) in
pedestrian-rich environments necessitates considering pedestrians' future positions and …

Autonomous vehicle navigation in shared pedestrian environments requires the ability to
predict future crowd motion both accurately and with minimal delay. Understanding the …

It is challenging for a mobile robot to navigate through human crowds. Existing approaches
usually assume that pedestrians follow a predefined collision avoidance strategy, like social …

We present a relational graph learning approach for robotic crowd navigation using model-
based deep reinforcement learning that plans actions by looking into the future. Our …

Mobile robots capable of navigating seamlessly and safely in pedestrian rich environments
promise to bring robotic assistance closer to our daily lives. In this paper we draw on insights …

Navigating mobile robots along time-efficient and collision-free paths in crowds is still an
open and challenging problem. The key is to build a profound understanding of the crowd …

This work presents a deep reinforcement learning framework for interactive navigation in a
crowded place. Our proposed Learning to Balance (L2B) framework enables mobile robot …

Safe and efficient navigation through human crowds is an essential capability for mobile
robots. Previous work on robot crowd navigation assumes that the dynamics of all agents …

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they
must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to …