Mai Nishimura — Page 2

arXiv2020

L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation

This work presents a deep reinforcement learning framework for interactive navigation in a crowded place. Our proposed approach, Learning to Balance (L2B) framework enables mobile robot agents to steer safely towards their destinations by avoiding collisions with a crowd, while actively clearing a path by asking nearby pedestrians to make room, if necessary, to keep their travel efficient. We observe that the safety and efficiency requirements in crowd-aware navigation have a trade-off in the presence of social dilemmas between the agent and the crowd. On the one hand, intervening in pedestrian paths too much to achieve instant efficiency will result in collapsing a natural crowd flow and may eventually put everyone, including the self, at risk of collisions. On the other hand, keeping in silence to avoid every single collision will lead to the agent's inefficient travel. With this observation, our L2B framework augments the reward function used in learning an interactive navigation policy to penalize frequent active path clearing and passive collision avoidance, which substantially improves the balance of the safety-efficiency trade-off. We evaluate our L2B framework in a challenging crowd simulation and demonstrate its superiority, in terms of both navigation success and collision rate, over a state-of-the-art navigation approach.

cs.RO↗

arXiv2019

Crowd Density Forecasting by Modeling Patch-based Dynamics

Forecasting human activities observed in videos is a long-standing challenge in computer vision, which leads to various real-world applications such as mobile robots, autonomous driving, and assistive systems. In this work, we present a new visual forecasting task called crowd density forecasting. Given a video of a crowd captured by a surveillance camera, our goal is to predict how that crowd will move in future frames. To address this task, we have developed the patch-based density forecasting network (PDFN), which enables forecasting over a sequence of crowd density maps describing how crowded each location is in each video frame. PDFN represents a crowd density map based on spatially overlapping patches and learns density dynamics patch-wise in a compact latent space. This enables us to model diverse and complex crowd density dynamics efficiently, even when the input video involves a variable number of crowds that each move independently. Experimental results with several public datasets demonstrate the effectiveness of our approach compared with state-of-the-art forecasting methods.

cs.CV↗