Visual Forecasting as a Mid-level Representation for Avoidance
Hsuan-Kung Yang Tsung-Chih Chiang Ting-Ru Liu Chun-Wei Huang Jou-Min Liu Chun-Yi Lee
The challenge of navigation in environments with dynamic objects continues to
be a central issue in the study of autonomous agents. While predictive methods
hold promise, their reliance on precise state information makes them less
practical for real-world implementation. This study presents visual forecasting
as an innovative alternative. By introducing intuitive visual cues, this
approach projects the future trajectories of dynamic objects to improve agent
perception and enable anticipatory actions. Our research explores two distinct
strategies for conveying predictive information through visual forecasting: (1)
sequences of bounding boxes, and (2) augmented paths. To validate the proposed
visual forecasting strategies, we initiate evaluations in simulated
environments using the Unity engine and then extend these evaluations to
real-world scenarios to assess both practicality and effectiveness. The results
confirm the viability of visual forecasting as a promising solution for
navigation and obstacle avoidance in dynamic environments.