Visionnet: A Coarse-To-Fine Motion Prediction Algorithm Based On Active Interaction-Aware Drivable Space Learning

Abstract

Trajectory prediction is a fundamental task in many real applications such as autonomous robotics and video surveillance. In this paper, we propose a novel vision-based trajectory prediction method which is able to extract the interactive features by active global interaction-aware drivable space learning. The learned global interaction-aware drivable spaces denote the areas with low occupation probabilities, which provide the regions and directions that the agents can move into. Specifically, our method describes a sequence of motion states, i.e. the location, the velocity and the acceleration, as occupancy grid maps, and then use them to train the deep learning model in the supervised manner. Moreover, an interactive loss for training the inference net of drivable spaces and trajectory prediction net simultaneously is introduced. Comparisons with state-of-the-art methods on benchmark datasets demonstrate the effectiveness of the proposed method.