Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments
This work addresses the need for proactive decision-making in autonomous vehicles, though it appears incremental as it builds on existing detection and prediction methods.
The paper tackles the problem of predicting future occupancy states in urban environments for autonomous vehicles by integrating moving object detection and segmentation with spatiotemporal prediction, achieving higher accuracy than baseline methods on the Waymo Open Dataset.
Detection and segmentation of moving obstacles, along with prediction of the future occupancy states of the local environment, are essential for autonomous vehicles to proactively make safe and informed decisions. In this paper, we propose a framework that integrates the two capabilities together using deep neural network architectures. Our method first detects and segments moving objects in the scene, and uses this information to predict the spatiotemporal evolution of the environment around autonomous vehicles. To address the problem of direct integration of both static-dynamic object segmentation and environment prediction models, we propose using occupancy-based environment representations across the whole framework. Our method is validated on the real-world Waymo Open Dataset and demonstrates higher prediction accuracy than baseline methods.