Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments

TL;DR

This paper introduces a deep learning framework that combines moving obstacle detection, segmentation, and environment occupancy prediction to enhance autonomous vehicle decision-making in urban settings.

Contribution

It presents a novel integrated approach using occupancy-based representations for improved spatiotemporal environment prediction in autonomous driving.

Findings

Achieves higher prediction accuracy than baseline methods on Waymo dataset.

Effectively integrates static and dynamic object segmentation with environment prediction.

Demonstrates robustness in real-world urban scenarios.

Abstract

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.