2022/05/06 by Khushdeep Singh Mann, Abhishek Tomy, Mann, Khushdeep Singh +7 · 2 citations
Computer Science · Engineering · Social Sciences · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Traffic Prediction and Management Techniques #Video Surveillance and Tracking Methods
paper · doi:10.48550/arxiv.2205.03212
openalex publication_date 2022/05/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Reliably predicting future occupancy of highly dynamic urban environments is an important precursor for safe autonomous navigation. Common challenges in the prediction include forecasting the relative position of other vehicles, modelling the dynamics of vehicles subjected to different traffic conditions, and vanishing surrounding objects. To tackle these challenges, we propose a spatio-temporal prediction network pipeline that takes the past information from the environment and semantic labels separately for generating future occupancy predictions. Compared to the current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds and in a relatively complex environment from the nuScenes dataset. Our experimental results demonstrate the ability of spatio-temporal networks to understand scene dynamics without the need for HD-Maps and explicit modeling dynamic objects. We publicly release our occupancy grid dataset based on nuScenes to support further research.