OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment

arXiv:2404.02263v1 Announce Type: new
Abstract: The task of motion prediction is pivotal for autonomous driving systems, providing crucial data to choose a vehicle behavior strategy within its surroundings. Existing motion prediction techniques primarily focus on predicting the future trajectory of each agent in the scene individually, utilizing its past trajectory data. In this paper, we introduce an end-to-end neural network methodology designed to predict the future behaviors of all dynamic objects in the environment. This approach leverages the occupancy map …

abstract agent arxiv autonomous autonomous driving autonomous driving systems behavior cs.ai cs.cv cs.ro data driving environment flow focus future pivotal prediction strategy systems trajectory type urban