Asghar, Rabbia (Centre Inria de l'Université Grenoble Alpes), Liu, Wenqian (Inria), Rummelhard, Lukas (INRIA), Spalanzani, Anne (INRIA), Laugier, Christian (INRIA)

Flow-Guided Motion Prediction with Semantics and Dynamic Occupancy Grid Maps

Scheduled for presentation during the Regular Session "Sensing, Vision, and Perception V" (FrAT5), Friday, September 27, 2024, 10:30−10:50, Salon 13

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 7, 2024

Abstract

Accurate prediction of driving scenes is essential for road safety and autonomous driving. Occupancy Grid Maps (OGMs) are commonly employed for scene prediction due to their structured spatial representation, flexibility across sensor modalities and integration of uncertainty. Recent studies have successfully combined OGMs with deep learning methods to predict the evolution of scene and learn complex behaviors. These methods, however, do not consider prediction of flow or velocity vectors in the scene. In this work, we propose a novel multi-task framework that leverages dynamic OGMs and semantic information to predict both future vehicle semantic grids and the future flow of the scene. This incorporation of semantic flow not only offers intermediate scene features but also enables the generation of warped semantic grids. Evaluation on the real-world NuScenes dataset demonstrates improved prediction capabilities and enhanced ability of the model to retain dynamic vehicles within the scene.