Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks
| Autor: | Asghar, Rabbia, Rummelhard, Lukas, Spalanzani, Anne, Laugier, Christian |
|---|---|
| Přispěvatelé: | Robots coopératifs et adaptés à la présence humaine en environnements (CHROMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Robotics Artificial Intelligence (cs.AI) Scene Prediction Deep Learning Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Robotics (cs.RO) Autonomous Vehicles |
| Zdroj: | ICARCV 2022-17th International Conference on Control, Automation, Robotics and Vision ICARCV 2022-17th International Conference on Control, Automation, Robotics and Vision, Dec 2022, Singapore, Singapore |
| Popis: | Prediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions between various dynamic agents, such as vehicles and vulnerable road users. Previous approaches have used egocentric occupancy grid maps to represent and predict dynamic environments. However, these predictions suffer from blurriness, loss of scene structure at turns, and vanishing of agents over longer prediction horizon. In this work, we propose a novel framework to make long-term predictions by representing the traffic scene in a fixed frame, referred as allo-centric occupancy grid. This allows for the static scene to remain fixed and to represent motion of the ego-vehicle on the grid like other agents'. We study the allo-centric grid prediction with different video prediction networks and validate the approach on the real-world Nuscenes dataset. The results demonstrate that the allo-centric grid representation significantly improves scene prediction, in comparison to the conventional ego-centric grid approach. Comment: ICARCV 2022-17th International Conference on Control, Automation, Robotics and Vision |
| Databáze: | OpenAIRE |
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