Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps

Autor: Asghar, Rabbia, Diaz-Zapata, Manuel, Rummelhard, Lukas, Spalanzani, Anne, Laugier, Christian

Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior of agents. In this paper, we tackle this problem by representing the scene as dynamic

Externí odkaz: http://arxiv.org/abs/2308.04303

Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks

Autor: Asghar, Rabbia, Rummelhard, Lukas, Spalanzani, Anne, Laugier, Christian

Publikováno v: 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

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 u

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d67bdc3e9b8ac23ed8abe74a15fecbf
https://inria.hal.science/hal-03862095/file/main.pdf

Map Relative Localization Based on Visual and Topological Map Matching

Autor: Asghar, Rabbia, Garzón, Mario, Lussereau, Jerome, Laugier, Christian

Publikováno v: [Technical Report] Inria Chroma. 2019

Accurate and reliable localization is crucial to autonomous vehicle navigation and driver assistance systems. This paper presents a novel approach for online vehicle local-ization in a digital map. Two distinct map matching algorithms are proposed: i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a66c90740bdbb73fb28c304d9b9f43f6
https://inria.hal.science/hal-02412837/document