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Conference Papers Year : 2020

Recognize Moving Objects Around an Autonomous Vehicle Considering a Deep-learning Detector Model and Dynamic Bayesian Occupancy

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Abstract

Perception systems on autonomous vehicles have the challenge of understanding the traffic scene in different situations. The fusion of redundant information obtained from different sources has been shown considerable progress under different methodologies to achieve this objective. However, new opportunities are available to obtain better fusion results with the advance of deep-learning models and computing hardware. In this paper, we aim to recognize moving objects in traffic scenes through the fusion of semantic information with occupancy-grid estimations. Our approach considers a deep-learning model with inference times between 22 to 55 milliseconds. Moreover, we use a Bayesian occupancy framework with a Highly-parallelized design to obtain the occupancygrid estimations.We validate our approach using experimental results with real-world data on urban scenery.
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hal-03038599 , version 1 (03-12-2020)

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  • HAL Id : hal-03038599 , version 1

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Andrés Eduardo Gómez Hernandez, Özgür Erkent, Christian Laugier. Recognize Moving Objects Around an Autonomous Vehicle Considering a Deep-learning Detector Model and Dynamic Bayesian Occupancy. ICARCV 2020 - 16th International Conference on Control, Automation, Robotics & Vision, Dec 2020, Shenzhen, China. pp.1-7. ⟨hal-03038599⟩
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