Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal

• URL: http://arxiv.org/abs/2008.09672v1
• Date: Fri, 21 Aug 2020 20:36:21 GMT
• Title: Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal
• Authors: Jorge Beltr\'an, Carlos Guindel, Irene Cort\'es, Alejandro Barrera, Armando Astudillo, Jes\'us Urdiales, Mario \'Alvarez, Farid Bekka, Vicente Milan\'es, and Fernando Garc\'ia
• Abstract summary: This paper presents a framework for 3D object detection and tracking for autonomous vehicles. The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection. A variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack.
• Score: 87.11988786121447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, a multi-modal 360$^{\circ}$ framework for 3D object detection and tracking for autonomous vehicles is presented. The process is divided into four main stages. First, images are fed into a CNN network to obtain instance segmentation of the surrounding road participants. Second, LiDAR-to-image association is performed for the estimated mask proposals. Then, the isolated points of every object are processed by a PointNet ensemble to compute their corresponding 3D bounding boxes and poses. Lastly, a tracking stage based on Unscented Kalman Filter is used to track the agents along time. The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection. A wide variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack in a real autonomous driving application.

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