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Communication Dans Un Congrès Année : 2021

Augmented Perception with Cooperative Roadside Vision Systems for Autonomous Driving in Complex Scenarios

Résumé

Performing autonomous driving in urban environments is a challenging task, especially when there is a reduced visibility of traffic participants in complex driving scenarios. For this reason, we investigate the advantages of cooperative perception systems to enhance on-board perception capabilities. In this paper, we present a cooperative roadside vision system for augmenting the embedded perception of an autonomous vehicle navigating in a complex urban scenario. In particular, we use an HD map to implement a map-aided tracking system that merges the information from both onboard and remote sensors. The road users detected by the on-board LiDAR are represented as bounding polygons that include the localization uncertainty whereas, for the camera, the detected bounding boxes are projected in the map frame using a geometric constrained optimization. We report experimental results using two experimental vehicles and a roadside camera in a real traffic scenario in a roundabout. These results quantify how the cooperative data fusion extends the field of view and how the accuracy of the pose estimation of perceived objects is improved.
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Dates et versions

hal-03521341 , version 1 (11-01-2022)

Identifiants

  • HAL Id : hal-03521341 , version 1

Citer

Stefano Masi, Sio-Song Ieng, Philippe Xu, Philippe Bonnifait. Augmented Perception with Cooperative Roadside Vision Systems for Autonomous Driving in Complex Scenarios. 24th IEEE International Conference on Intelligent Transportation Systems (ITSC 2021), Sep 2021, Indianapolis, United States. pp.1140-1146. ⟨hal-03521341⟩
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