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Abstract:

A fundamental component required for safe autonomous driving is highly accurate maps, which contain semantic information regarding the position and content of traffic signs, lane markings, and other road features. At Mapscape, we rely heavily on deep learning to extract information from images to aid with the creation of these maps. In this presentation, we explore two parts of our process: An object detection pipeline running onboard NVIDIA Jetson devices through TensorRT capable of recognizing among 167 different traffic signs in real time, and a semantic segmentation pipeline capable of extracting up to 45 different road level features such as lanes, arrows, and other road surface signs.

A fundamental component required for safe autonomous driving is highly accurate maps, which contain semantic information regarding the position and content of traffic signs, lane markings, and other road features. At Mapscape, we rely heavily on deep learning to extract information from images to aid with the creation of these maps. In this presentation, we explore two parts of our process: An object detection pipeline running onboard NVIDIA Jetson devices through TensorRT capable of recognizing among 167 different traffic signs in real time, and a semantic segmentation pipeline capable of extracting up to 45 different road level features such as lanes, arrows, and other road surface signs.

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Topics:
Computer Vision, Intelligent Machines, IoT & Robotics, HD Mapping
Type:
Talk
Event:
GTC Europe
Year:
2017
Session ID:
23304
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