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Abstract:

By now the industry agrees that HD maps are needed for autonomous driving. Cars need to position themselves very accurately and be aware of the road ahead in order to plan their next move. In this panel session on HD mapping, three map companies will talk about how they are building HD maps in different regions of the world and how to automate map making using AI. But even more important, how will they keep their HD maps up to date? After all, an out of date HD map will not help the car. The panel will also touch on how cars should access the latest, most up-to-date HD maps with minimal latency.

By now the industry agrees that HD maps are needed for autonomous driving. Cars need to position themselves very accurately and be aware of the road ahead in order to plan their next move. In this panel session on HD mapping, three map companies will talk about how they are building HD maps in different regions of the world and how to automate map making using AI. But even more important, how will they keep their HD maps up to date? After all, an out of date HD map will not help the car. The panel will also touch on how cars should access the latest, most up-to-date HD maps with minimal latency.

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Topics:
Autonomous Vehicles
Type:
Panel
Event:
GTC Europe
Year:
2018
Session ID:
E8469
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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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