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A Traffic Sign Discovery Driven System for Traffic Rule Updating

Published: 05 November 2019 Publication History

Abstract

Traffic rules characterize the relationship of connected roads, and are fundamental data in map. They play an important role in route planning, driver navigation, and autonomous driving system. The traffic rule update is a key ability of cartography since they change day by day in real world. Trajectory mining based method has been proposed to update traffic rule. However, it is usually effected by the trajectory outliers and hard to improve the accuracy. In this paper, we consider using data from a different source, the street images captured by driving vehicle recorders (DVR), and propose a traffic sign driven system to update the rules. To collect candidate traffic rule changes, we train an object detection model to detect the traffic signs in street images. To improve the flexibility of the system, we propose a model compression method to reduce the model size, and integrate it into DVR. Finally, we propose a spatio-temporal attention method to cluster the recognized rules. Our system supports the updating of many types of traffic rules, such as no left/right/u turn, no parking, speed limit, etc., and has high extendibility. We validate our image recognition algorithm in a real world dataset, and it achieves a precision of 99.2% and recall rate of 95.1% for image level output. It demonstrates the advantage of our proposed system.

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cover image ACM Conferences
GeoAI '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery
November 2019
96 pages
ISBN:9781450369572
DOI:10.1145/3356471
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

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Author Tags

  1. deep learning
  2. heterogeneous platform deployment
  3. map update
  4. road network structure
  5. traffic sign recognition

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  • Short-paper
  • Research
  • Refereed limited

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SIGSPATIAL '19
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GeoAI '19 Paper Acceptance Rate 17 of 25 submissions, 68%;
Overall Acceptance Rate 17 of 25 submissions, 68%

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