Deep Traffic Light Detection for Self-driving Cars from a Large-scale Dataset

Jinkyu Kim, Hyunggi Cho, Myung Hwangbo, Jaehyung Choi, John Canny, Youngwook Paul Kwon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)


Traffic lights perception problem is one of the key challenges for autonomous vehicle controllers in urban areas. While a number of approaches for traffic light detection have been proposed, these methods often require a prior knowledge of map and/or show high false positive rates. Recent successes suggest that deep neural networks will be widely used in self-driving cars, but current public datasets do not provide sufficient amount of labels for training such large deep neural networks. In this paper, we developed a two-step computational method that can detect traffic lights from images in a real-time manner. The first step exploits a deep neural object detection architecture to fine true traffic light candidates. In the second step, a point-based reward system is used to eliminate false traffic lights out of the candidates. To evaluate the proposed approach, we collected a human-annotated large-scale traffic lights dataset (over 60 hours). We also designed a real-world experiment with an instrumented self-driving vehicle and observed that the proposed method was able to handle false traffic lights substantially better compared with the baseline considered.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728103235
Publication statusPublished - 2018 Dec 7
Externally publishedYes
Event21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States
Duration: 2018 Nov 42018 Nov 7

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC


Conference21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Country/TerritoryUnited States

Bibliographical note

Funding Information:
The authors would like to thank Jihyun Yoon and Chankyu Lee for their helpful comments and the anonymous reviewers of this paper. This work was supported in part by Samsung Scholarship.

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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