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.
|Title of host publication
|2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2018 Dec 7
|21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States
Duration: 2018 Nov 4 → 2018 Nov 7
|IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
|21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
|18/11/4 → 18/11/7
Bibliographical noteFunding 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.
© 2018 IEEE.
ASJC Scopus subject areas
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications