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.


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    Titel :

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


    Beteiligte:
    Kim, Jinkyu (Autor:in) / Cho, Hyunggi (Autor:in) / Hwangbo, Myung (Autor:in) / Choi, Jaehyung (Autor:in) / Canny, John (Autor:in) / Kwon, Youngwook Paul (Autor:in)


    Erscheinungsdatum :

    2018-11-01


    Format / Umfang :

    2445422 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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