Autonomous tracking of vehicle rear lights and detection of brakes and turn signals

Akhan Almagambetov, Mauricio Casares, Senem Velipasalar

Research output: Contribution to conferencePaper

21 Scopus citations

Abstract

Automatic detection of vehicle alert signals is extremely critical in autonomous vehicle applications and collision avoidance systems, as these detection systems can help in the prevention of deadly and costly accidents. In this paper, we present a novel and lightweight algorithm that uses a Kalman filter and a codebook to achieve a high level of robustness. The algorithm is able to detect braking and turning signals of the vehicle in front both during the daytime and at night (daytime detection being a major advantage over current research), as well as correctly track a vehicle despite changing lanes or encountering periods of no or low-visibility of the vehicle in front. We demonstrate that the proposed algorithm is able to detect the signals accurately and reliably under different lighting conditions.

Original languageEnglish (US)
DOIs
StatePublished - Oct 26 2012
Event2012 5th IEEE Symposium on Computational Intelligence for Security and Defence Applications, CISDA 2012 - Ottawa, ON, Canada
Duration: Jul 11 2012Jul 13 2012

Other

Other2012 5th IEEE Symposium on Computational Intelligence for Security and Defence Applications, CISDA 2012
CountryCanada
CityOttawa, ON
Period7/11/127/13/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

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    Almagambetov, A., Casares, M., & Velipasalar, S. (2012). Autonomous tracking of vehicle rear lights and detection of brakes and turn signals. Paper presented at 2012 5th IEEE Symposium on Computational Intelligence for Security and Defence Applications, CISDA 2012, Ottawa, ON, Canada. https://doi.org/10.1109/CISDA.2012.6291543