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

Akhan Almagambetov, Mauricio Casares, Senem Velipasalar

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

23 Citations (Scopus)

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
Title of host publication2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications
Pages1-7
Number of pages7
DOIs
Publication statusPublished - Jul 1 2012

Keywords

  • Vehicles
  • Kalman filters
  • Image color analysis
  • Radar tracking
  • Reliability
  • Accidents
  • Algorithm design and analysis

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