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 language | English (US) |
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DOIs | |
State | Published - 2012 |
Event | 2012 5th IEEE Symposium on Computational Intelligence for Security and Defence Applications, CISDA 2012 - Ottawa, ON, Canada Duration: Jul 11 2012 → Jul 13 2012 |
Other
Other | 2012 5th IEEE Symposium on Computational Intelligence for Security and Defence Applications, CISDA 2012 |
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Country/Territory | Canada |
City | Ottawa, ON |
Period | 7/11/12 → 7/13/12 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications