Real time detection of the back view of a preceding vehicle for automated heterogeneous platoons in unstructured environment using video

Mohammad Alfraheed (), Alicia Dröge (), Ralph Kunze, Max Klingender (), Daniel Schilberg () and Sabina Jeschke ()
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Mohammad Alfraheed: RWTH Aachen University, IMA/ZLW
Alicia Dröge: RWTH Aachen University, IMA/ZLW
Max Klingender: RWTH Aachen University, IMA/ZLW
Daniel Schilberg: RWTH Aachen University, IMA/ZLW
Sabina Jeschke: RWTH Aachen University, IMA/ZLW

A chapter in Automation, Communication and Cybernetics in Science and Engineering 2011/2012, 2013, pp 569-582 from Springer

Abstract: Abstract Due to the increase in road transportation several projects concerning automated highway systems were initiated to optimize highway capacity. In the future, the developed techniques should be applicable in unstructured environment (e.g. desert) and adaptable for heterogeneous vehicles. But before, several challenges, i.e. independency of lane markings, have to be overcome. Our solution is to consider the back view of the preceding vehicle as a reference point for the lateral and longitudinal control of the following vehicle. This solution is independent from the environmental structure as well as additional equipment like infrared emitters. Thus, both the detection and tracking process of the back view are needed to provide automated highway systems with the distance and the deviation degree of the preceding vehicle. In this paper the first step, the detection and location of the back view on video streams, is discussed. For a definite detection in a heterogeneous platoon several features of the back view are detected. A method is proposed to run rejection cascades generated by the AdaBoost classifier theory on the video stream. Compared to other methods related to object detection, the proposed method reduces the running time for the detection of the back view to 0.03–0.08 s/frame. Furthermore, the method enables a more accurate detection of the back view.

Keywords: Video Stream; Current Frame; Real Time Detection; Intelligent Transportation System; Real Time Constraint (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-33389-7_45

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DOI: 10.1007/978-3-642-33389-7_45

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