Title: Robust road lanes and traffic signs recognition for driver assistance system

Authors: Ahmed Hechri; Rihab Hmida; Abdellatif Mtibaa

Addresses: Laboratory E?E, Faculty of Sciences of Monastir, University of Monastir, Av Ibn ElJazzar 5019 Monastir, Tunisia; National School of Engineering of Monastir, Av Ibn ElJazzar 5019 Monastir, Tunisia ' Laboratory E?E, Faculty of Sciences of Monastir, University of Monastir, Av Ibn ElJazzar 5019 Monastir, Tunisia; National School of Engineering of Monastir, Av Ibn ElJazzar 5019 Monastir, Tunisia ' Laboratory E?E, Faculty of Sciences of Monastir, University of Monastir, Av Ibn ElJazzar 5019 Monastir, Tunisia; National School of Engineering of Monastir, Av Ibn ElJazzar 5019 Monastir, Tunisia

Abstract: Increasing safety and reducing road accidents, thereby saving lives, are one of the great interests in the context of advanced driver assistance systems. Apparently, among the complex and challenging tasks of future intelligent vehicles is road lanes detection and road signs recognition. In this paper, a multitask driver assistance system has been proposed. First, the system provides the driver with real-time information from lanes markers and road signs, which consist of the most important and challenging tasks. Secondly, it generates an acoustic warning to the driver in advance of any danger. This warning then allows the driver to take appropriate corrective actions in order to mitigate or completely avoid the event. The proposed system was tested on real road scene captured from moving vehicle. From the experimental results, the system has demonstrated a robust performance for detecting the road lanes and signs under different conditions.

Keywords: driver assistance systems; DASs; multitasking; intelligent vehicles; lane detection; road signs; sign recognition; road lanes; traffic signs; lane markers; acoustic warning signals.

DOI: 10.1504/IJCSE.2015.067046

International Journal of Computational Science and Engineering, 2015 Vol.10 No.1/2, pp.202 - 209

Received: 12 Nov 2011
Accepted: 30 May 2012

Published online: 25 Jan 2015 *

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