Vision based driver interactive safety driving agent system

ABSTRACT Most of ADAS (Advanced Driver Assistance Systems) have some drawbacks because they do not use all but only some parts of the information on Traffic environment-Vehicle-Driver (TVD). Recently, researches on making more efficient and effective assistant system by fusing all the information from TVD are being executed to overcome this limitation. As a part of this research, this paper focuses on decision-level fusion to estimate the driver’s vigilance from the vision information of traffic environment and driver state. The driver state is defined as the tracked gazing direction and face feature points of the driver which is obtained by using the Adaboost face detector and Active Appearance Model (AAM). The state of traffic environment is defined as lane-off or collision from the information of the vehicle’s forward area, i.e., lanes, vehicles, and ego-motion. Warnings for lane-off, collision, and driver inattention are generated by fusing these in and out vehicle vision information.

[1]  Rainer Lienhart,et al.  Using CART to segment road images , 2006, Electronic Imaging.

[2]  Takeo Kanade,et al.  Real-time combined 2D+3D active appearance models , 2004, CVPR 2004.

[3]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[4]  Bo Zhang,et al.  Color-based road detection in urban traffic scenes , 2004, IEEE Transactions on Intelligent Transportation Systems.

[5]  David J. LeBlanc,et al.  CAPC: an implementation of a road-departure warning system , 1996, Proceeding of the 1996 IEEE International Conference on Control Applications IEEE International Conference on Control Applications held together with IEEE International Symposium on Intelligent Contro.

[6]  Luke Fletcher,et al.  Correlating driver gaze with the road scene for driver assistance systems , 2005, Robotics Auton. Syst..

[7]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

[8]  Se-Young Oh,et al.  Real-time Recognition of Facial Expression using Active Appearance Model with Second Order Minimization and Neural Network , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[9]  Amnon Shashua,et al.  A robust method for computing vehicle ego-motion , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[10]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[11]  Tarak Gandhi,et al.  Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety , 2007, IEEE Transactions on Intelligent Transportation Systems.