A Particle Filter-Based Lane Marker Tracking Approach Using a Cubic Spline Model

In this paper we present a particle filter-based lane marker tracking approach using a cubic spline model. The system can detect the two main lane markers (i.e. Lane strips) of marked roads from a monocular camera mounted on the top of a vehicle. Traditional lane marker detection and tracking systems have limitations to properly detect curved roads and to use temporal information to better estimate and track lane markers. The proposed system works on a temporal sequence of images. For each image, one at time, it applies a sequence of steps comprising an inverse perspective mapping to correct for perspective distortions, and a particle filter to smoothly track the lane markers along time. The output of the system is a lane marker generated by a cubic spline interpolation scheme to fit a wider range of lanes. Our system can run in real applications and it was validated with various road and traffic conditions. As a result, it achieves a high precision (98.13%) and a small error (0.0143 meters).