A novel multi-lane detection and tracking system

Abstract

In this paper a novel spline-based multi-lane detection and tracking system is proposed. Reliable lane detection and tracking is an important component of lane departure warning systems, lane keeping support systems or lane change assistance systems. The major novelty of the proposed approach is the usage of the so-called Catmull-Rom spline in combination with the extended Kalman filter tracking. The new spline-based model enables an accurate and flexible modeling of the lane markings. At the same time the application of the extended Kalman filter contributes significantly to the system robustness and stability. There is no assumption about the parallelism or the shapes of the lane markings in our method. The number of lane markings is also not restrained, instead each lane marking is separately modeled and tracked. The system runs on a standard PC in real time (i.e. 30 fps) with WVGA image resolution (752 × 480).The test vehicle has been driven on the roads with challenging scenarios, like worn out lane markings, construction sites, narrow corners, exits and entries of the highways, etc., and good performance has been demonstrated. The quantitative evaluation has been performed using manually annotated video sequences.

A new performance measure and evaluation benchmark for road detection algorithms

“…Approaches providing lane boundaries [23], [24], see Fig. 1b, are traditionally evaluated via the distance of the estimated lane marking to the ground truth borders in the image.…”

“…1b, are traditionally evaluated via the distance of the estimated lane marking to the ground truth borders in the image. By allowing a flexible margin for counting successful border candidates, TP and FP rates can be obtained [24], [25], [26], [27]. The metric deviations of the borders of the road using a segmentation approach in the BEV space are evaluated in [8].…”

Driving Rhythm Method for Driving Comfort Analysis on Rural Highways

“…Even if there are no lane markings on the road surface, the drivers are able to perceive the shape of a "lane" according to the specific situations and then determine their driving behaviours. In fact, this "lane" can be described as driver's visual lane model based on Catmull-Rom Spline [23]. Yu et al [24] and Chen et al [25] have verified that Catmull-Rom Spline is more efficient and precise than others for fitting the driver's visual lane.…”

Multi-source information fusion for drowsy driving detection based on wireless sensor networks

“…In the lane keeping detection method [26], [27] and [28], the camera is fixed on the vehicles, pointing towards the road ahead. The camera monitors the lane tracking according to certain image processing algorithms to determine whether a driver is distracted or drowsy.…”