An improved and low-complexity neural network model for curved lane detection of autonomous driving system

Lane detection is considered as a key component for the autonomous vehicles of futuristic transport systems. The extraction and fitting problems of lane markers from the road images have been addressed in recent research studies. However, these are still ineffective under curved lane and color light conditions. Illumination changes and the road structure mainly affect the efficiency of lane detection which may lead to traffic accidents especially in case of a curved road. In this work, a novel method based on a low complexity but efficient functional link artificial neural network (FLANN) model is proposed to estimate the entire lane by interpolating the lane markers under different road scenarios. The road image is divided into regions and the extracted lane markers from each region are employed in the proposed trigonometric, polynomial, exponential, and Chebyshev functional expansion based FLANN models for the estimation of the lane curvature. The performance of each model is evaluated and tested on road images using three standard datasets. In terms of mean accuracy and computational time out of four FLANN models, the Chebyshev FLANN (CFLNN) outperforms other three proposed methods. The detection accuracy of CFLANN model is found to be 94.3% which is higher than the results reported by other three models.