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.

[1]  Hyunchul Shin,et al.  Robust hypothesis generation method using binary blob analysis for multi-lane detection , 2017, IET Image Process..

[2]  Dinh Cong Le,et al.  A bilinear functional link artificial neural network filter for nonlinear active noise control and its stability condition , 2018 .

[3]  Ganapati Panda,et al.  Development and performance evaluation of FLANN based model for forecasting of stock markets , 2009, Expert Syst. Appl..

[4]  Ganapati Panda,et al.  Nonlinear channel equalization for QAM signal constellation using artificial neural networks , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[5]  Ahmed Khattab,et al.  A Robust, Real-Time and Calibration-Free Lane Departure Warning System , 2019, 2019 IEEE International Symposium on Circuits and Systems (ISCAS).

[6]  Yi Yu,et al.  Time delay Chebyshev functional link artificial neural network , 2019, Neurocomputing.

[7]  Ke Sun,et al.  Fast lane detection based on bird’s eye view and improved random sample consensus algorithm , 2016, Multimedia Tools and Applications.

[8]  Kwanghoon Sohn,et al.  Gradient-Enhancing Conversion for Illumination-Robust Lane Detection , 2013, IEEE Transactions on Intelligent Transportation Systems.

[9]  P. Kanungo,et al.  Automatic Lane Detection in NH5 of Odisha , 2015 .

[10]  Kwanghoon Sohn,et al.  Real-time illumination invariant lane detection for lane departure warning system , 2015, Expert Syst. Appl..

[11]  Meetha V. Shenoy,et al.  A lightweight ANN based robust localization technique for rapid deployment of autonomous systems , 2020, J. Ambient Intell. Humaniz. Comput..

[12]  Jannik Fritsch,et al.  A new performance measure and evaluation benchmark for road detection algorithms , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[13]  Chen Change Loy,et al.  Learning Lightweight Lane Detection CNNs by Self Attention Distillation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[14]  Oğuzhan Urhan,et al.  Real-time illumination and shadow invariant lane detection on mobile platform , 2019, Journal of Real-Time Image Processing.

[15]  Wenqi Wang,et al.  The research on edge detection algorithm of lane , 2018, EURASIP J. Image Video Process..

[16]  Jian Yang,et al.  Vanishing Point Constrained Lane Detection With a Stereo Camera , 2018, IEEE Transactions on Intelligent Transportation Systems.

[17]  Raja Muthalagu,et al.  Lane detection technique based on perspective transformation and histogram analysis for self-driving cars , 2020, Comput. Electr. Eng..

[18]  Jianwei Niu,et al.  Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting , 2016, Pattern Recognit..

[19]  Wei Zhang,et al.  Lane Detection and Tracking Using a Parallel-snake Approach , 2015, J. Intell. Robotic Syst..

[20]  Nasser Kehtarnavaz,et al.  JRTIP first issue editorial , 2006, Journal of Real-Time Image Processing.

[21]  Xiang-Hai Wang,et al.  Lane Boundary Detection Algorithm Based on Vector Fuzzy Connectedness , 2017, Cognitive Computation.

[22]  Ahmed Khattab,et al.  A robust, real-time and calibration-free lane departure warning system , 2019, Microprocess. Microsystems.

[23]  J. R. MacDonald.  The Government of India , 2017 .

[24]  Jun Gao,et al.  Personalized detection of lane changing behavior using multisensor data fusion , 2019, Computing.

[25]  Dongsuk Kum,et al.  Robust multi-lane detection and tracking using adaptive threshold and lane classification , 2018, Machine Vision and Applications.

[26]  Mohamed Amine Fakhfakh,et al.  Bayesian curved lane estimation for autonomous driving , 2020, J. Ambient Intell. Humaniz. Comput..

[27]  He Huang,et al.  Lane Detection of Curving Road for Structural Highway With Straight-Curve Model on Vision , 2019, IEEE Transactions on Vehicular Technology.

[28]  Mohamed Aly,et al.  Real time detection of lane markers in urban streets , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[29]  Fang Zheng,et al.  Improved Lane Line Detection Algorithm Based on Hough Transform , 2018, Pattern Recognition and Image Analysis.

[30]  Ganapati Panda,et al.  Identification of nonlinear dynamic systems using functional link artificial neural networks , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[31]  Deok-Jin Lee,et al.  A Precise Lane Detection Algorithm Based on Top View Image Transformation and Least-Square Approaches , 2016, J. Sensors.

[32]  Pierre Charbonnier,et al.  Evaluation of Road Marking Feature Extraction , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.