ABSTRACT
The objective of our venture is to actualize a lane detection framework considering expanding the usability of driver assistance system functions, attempting to overcome the problems of the generalized method. It is tuned to detect mountainous roads on the Indian hilly terrain. The proposed technique acquires a video sequence as input and performs operation on each frame to extract the candidate lane lines. The novelty of this work is that a unique method is used to decide upon whether the lane is on the straight or curved roads and adaptive thresholding is used as it can adjust its value for various environmental conditions. Kalman filter is used in the lane tracking stage where the feasible results obtained for the present frame are compared with the results in the previous video frames. Comparison of the presented algorithm with the conventional method is carried out and attained an average detection accuracy of 93%.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Kodeeswari Manoharan is currently pursuing PhD degree from Electronics and communication engineering department, NIT Hamirpur, Himachal Pradesh, India. Her research interests include Image processing, Computer vision, Intelligent vehicles and Road environment understanding.
Philemon Daniel obtained his PhD from NIT Hamirpur in 2015. Presently he is working as an Assistant Professor in E&CE Department, National Institute of Technology Hamirpur, HP, India. His research interests include Driver Assistance System, Natural Language Processing and FPGA based system design.
ORCID
Kodeeswari Manoharan http://orcid.org/0000-0002-3808-2617