Abstract
Lane lines play a key role in indicating traffic flow and directing vehicles; lane detection serves as a core component in most of the modern-day advanced driver assistance systems (ADASs). Computer vision-based lane detection is an essential technology for self-driving cars. This paper proposes a lane detection system to detect lane lines in urban streets and highway roads under complex background. In order to nullify the distortions caused by the camera lenses, we generate a distortion model by calibrating images against a known object, and apply a generalized filtering approach using Sobel operator (Canny edge detection) in HLS color space. A bird eye view of image is generated using perspective transformation. A special search strategy using sliding window algorithm is used to detect lane lines, and later, curve fitting is done using polynomial regression. Thus, the obtained lane detector is overlaid upon a video to fill the detected portion of the lane. Then, it is applied to the video to detect lane lines. The image processing pipeline is written in Python using OpenCV libraries, and video processing is done using MoviePy. In this paper, the system developed is tested by applying it on a video taken from a camera mounted over the car. The environment used to implement the system is Anaconda. The results obtained show that the proposed system for lane detection, self-calibration and vehicle offset estimation is effective, accurate for both straight and curved lanes and robust to challenging environments.
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References
Xing Y et al (2018) Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision. IEEE/CAA J Autom Sinica 5(3):645–661
Shirke S, Rajabhushanam C (2017) A study of lane detection techniques and lane departure system. In: 2017 international conference on algorithms, methodology, models and applications in emerging technologies (ICAMMAET), Chennai, 2017, pp 1–4
Feniche M, Mazri T (2019) Lane detection and tracking for intelligent vehicles: a survey. In: 2019 international conference of computer science and renewable energies (ICCSRE), Agadir, Morocco, 2019, pp 1–4
Gao Q, Feng Y, Wang L (2017) A real-time lane detection and tracking algorithm. In: 2017 IEEE 2nd information technology, networking, electronic and automation control conference (ITNEC), Chengdu, 2017, pp 1230–1234
de Paula MB, Jung CR (2015) Automatic detection and classification of road lane markings using onboard vehicular cameras. IEEE Trans Intell Transp Syst 16(6):3160–3169
Wang H, Wang Y, Zhao X, Wang G, Huang H, Zhang J (2019) Lane detection of curving road for structural highway with straight-curve model on vision. IEEE Trans Veh Technol 68(6):5321–5330
Yuan C, Chen H, Liu J, Zhu D, Xu Y (2018) Robust lane detection for complicated road environment based on normal map. IEEE Access 6:49679–49689
Lim KH, Seng KP, Ang L, Chin SW (2009) Lane detection and Kalman-based linear-parabolic lane tracking. In: 2009 international conference on intelligent human-machine systems and cybernetics, Hangzhou, Zhejiang, 2009, pp 351–354
He J, R Hong, Gong J, Huang W (2010) A lane detection method for lane departure warning system. In: 2010 international conference on optoelectronics and image processing, Haikou, 2010, pp 28–31
Gupta A, Choudhary A (2018) A framework for camera-based real-time lane and road surface marking detection and recognition. IEEE Trans Intell Veh 3(4):476–485
Bosetti P, Da Lio M, Saroldi A (2015) On curve negotiation: from driver support to automation. IEEE Trans Intell Transp Syst 16(4):2082–2093
Rossi R, Gecchele G, Gastaldi M, Biondi F, Mulatti C (2017) An advanced driver assistance system for improving driver ability. Design and test in virtual environment. In: 2017 5th IEEE international conference on models and technologies for intelligent transportation systems (MT-ITS), Naples, 2017, pp 509–513
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Dharoori, R.A., Narayanan, S. (2021). Vision-Based Lane Detection for Advanced Driver Assistance Systems. In: Zhou, N., Hemamalini, S. (eds) Advances in Smart Grid Technology. Lecture Notes in Electrical Engineering, vol 688. Springer, Singapore. https://doi.org/10.1007/978-981-15-7241-8_40
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DOI: https://doi.org/10.1007/978-981-15-7241-8_40
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