Abstract
Unstructured road detection is one of the difficult tasks for self-driving cars than the detection of road with proper lane markings. Also, it is an extremely difficult task to detect the highly deteriorated district and taluk roads using currently available vision-based algorithm; as the exposed gravels and grass covering on both sides (edges) of road adds more noise in the input image. To address this issue, a novel vision-based road detection technique is proposed in this research work. This new method uses noise to enhance the road edges in the image and unstructured straight road is detected using Hough Transform. This paper is divided into three parts: bird’s eye view transformation of 2D road image received from the vehicle camera to correct the perspective distortion and easier identification of Region of Interest (ROI), addition of noise in the ROI of image to differentiate the valid road from the background and use of Hough Transform to identify the edges of unstructured road having no road markings. Finally, we present a simple way to find the centerline on the detected road for departure warning to reduce the additional computation. The simulation results corroborate that the proposed method detects the road successfully and can be used in real-time detection system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Saluja, N.: Road Accidents Claimed Over 1.5 Lakh Lives in 2018, Over Speeding Major Killer. The Ministry of Road Transport, India (2019)
Direct, Budget: Car Accident Statistics 2019. Auto & General Services Pty Ltd, Australia (2019)
Atkins Ltd.: Research on the impacts of connected and autonomous vehicles (CAVs) on traffic flow, U.K. Dept. Transport, London, U.K. (2016)
Chen, M., Jochem, T., Pomerleau, D.: Aurora: A vision-based roadway departure warning system. In: Proceedings of IEEE RSJ Intelligent Robots and Systems, issue no. 03, pp. 243–248 (1995)
Nguyen, V., Kim, H., Jun, S., Boo, K.: A study on real-time detection method of lane and vehicle for lane change assistant system using vision system on highway. Eng. Sci. Technol. Int. J. 21(5), 822–833 (2018)
Chiu, K.Y., Lin, S.F.: Lane detection using color-based segmentation. In: IEEE Proceedings. Intelligent Vehicles Symposium, pp. 706–711 (2005)
Wu, C.F., Lin, C.J., Lin, H.Y., Chung, H.: Adjacent lane detection and lateral vehicle distance measurement using vision-based neuro-fuzzy approaches. J. Appl. Res. Technol. 11(2), 251–258 (2013)
Phung, S.L., Le, M.C., Bouzerdoum, A.: Pedestrian lane detection in unstructured scenes for assistive navigation. Comput. Vis. Image Underst. 149, 186–196 (2016)
Kong, H., Audibert, J.Y., Ponce, J.: General road detection from a single image. IEEE Trans. Image Process. 19(8), 2211–2220 (2010)
Li, M., Li, Y., Jiang, M.: Lane detection based on connection of various feature extraction methods. Adv. Multimedia (2018)
Malmir, S., Shalchian, M.: Design and FPGA implementation of dual-stage lane detection, based on Hough transform and localized stripe features. Microprocess. Microsyst. 64, 12–22 (2019)
Wu, L., Yu, Q., Xu, T., Zhang, S.: An unstructured road detection method with multi-environmental adaptability. Int. J. Simul. Syst. Sci. Technol. 17(12) (2016)
Em, P.P., Hossen, J., Fitrian, I., Wong, E.K.: Vision-based lane departure warning framework. In: Heliyon, vol. 5, issue no. 8 (2019)
Yi, S.C., Chen, C., Chang, C.H.: A lane detection approach based on intelligent vision. Comput. Electr. Eng. 42, 23–29 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rajesh, R., Manivannan, P.V. (2021). A Vision-Based Unstructured Road Detection Algorithm for Self-driving Cars. In: Deepak, B.B.V.L., Parhi, D.R.K., Biswal, B.B. (eds) Advanced Manufacturing Systems and Innovative Product Design. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-9853-1_31
Download citation
DOI: https://doi.org/10.1007/978-981-15-9853-1_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9852-4
Online ISBN: 978-981-15-9853-1
eBook Packages: EngineeringEngineering (R0)