ABSTRACT
Every year thousands of people lose their lives due to traffic accidents. Road accidents, especially the ones on the highways are the most fatal ones. Accidents not only cut peoples' lives short but also cause intense financial loss to the country. Many people who survive disastrous accidents are often left with critical injury or paralysis. Particularly in Bangladesh majority of the drivers are minimally educated. They do not have sufficient knowledge and tend to ignore the traffic rules often. As a result the roads are filled with careless drivers. Consequently most of the accidents on the highways and city roads occur due to the lack of awareness of the drivers. Additionally, many of the roads are poorly lit which makes it difficult to drive in unfavorable weather. An autonomous lane detection system can play an important role as a solution to the problem by assisting the driver in seeing the lane clearly. It can also generate warning to the driver in case of an unintentional or incorrect change in lane to avoid accidents. The lane detection method can be further developed to traffic sign and pedestrian detection and eventually a self-driving vehicle. In this study a robust lane detection method using deep learning has been proposed which can detect lanes in various weather and lighting situations. The proposed system has been compared to other baselines in related field demonstrates high accuracy and real time performance.
- Gaikwad, Vijay, and Shashikant Lokhande (2015). "Lane departure identification for advanced driver assistance." IEEE Transactions on Intelligent Transportation Systems 16.2: 910--918.Google ScholarDigital Library
- Yi, Shu-Chung, Yeong-Chin Chen, and Ching-Haur Chang (2015). "A lane detection approach based on intelligent vision." Computers & Electrical Engineering 42: 23--29.Google ScholarDigital Library
- Bhujbal, Pradnya N., and Sandipann P. Narote (2015). "Lane departure warning system based on Hough transform and Euclidean distance." Image Information Processing (ICIIP), Third International Conference on. IEEE, 2015.Google Scholar
- Guo, Jie, Zhihua Wei, and Duoqian Miao (2015). "Lane detection method based on improved RANSAC algorithm." Autonomous Decentralized Systems (ISADS), 2015 IEEE Twelfth International Symposium on. IEEE.Google Scholar
- Son, J., Yoo, H., Kim, S., & Sohn, K. (2015). Real-time illumination invariant lane detection for lane departure warning system. Expert Systems with Applications, 42(4), 1816--1824.Google ScholarDigital Library
- Gurghian, A., Koduri, T., Bailur, S. V., Carey, K. J., & Murali, V. N. (2016). Deeplanes: End-to-end lane position estimation using deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 38--45).Google ScholarCross Ref
- Kortli, Yassin, Mehrez Marzougui, and Mohamed Atri (2016). "Efficient implementation of a real-time lane departure warning system." Image Processing, Applications and Systems (IPAS), 2016 International. IEEE.Google Scholar
- Jung, Soonhong, Junsic Youn, and Sanghoon Sull (2016). "Efficient lane detection based on spatiotemporal images." IEEE Transactions on Intelligent Transportation Systems 17.1: 289--295.Google ScholarDigital Library
- El Hajjouji, I., El Mourabit, A., Asrih, Z., Mars, S., & Bernoussi, B. (2016). FPGA based real-time lane detection and tracking implementation. Electrical and Information Technologies (ICEIT), 2016 International Conference on (pp. 186--190). IEEE.Google ScholarCross Ref
- Lotfy, Omar G., Ahmed A. Kassem, Emad M. Nassief, Hassan A. Ali, Mario R. Ayoub, Magdy A. El-Moursy, and Mohammed M. Farag (2016). "Lane departure warning tracking system based on score mechanism." Circuits and Systems (MWSCAS), 2016 IEEE 59th International Midwest Symposium on, pp. 1--4. IEEE.Google Scholar
- Amaradi, Phanindra, Nishanth Sriramoju, Li Dang, Girma S. Tewolde, and Jaerock Kwon (2016). "Lane following and obstacle detection techniques in autonomous driving vehicles." Electro Information Technology (EIT), 2016 IEEE International Conference on, pp. 0674--0679. IEEE.Google Scholar
- Sattar, Junaed, and Jiawei Mo (2017). "SafeDrive: A Robust Lane Tracking System for Autonomous and Assisted Driving Under Limited Visibility. " arXiv preprint arXiv:1701.08449.Google Scholar
- Yenİaydin, Yasin, and Klaus Werner Schmidt (2018). "A lane detection algorithm based on reliable lane markings." 26th Signal Processing and Communications Applications Conference (SIU). IEEE.Google Scholar
- Nguyen, VanQuang, Heungsuk Kim, SeoChang Jun, and Kwangsuck Boo (2018). "A Study on Real-Time Detection Method of Lane and Vehicle for Lane Change Assistant System Using Vision System on Highway." Engineering Science and Technology, an International Journal.Google ScholarCross Ref
- Rudin, NS Ahmad, Y. Mohd Mustafah, Z. Zainal Abidin, J. Cho, HF Mohd Zaki, NNW Nik Hashim, and H. Abdul Rahman (2018). "Vision-based Lane Departure Warning System." Journal of the Society of Automotive Engineers Malaysia 2, no. 2: 166--176.Google ScholarCross Ref
- Mammeri, Abdelhamid, Guangqian Lu, and Azzedine Boukerche (2015). "Design of lane keeping assist system for autonomous vehicles." New Technologies, Mobility and Security (NTMS), 7th International Conference on. IEEE.Google Scholar
- Ambarak, Jamaa M., Hao Ying, Fazal Syed, and Dimitar Filev (2017). "A neural network for predicting unintentional lane departures." In Industrial Technology (ICIT), 2017 IEEE International Conference on, pp. 492--497. IEEE.Google Scholar
- Wang, Ze, Weiqiang Ren, and Qiang Qiu (2018). "LaneNet: Real-Time Lane Detection Networks for Autonomous Driving." arXiv preprint arXiv:1807.01726.Google Scholar
- Ye, Yang Yang, Xiao Li Hao, and Hou Jin Chen (2018). "Lane detection method based on lane structural analysis and CNNs." IET Intelligent Transport Systems.Google Scholar
- Kim, Jihun, Jonghong Kim, Gil-Jin Jang, and Minho Lee (2017). "Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection." Neural Networks 87: 109--121.Google ScholarCross Ref
- Chen, Ping-Rong, Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan, and Jing-Jhih Lin (2018). "Efficient Road Lane Marking Detection with Deep Learning." arXiv preprint arXiv:1809.03994.Google Scholar
- Zhang, Weiwei, Hui Liu, Xuncheng Wu, Lingyun Xiao, Yubin Qian, and Zhi Fang (2018). "Lane marking detection and classification with combined deep neural network for driver assistance." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering: 0954407018768659.Google Scholar
- Neven, Davy, Bert De Brabandere, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool (2018). "Towards End-to-End Lane Detection: an Instance Segmentation Approach." arXiv preprint arXiv:1802.05591.Google Scholar
- Huang, Yuhao, Shitao Chen, Yu Chen, Zhiqiang Jian, and Nanning Zheng (2018). "Spatial-Temporal Based Lane Detection Using Deep Learning." In IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 143--154. Springer, Cham.Google Scholar
- Sun, T., Tsai, S., and Chan, V. (2006). "HSI color model based lane-marking detection," in Proc. IEEE Intell. Transp. Syst. Conf., pp. 1168--1172.Google Scholar
- Chin, K. and Lin, S. (2005), "Lane detection using color-based segmentation," in Proc. IEEE Intell. Veh. Symp. pp. 706--711.Google Scholar
- Smadi, T. A. (2014), "Real-time lane detection for driver assistance system," J. Circuits Syst., vol. 5, no. 8, pp. 201--207.Google ScholarCross Ref
- Peng, Y.-Z. and Gao, H.-F. (2015), "Lane detection method of statistical Hough transform based on gradient constraint," Int. J. Intell. Inf. Syst., vol.4, no. 2, pp. 40--45.Google Scholar
- Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., and Wang, Z. (2016). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. IEEE Conference on Computer Vision and Pattern Recognition, pages 1874--1883.Google ScholarCross Ref
- Li, Jun, Xue Mei, Danil Prokhorov, and Dacheng Tao (2017). "Deep neural network for structural prediction and lane detection in traffic scene." IEEE transactions on neural networks and learning systems 28, no. 3: 690--703.Google Scholar
- Aly, M. (2008): Real time detection of lane markers in urban streets. In: Intelligent Vehicles Symposium, pp. 7--12. IEEE.Google ScholarCross Ref
- CalTech Lane Dataset. [ONLINE]. Available at: http://www.mohamedaly.info/datasets/caltech-lanes ast accessed: August 29, 2019Google Scholar
- TuSimple Lane Dataset. [ONLINE]. Available at: https://github.com/TuSimple/tusimple-benchmark/wiki Last accessed: August 29, 2019Google Scholar
- Narote, Sandipann P., Pradnya N. Bhujbal, Abbhilasha S. Narote, and Dhiraj M. Dhane (2018). "A review of recent advances in lane detection and departure warning system." Pattern Recognition 73: 216--234.Google ScholarCross Ref
- Road Traffic Injuries. [ONLINE]. Available at: http://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries Last accessed: August 29, 2019Google Scholar
- Over 25000 killed on roads in 3 years. [ONLINE]. Available at: https://en.prothomalo.com/bangladesh/news/181012/25-120-killed-on-roads-in-3-years Last accessed: August 29, 2019Google Scholar
- 2 killed, 30 hurt as bus veers off road. [ONLINE]. Available at: https://www.thedailystar.net/country/road-accidents-in-bangladesh-at-least-kill-4-in-rangpur-pabna-1651195 Last accessed: August 29, 2019Google Scholar
- 11 more lives lost on roads. [ONLINE]. Available at: https://www.thedailystar.net/news/country/bangladesh-brahmanbaria-road-accident-at-least-3-killed-1626097 Last accessed: August 29, 2019Google Scholar
- Over 2,400 deaths on roads this year: Report. [ONLINE]. Available at: https://www.thedailystar.net/country/bangladesh-road-accidents-in-2018-over-2400-deaths-on-roads-report-1598827 Last accessed: August 29, 2019Google Scholar
- A Brief History of Lane Departure Warning. [ONLINE]. Available at: https://medium.com/@ducannissan/a-brief-history-of-lane-departure-warnings-f6316fce8427 Last accessed: August 29, 2019Google Scholar
Index Terms
- A Robust Method for Lane Detection under Adverse Weather and Illumination Conditions Using Convolutional Neural Network
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