VEHICLE NAVIGATION USING ADVANCED OPEN SOURCE COMPUTER VISION

Soubhik Das, Deipali Gore, Paritosh Medhekar, Ameya Kale, Yash Gugale

Abstract


The current operational transport and vehicle systems consist of vehicles running on fossil fuels or battery powered systems. The navigation requires control by a human driver who is responsible for a safe and comfortable journey from one place to another. However, with human intervention there are several drawbacks that may lead to a poor performance by the system. Negligence in driving leading to
fatal accidents, environmental damage, infrastructure damage and destruction, health problems due to constrained sitting postures, long duration of operation and several others, have motivated researchers to look for solutions that will automate the driving process. Considering all these shortcomings of current systems, the new research consists of the use of self driving cars for transport and navigation. The complexity of this problem was seen when the initial systems were built using machine learning techniques that tried to understand and model the dynamic
nature of the environment. As the research progressed, we realized that the system must be trained to respond to a number of unpredictable situations such as rain, snow, lightning, oil spills, potholes, passerby pedestrians and animals, approaching vehicles and many more. We need to consider all these aspects before a fully functional real-time system can be used. We consider the problem of autonomous vehicle by focusing on three major aspects of any self driving car which form the foundation of the entire system. Firstly, we need to be able to detect the lane lines so that our vehicle can orient itself correctly and continue to follow a safe path while being aware of the dynamic environment. Further, it needs to know its departure from the center of the lane in the scenario that it needs to move in order to avoid potholes or other road obstacles.

Keywords


autonomous driving, vehicle navigation, OpenCV, Linear SVM, Canny Edge, CNN

Full Text:

PDF

References


REFERENCES

Maini, Raman, and Himanshu Aggarwal. "Study and comparison of various image edge detection techniques." International journal of image processing (IJIP) 3.1 (2009): 1-11.

Low, Chan Yee, Hairi Zamzuri, and Saiful Amri Mazlan. "Simple robust road lane detection algorithm." Intelligent and Advanced Systems (ICIAS), 2014 5th International Conference on. IEEE, 2014.

Zhu, Qiang, et al. "Fast human detection using a cascade of histograms of oriented gradients." Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 2. IEEE, 2006.

The German Traffic Sign Recognition Benchmark paper by Turk, Matthew A., et al. "VITS-A vision system for autonomous land vehicle navigation." IEEE Transactions on Pattern Analysis and Machine Intelligence 10.3 (1988): 342-361.

Narendra, V. G., and K. S. Hareesh. "Study and comparison of various image edge detection techniques used in quality inspection and evaluation of agricultural and food products by computer vision." International Journal of Agricultural and Biological Engineering 4.2 (2011): 83-90.

Driankov, Dimiter, and Alessandro Saffiotti, eds. Fuzzy logic techniques for autonomous vehicle navigation. Vol. 61. Physica, 2013.

Leonard, John J., and Alexander Bahr. "Autonomous underwater vehicle navigation." Springer Handbook of Ocean Engineering. Springer, Cham, 2016. 341-358.

Li, Qingquan, et al. "A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios." IEEE Transactions on Vehicular Technology 63.2 (2014): 540-555.

Ujjainiya, Lohit, and M. Kalyan Chakravarthi. "Raspberry—Pi Based Cost Effective Vehicle Collision Avoidance System Using Image Processing." ARPN J. Eng. Appl. Sci 10 (2015): 3001-3005.

Guo, Chunzhao, Seiichi Mita, and David McAllester. "A vision system for autonomous vehicle navigation in challenging traffic scenes using integrated cues." Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on. IEEE, 2010.

Lorsakul, Auranuch, and Jackrit Suthakorn. "Traffic sign recognition using neural network on OpenCV: Toward intelligent vehicle/driver assistance system." 4th International Conference on Ubiquitous Robots and Ambient Intelligence. 2007.

Neto, A. Miranda, et al. "Robust horizon finding algorithm for real-time autonomous navigation based on monocular vision." Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on. IEEE, 2011.

Souza, Jefferson R., et al. "Template-based autonomous navigation in urban environments." Proceedings of the 2011 ACM Symposium on Applied Computing. ACM, 2011.

Bento, L. Conde, et al. "Sensor fusion for precise autonomous vehicle navigation in outdoor semi-structured environments." Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE. IEEE, 2005.

Stafylopatis, Andreas, and Konstantinos Blekas. "Autonomous vehicle navigation using evolutionary reinforcement learning." European Journal of Operational Research 108.2 (1998): 306-318.

Cui, Youjing, and Shuzhi Sam Ge. "Autonomous vehicle positioning with GPS in urban canyon environments." IEEE transactions on robotics and automation 19.1 (2003): 15-21.




DOI: https://doi.org/10.26483/ijarcs.v9i1.5350

Refbacks

  • There are currently no refbacks.




Copyright (c) 2018 International Journal of Advanced Research in Computer Science