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Publication Type : Conference Paper
Publisher : Advances in Intelligent Systems and Computing
Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 898, p.639-648 (2019)
ISBN : 9789811333927
Keywords : Autonomous Vehicles, Computational platforms, Deep neural networks, Distance estimation, Electric automobiles, Human intervention, Object Detection, Obstacle detection, Obstacle detectors, Optical radar, Radar, Research purpose, Soft computing, Stereo image processing, Stereo vision, Time of flight, Tracking radar, Vision algorithms, Vision sensors
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2019
Abstract : Automation—replacement of humans with technology—is everywhere. It is going to become far more widespread, as industries are continuing to adapt to new technologies and are trying to find novel ways to save time, money, and effort. Automation in automobiles aims at replacing human intervention during the run time of vehicle by perceiving the environment around automobile in real time. This can be achieved in multitude of ways such as using passive sensors like camera and applying vision algorithms on their data or using active sensors like RADAR, LIDAR, time of flight (TOF). Active sensors are costly and not suitable for use in academic and research purposes. Since we have advanced computational platforms and optimized vision algorithms, we can make use of low-cost vision sensors to capture images in real time and map the surroundings of an automobile. In this paper, we tried to implement stereo vision on autonomous electric vehicle for obstacle detection and distance estimation. © Springer Nature Singapore Pte Ltd. 2019.
Cite this Research Publication : S. Emani, Dr. Soman K. P., Sajith Variyar V. V., and Adarsh, S., “Obstacle detection and distance estimation for autonomous electric vehicle using stereo vision and DNN”, in Advances in Intelligent Systems and Computing, 2019, vol. 898, pp. 639-648.