Real Time Traffic Signs and Obstacle Detection in Self-Driving Car
Konda Nandini1, V. Naveen kumar2, Y. Padma Sai3

1Konda Nandini, Department of ECE, Vallurupalli Nageshwara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India.
2V. Naveen Kumar, Department of ECE, Vallurupalli Nageshwara Rao Vignana Jyothi Institute of Engineering and Technology , Hyderabad, India.
3Dr. Y. Padma Sai, Department of ECE, Vallurupalli Nageshwara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on April 01, 2020. | Manuscript published on April 10, 2020. | PP: 253-256 | Volume-9 Issue-6, April 2020. | Retrieval Number: D1936029420/2020©BEIESP | DOI: 10.35940/ijitee.D1936.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The motivation behind this research work is to improve car safety and efficiency. The concept of self driving cars is heard from years, it has not come into usage in many countries because of the lack of complete intelligence in the vehicle. Some of the modern vehicles provide partially automated specifications such as keeping the car within its lane, speed controls or emergency braking. According to statistics most of the accidents occur due to lack of instant response to traffic signs and obstacles ahead. In case of self driving car this problem can be addressed by detecting the traffic signals using high end camera. Real time traffic sign detection model accomplishes its objective by identifying the traffic signals and obstacles. A high end camera is used to capture the image, raspberry pi 3 is used as hardware and open computer vision library is used to process the image and identify the patterns in the image to properly detect the signals. Ultra sonic distance sensor is used to identify the obstacles. 
Keywords: Raspberry Pi, Median Filter, Hough Circles, k Means Clustering, Edge Detection.
Scope of the Article: Network Traffic Characterization and Measurements