Design and implementation of system prediction and traffic conditions visualization in two dimensional map (case study: Bandung city)

Anggraini, Dian; Siswantoko, Wisnu; Henriyan, Diotra; Subiyanti, Devie Pratama; Aziz, Mochamad Vicky Ghani; Prihatmanto, Ary Setijadi

doi:10.1109/icsengt.2016.7849629

Cited by 5 publications

(2 citation statements)

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“…The points where the image brightness changes sharply are grouped together and stored as a set of curved line segments [9,10]. These line segments compose the edges of the region of interest [11]. Edge detection is a fundamental tool in image processing, machine learning, and computer vision, after which the functions are performed on the image [12].…”

Section: Literature Surveymentioning

confidence: 99%

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Autonomous Bot Using Machine Learning and Computer Vision

Karkera

Singh

2021

SN COMPUT. SCI.

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Self-driving vehicles have the potential to revolutionize urban mobility by providing sustainable, safe, and convenient transportability. In recent years several companies have identified automation as their major area of research and also are investing a huge amount of their financial resources in automating vehicles. This is the period of time where autonomous vehicles are very close to being capable of transporting us to destinations without the aid of drivers in the very near future. In the current generation, the main focus is to make vehicles more automated to provide a better driving experience. These vehicles are designed to drive without or with little human assistance by sensing it's the environment. This can be achieved by a combination of sensors and processing the data with the help of computer vision technology and machine learning. The vehicle autonomy needs to be conducted with care, keeping in mind the challenges that can be faced during the process. Recognizing the traffic signals, understanding the signs, identifying the lane markings are some of the basic functions that it needs to perform. After gathering all this information, the next task is to understand the predefined protocols and follow them without any fault. This problem can be solved stepwise using some functions from image processing and computer vision technology such as Haar transform, perspective mapping, perspective transformation, canny edge detection, and histogram equalization. This solution is further enhanced by including machine learning, which improves performance with experience, making it more reliable. It should be noted that, although the vehicles promoted by the companies ensure 80% reliability, we are not yet ready to completely adapt to the idea of automated vehicles. This paper hence focuses on the negative of current ideology and makes it reliable enough to pave a way for its immediate implementation. In this paper, the authors have used a microcontroller and a microprocessor, to Arduino uno is used as a microcontroller and Raspberry pi B+ model is used as the microprocessor. To detect the lanes the authors have used image processing using a library called OpenCV. For detecting the traffic signs the authors have used supervised machine learning technique, to capture the images authors have used raspberry pi version 2 cam, using cascade training to classify the positive images from the negative images.

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Section: Literature Surveymentioning

confidence: 99%

“…Finally a classifier strong enough to classify between is build, which is given by Eqs. (9)(10)(11). Where h t is a classifier, where alpha and beta are used for updation of the weights.…”

Section: Sn Computer Sciencementioning

confidence: 99%