ABSTRACT:
The self-Driving Car research problem requires several sub-topics that need to be discussed more deeply. Such as Deep Learning, Computer Vision, Fusion Sensor, Localization, Control, until Path Planning. All of them are fusion of several fields of study. This paper discusses the results of implementation of lane detection algorithm on toll road Cipularang as parts of self-driving car system. Video image taken using action camera mounted on top of the vehicle, with 1280×720 resolution. Average speed of the vehicle is 100 km per hour. Programming language of image processing using Python 3. Image processing methods are a combination of methods of colour region, line selection, canny edge detection, and Hough transform. The result shows this algorithm needed to be add some method that can changing the parameters during day and night adaptively. Because constant parameters can only be used in the same lighting conditions. Overall the implementation method in Python Language can successfully detect the road lane with accuracy above 90 percent.
INTRODUCTION:
Intelligent Transportation Systems (ITS) have, amongst its main goals, to avoid traffic delays and traffic jams, improve road safety, and reduce power consumption and emissions. Daily users and transportation agencies benefit from information supplied by ITS through improvements on traffic flow monitoring, management and control. The objective of the present work is to apply Computer Vision techniques to estimate vehicles geo referenced position and speed as part of an ITS. Some of the advantages of leaning towards a Computer Vision approach have already been established in prior work (Oliva et al., 2015), in which we developed an algorithm for traffic flow description with no classification capabilities. In the present paper, we focus on the classification issue. In section 2, we describe the Computer Vision techniques and Machine Learning scheme used for the task. In section 3, we show the measurement results obtained from applying these methods to a video obtained from a highway traffic camera. Finally, in section 4, we discuss results and potential improvements to develop in the future.
HARDWARE AND SOFTWARE REQUIREMENT:SOFTWARE REQUIREMENTS:
Operating System : WINDOWS
Simulation Tool : OPENCV PYTHON
Documentation : Ms-Office
HARDWARE REQUIREMENTS:
CPU type : Intel Pentium 4
Clock speed : 3.0 GHz
Ram size : 512 MB
Hard disk capacity : 80 GB
Monitor type : 15 Inch colour monitor
Keyboard type: Internet keyboard
CD -drive type : 52xmax
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