International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
Intelligent Transportation System Based On Machine Learning For Vehicle Perception Harshilsinh Rana1, Dr. Dipesh Makwana2 1Student, Dept. of I.C Engineering, L.D college of Engineering, Gujarat, India
2 Associate Professor, Dept. of I.C Engineering, L.D college of Engineering, Gujarat, India
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Abstract - The term "intelligent transportation systems," or
Parallel to this, cloud and edge computing, as well as other technologies, have helped machine learning (ML) techniques to gain a lot of popularity. A wide range of applications that, like ITS services, demand a variety of requirements have incorporated machine learning (ML). For prediction and precise decision making[3-5] in addition to vehicular cybersecurity, ML technologies like deep learning and reinforcement learning have been particularly helpful tools to discover patterns and underlying structures in massive data sets.[6] The number of research initiatives leveraging ML to enable and optimize ITS tasks has clearly increased over the past 10 years, according to statistics on scientific publications (see Figure 1).
ITS for short, refers to a group of services and applications that include, among others, driverless vehicles, traveler information systems, and management of public transit systems. Future smart cities and urban planning are predicted to heavily rely on ITS, which will improve transportation and transit efficiency, road and traffic safety, as well as energy efficiency and environmental pollution reduction .However, because of its scalability, a wide range of quality-of-service requirements, and the enormous amount of data it will produce, ITS poses a number of difficulties .In this study, we investigate how to make ITS possible using machine learning (ML), a field that has recently attracted a lot of attention. We offer a comprehensive analysis of the current state-of-the-art which covers many fold perspectives grouped into ITS MLdriven supporting tasks, namely perception, prediction and management of how ML technology has been used to a variety of ITS applications and services, like cooperative driving and road hazard warning, and determine future paths for how ITS might more fully utilize and profit from ML technology.
The following figure explains about the number of publications that are done on ITS, which are included in machine learning approaches. Some different functions that are also used in the proposed system as vehicle detection, identification, classification, speed detection. Hence we will use the different algorithms for all the system. This study work intends to discuss a precise and useful method for counting moving vehicles that may be applied in the confusing traffic situation. To find a moving vehicle and get a foreground image, techniques like adaptive background reduction and morphological activities are utilized
Key Words: Intelligent transportation system, Perception tasks, Machine Learning, Road safety, Privacy and security 1.INTRODUCTION The use of information, communication, and sensing technologies in transportation and transit systems is commonly referred to as "intelligent transportation systems," or ITS for short[1]. Future smart cities are anticipated to incorporate ITS as a key element, and it will likely contain a range of services and applications, including autonomous vehicles, traveler information systems, and management of public transit systems, to mention a few[2]. It is anticipated that ITS services will make a substantial contribution to higher energy efficiency, decreased environmental pollution, improved road and traffic safety, and transportation and transit efficiency. ITS applications have been made possible by remarkable developments in sensing, computation, and wireless communication technology; but because of their scalability and a range of quality-of-service requirements, they will present a number of obstacles, additionally to the enormous amounts of data that they will produce.
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Chart -1: Number of publications on ITS, including MLbased approaches, from 2010 to 2020 Now we will have some background information about the computer vision and OpenCV. The goal of the branch of study known as computer vision is to make it possible for machines to analyze and comprehend visual data from the environment. It is a branch of artificial intelligence (AI) concerned with giving machines the ability to see and comprehend the visual world. Object identification and
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