Review on Automatic Vehicle Driving and Parking using Image Processing and Sensors
Author(s):
Amit U.Godge, Dr. Ulhas B. Shinde
Keywords:
Automation, Tracking, Automatic Vehicle Driving, Lane Detection
Abstract
Today automobile sector is most developing area and it is became important part of human life. With the rapid increase of cars the need to find available parking space in the most efficient manner, to avoid traffic congestion in a parking area, is becoming a necessity in car park management. Current car park management is dependent on either human personnel keeping track of the available car park spaces or a sensor based system that monitors the availability of each car park space or the overall number of available car park spaces. In both situations, the information available was only the total number of car park spaces available and not the actual location available. In addition, the installation and maintenance cost of a sensor based system is dependent on the number of sensors used in a car park. Automatic Vehicle Driving is a generic term used to address a technique aimed at automating entirely or in part one or more driving tasks. The automation of these tasks carries a large number of benefits, such as: a higher exploitation of the road network, lower fuel and energy consumption, and of course improved safety conditions compared to the current scenario. The tasks that automatically driven vehicles are able to perform include the possibility to follow the road and keep within the right lane, maintaining a safe distance between vehicles, regulating the vehicle's speed according to traffic conditions and road characteristics, moving across lanes in order to overtake vehicles and avoid obstacles, helping to find the correct and shortest route to a destination, and the movement and parking within urban environments. Since the potential of soft-computing for driver assistance systems has been recognized, much effort has been spent in the development of appropriate techniques for robust lane detection, object classification, tracking, and representation of task relevant objects.
Article Details
Unique Paper ID: 157689

Publication Volume & Issue: Volume 9, Issue 7

Page(s): 805 - 810
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