irjece:: regulation, pole placement & tracking (rst) robust controller for automatic highway system

ISSN: 2395-0587 International Research Journal of Electronics & communication Engineering

www.irjece.com Volume 1, Issue 4 of May 2015

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Regulation, Pole Placement & Tracking (RST) Robust Controller for Automatic Highway System

Zain Anwar Ali1, Li Ning2, Iftikhar-uddin H.Farooqui3, Faheem H. Rizvi4, Muhammad Faraz5.

1. College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China. 2. College of Electronic Information Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China.

4. Department of Telecommunication Engineering, Sir Syed University of Engineering& Technology, Karachi, Pakistan 3 &5. Department of Electronic Engineering, Sir Syed University of Engineering& Technology, Karachi, Pakistan.

Abstract The goal of this research is to develop a system which can detect the vehicle, detect the lane and tracking by using Automatic Highway System (AHS) model. For achieving this goal by using a video processing technique as well as image processing to detect the vehicle and lane tracking. The video processing is used to detect the vehicle in a road and maintain a safe distance between vehicles as well as image processing tool Hough transform is used to detect the lanes of the road all the detection mechanism is done by using color threshold checking and detect the vehicle and a lane. Moreover, Regulation, Pole-Placement and Tracking controller is designed for the non-linear dynamics in order to control the vehicle dynamics and decision making. The overall model of the system is designed in Matlab which shows that system successfully works in the required scenario.

Keywords RST Controller, AHS System, Video & Image processing.

1. Introduction China is the 4th biggest country in the Universe with respect to its huge land area about 9327489.90 square km according to World Bank [1]. According to the geographic area and population, Chinas road network in 2002 extended roughly 1.76 million km, with some 25,130 km of expressways and about 27,468 km of other high-grade highways in operation [2]. The daily basis accident ratio rate is about 20.8 due to the human error moderate rate of accidents as compared to other countries. The Smart Road concept and Automatic Highway System (AHS) pays a great benefit to the Economy. The Automatic Highway System is now become an active research area in many years. AHS is an important area to avoid accident and ensure passenger safety. By automating all the vehicles on the road and by making those coordinating with each other seamlessly will improve the passenger comfort in traveling. Another advantage of a system which makes sure that the vehicle is driving at the lawful speed and following all the other law enforced by the state. This will avoid any fines and point on the driver's license other than just improving the road safety [3]. Initially, AHS will probably be deployed and operated on high-priority routes in high-demand major urban and intercity freeway corridors [4]. And an AHS car will look like a normal car. But both facility and road will be outfitted with sophisticated control and communication devices that will essentially put the vehicle in communication with the roadside. The car will "know" what roadway conditions are like. The road will "offer" each vehicle options, navigation, and advisories based on its conditions. While on the AHS facility, the vehicle will be operated under automated control--similar to the autopilot control in aircraft [5]. AHS will be a collection of different systems working together to achieve a collective task of automating a car's drive on any highway [5]. In the long run, these different systems like cruise control, GPS navigation, lane detection system, ABS system, RF based interaction between vehicles and collision avoidance system, etc, to interact with each other and a master system to get guidelines and updates on road traffic in order to route the journey effectively and drive on its own to the destination [6].




The theme of this research is to develop an automated system for highway that can detect the vehicle as well as lane track and regulate the speed of the vehicle. The developed algorithm of this paper is divided into two parts the detection of vehicle which is done by using video processing and other is detection of lane tracking by using image processing. The division of this article is as follows. In section 2 visions of the complete system are defined along with detection of vehicle and detection of lane tracking. Section 3 defines the Automatic, Cruise Controller and in last section covers the discussion and results are as follows.

2. Visioning of Complete System The color camera is equipped with a car that record, the video of every 5 seconds regularly and after recording that video the video reader will read that video and taking all parameters of the video and convert it into video frames and gives images after that convert RGD to grayscale and checking the threshold. If the threshold value is greater than 55 it detects the dark object and neglect it and again goes to the first step. If the threshold of the image is in between 45-55 it means detect light color and detect a vehicle and dynamic model of AHS start working and gives the signal to the vehicle controller and maintain a safe distance between another vehicle. If the threshold value is between 35-44 it will detect lane tracking and after that detection of the edges in that image and by applying Hough transform boundaries of the road is found and it gives the signal to the AHS model and after that car controller will adjust the car in a lane or follow a lane tracking. As shown in figure 1 before Conclusion.

2.1 Detection of Vehicle The vehicle detection is one of the major tasks in this research by using video processing. Object detectors such as face and pedestrian detection are among the well-researched domains. But the vehicle detection algorithm typically uses extracted features and learning algorithm to recognize instances of a vehicle. Vehicle detection has many applications in different areas like traffic controlling and surveillance, etc. The reference object in a scene using feature characteristic and similarity. The "ransac" command can be used to calculate approximately the locality of the object in the test image. Many other approaches like an image segmentation gradient based, derived based and template based approaches may also be used for that.

Taking "avi" format video file as an input from the camera storage. After reading that file and getting information from video. Matlab provides a platform for reading a video and create an object that can take every information about that video clip. Then this video clip can play using implay command. Two gray scale pictures from the 74th frame is taken one is for the image processed and other is for input. The threshold value is used to process known as keyword. The intensity of some pixel lower than the threshold will be discarded and processed into dark or black. 55 is the threshold value, and it is a modest bit above the middling intensity of the dark color object. For processing the image through most of the dark objects removed, but there is some leftover of the dark object.

The lane marks are not touching at all because their pixel values are above the threshold level. Any object size is smaller than the defined size, length and width will be discarded. Not only is the small objects also the dark objects and lane marks is also obsolete due to the width smaller than the "disk". The disk is created by the function "steel" and its function is sizing the structure of elements such as the shape, square and lines of the ball, etc. "imopen" command open the morphological binary image or grayscale with the "SE" element structure. The structure of the element "SE" should be single and opposite to an array of an object. The whole procedure is used to detect multiple cars in a road. After that maintain a safe distance between them.




Figure No: 02 Detection of Vehicle

Figure No: 03Converting into Binary image

2.2 Detection of Lane Tracking Firstly, we find out the edges of the image using edge detection algorithm. Many techniques are there for edge detection like Sobel, Canny, Prewitt or Roberts. But we use canny to convert an image output into binary, it gives the matrix of Boolean values equivalent to the edges. After that step using Hough transform to detect the lines.

= x * cos () + y * sin() (1)

The equation (1) help to map points in the Cartesian picture co-ordinate. Where "" and " " represent the rows and column respectively. The Hough transform output is used for line segment adjustment to contact with the image boundary outlines and after that calculate the Hough lines. The Cartesian coordinate finds by the Hough line in image or video processing by locating the collision between the lines that is characterized by the parameters "" and " " and reference image boundaries. The image is reconstructed by computing endpoints to draw the polygon. The detection of lines from the sides of the polygon. The original video or image is overlaid and simulate the AHS model to verify the detection and tracking of the vehicle.

Figure No: 04 Image of China Highway




Figure No: 05 Gray Scale Conversion

Figure No: 06 Binary Image

Figure No: 07 Hough Transform

3. Automatic Cruise Controller In this part of our paper convinced that the vehicle maintains a secure space from the master vehicle. This could

be done by pursuing the velocity of the leading vehicle and the maximum speed of the road which we were taken from the highway is 120km/hr. The dynamics of master and slave vehicle is taken from [7].

x = [, v,f] (2) The "" sign shows the space between master and slave vehicle the velocity of the vehicle is defined by "v" the

driving and force is defined by "f". . Equation (3) defines the spacing between vehicle 1 & 2 and equation (4) defines the spacing between vehicle 2 & 3. Same procedure apply as follows.




= x x (3) = x x (4)

The positive sign shows the force between vehicles and the negative sign shows the braking between vehicles.

The dynamics of the system in longitudinal displacement shown as below.

= v v (5) 1 = v v (6) v1 =

(Av d + f) (7)

v2 =

(Av d + f) (8) f1 = (f + u) (9) f2 =

(f + u) (10)

The above equations shows the orientation of master vehicle called as vehicle "one" along with secondary vehicle

called as vehicle "two". After that secondary vehicle act as master vehicle, then secondary vehicle called as a vehicle "three" follow the same placement and distance. Taking mass as constant about 1500kg for all vehicles. The engine time constant is 0.15 seconds.

Step signal is applied to the input of the system, it defines the system is velocity dependent. A reasonable spacing

of the vehicle is defined by using master slave configuration and it spacing between 18kph to 20kph. The major system implementations are done by using simulink Matlab. The robust RST controller is used as a main controller, which can help us to run the system smoothly. The RST controller is used for linear system dynamics, but also used for non-linear dynamics. The responses of the system show stability.

Figure No: 08 Simulink Model of System




Figure No: 09 Master & Secondary Vehicle Tracking w.r.t Velocity & Time.

Figure No: 01 Flow Diagram of Complete System

Video Recording

Video Parameter Reading

Video Reading

Frame in to Image

RGB Convert to Grayscale

Threshold Color > 55

Detection of dark object

Threshold > = 45-55

Threshold > = 35-44

Threshold > = 0-34

Vehicle Detect

Dynamic Model AHS

Vehicle Controller

Lane Tracking

Edge Detection

Hough Transform

Boundaries Found




CONCLUSION In this article, a video processing based vehicle spacing or vehicle distance maintain technique is presented along

with image processing is also used for detecting the lanes and tracking of the system by using Hough transform canny technique is used to detect lanes of the road. The proposed system works identically in a practical scenario and vehicle follow master slave mechanism successfully. Where robust RST controller also helps to remove steady state errors in the system.

REFERENCES

[1] Trading Economics, Table 1: " china/land-area-sq-km-wb-data.html , 2013." [2] World Bank, Data base, worldbank.org/transport/transportresults/regions/eap/eap-china-output.pdf. [3] Fahad A. Siddiqui, Samreen Amir, Muhammad Asif, and Zain Anwar Ali, LANE TRACKING AND

AUTONOMOUS CRUISE CONTROL FOR AUTOMATIC HIGHWAY SYSTEM, IEEE 19th Conference on Signal Processing and Communications Applications (SIU), 2011.

[4] "Request for Applications Number DTFH61-94-X-0001 to Establish a National Automated Highway System Consortium," Federal Highway Administration, Washington, D.C., December 1993.

[5] Horowitz, R. and Varaya, P., "Control Design of Automated Highway System", Proc of IEEE , vol 88, issue 7, July 2000

[6] Ashley, S. "Smart Cars and Smart Highways", Magzine: Mechanical Engineering, The American Society of Mechanical Engineers, May 1998

irjece:: regulation, pole placement & tracking (rst) robust controller for automatic highway system

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