Vision-Based Autonomous Vehicle Driving Control System
In recent years, extensive research has been carried out on autonomous vehicle system. A completely autonomous vehicle is one in which a computer performs all the tasks that the human driver normally would. However, this study only focuses on driving control system that based on vision sensor. Th...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English English |
| Published: |
2005
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/6052/1/FK_2005_45.pdf http://psasir.upm.edu.my/id/eprint/6052/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In recent years, extensive research has been carried out on autonomous vehicle
system. A completely autonomous vehicle is one in which a computer performs all
the tasks that the human driver normally would. However, this study only focuses on
driving control system that based on vision sensor. Therefore, this study presents a
simulation system with Graphical User Interface (GUI) to simulate and analyse the
driving control for autonomous vehicle that based on video taken fiom the vehicle
during driving on highway, by using MATLAB programming. The GUI gives easy
access to analyse video, image and vehicle dynamics. Once the GUI application for
simulation is launched, user can enter input parameters value (number of frames,
canny edge detection value, vehicle speed, and braking time) in text control to
simulate and analyse video images and vehicle driving control.
In this study, there are four subsystems in the system development process. The first
subsystem is sensor. This study was used a single Grandvision Mini Digital Video as
sensor. This video camera provides the information of Selangor's highway
environment by recording highway scene in front of the vehicle during driving Then, the recorded video is process in second subsystem or named as imageprocessing
subsystem. In this subsystem, image-capturing techniques capture the
video images frame by frame. After that, lane detection process extracts the
information about vehicle position with respect to the highway lane. The results are
angle between the road tangent and orientation of the vehicle at some look-ahead
distance. Driving controller in the controller subsystem that is the third subsystem
used the resulted angle from lane detection process along with vehicle dynamics
parameters to determine the vehicledriving angle and vehicle dynamics
performance. In this study, designing a vehicle controller requires a model of
vehicle's behaviour whether dynamics or kinematics. Therefore, in vehicle
subsystem that is the fourth subsystem, this study used vehicle's dynamics behaviour
as the vehicle model. The model has six degrees of fieedom (DOF) and several
factors such as the vehicle weight, centre of gravity, and cornering stifkess were
taken into account of dynamics modelling.
The important contribution of this study is the development of vehicle lane detection
and tracking algorithm based on colour cue segmentation, Canny edge detection and
Hough transform. The algorithm gave good result in detecting straight and smooth
curvature lane on highway even when the lane was afTected by shadow. In this study,
all the methods have been tested on video data and the experimental results have
demonstrated a fast and robust system. |
|---|