UWE Bristol Engineering showcase 2015

Artem Kalus
BEng Motorsport Engineering
Project Supervisor
Gary Atkinson
Terrain Detection in a field of automotive industry
Introduction
In our day and age, car-manufacturing companies
are using most modern technologies from almost
all possible industries. The modern technologies
made possible invention and implementation of
hundreds systems that improve vehicle dynamics,
stability, safety and comfort. However, existing
systems could be optimized and perform more
efficiently, if the terrain type is know. For instance,
ABS (anti-lock brakes system) is a system that
optimizes braking pressure on each individual
wheel to minimize braking distance when
necessary. The use of the ABS can be optimized for
a specific terrain type. In case of driving a vehicle
on sand in a dessert it is more efficient to disable
the ABS system, however, driving on snow is much
safer with use of this system. By this reason, most
car manufacturers already offer manual driving
conditions selection (The modern commercial
terrain detection control panel is demonstrated in
Figure 1), nevertheless, it could be an
improvement, if the user input would not be
required and vehicles could detect terrain type
autonomously by only using a digital camera.
Terrain detection system is an electronic system,
which may consist of sensors and control unit. The
system feature is to analyse terrain a vehicle is
driven on by using sensors and produce a relevant
output that could be used by other vehicle onboard
electronics to improve driving experience. In
this project several automated terrain detection
programmes based on use of a digital camera and
image processing were developed.
Methodology
In this project five different methodologies were
tested. All of the tested methods results were
included in the Table 1. A cluster or pre-defined
regions classification were used to define closest
terrain type from the learning library to the tested
image. Figure 3 demonstrates a cluster analysis of
a two-dimensional method. In order to test every
method twenty photos were taken, 5 of every
terrain type. In total 4 terrain types were tested:
sand, snow, grass, and tarmac.
Chosen approach
From all the tested methods the best results were
demonstrated by the Method 3 Regional
Restriction STD. The STD (Standard Deviation)
Regional Restriction method is based on
implementing the standard deviation function as a
visual terrain analytic tool. The alteration and the
closely related standard deviation are measures of
how spread out a distribution is. The minimum
and maximum STD values of each terrain type are
obtained by the learning programme and used to
train the terrain detection programme. After that
the terrain detection programme is trained with
the library produced by the learning programme
and defines terrain regions on a one-dimensional
graph in terms of STD values. Figure 2 depicts a
tested sand image located in the sand terrain
region. Figure 4 shows the sand terrain tested
image used in Figure 1. The terrain detection
programme than defines the region where the
input image is inherent.
Chosen approach data analysis
The Method 2 Distance to Average STD showed
100% correct detection of the terrain type and this
made the method most precise out all presented
herein. However, the method was based on using
minimum and maximum STD values and the
following makes the method vulnerable to false
detections in case of continuous use of the system
without any additional runs of the training
programme. Nevertheless, the method was the
most efficient out of all tested one-dimensional
classification approaches.
Figure 1 Range Rover Terrain Response Control
Panel (Jaguar Land Rover North America, n.d.)
Table 1 False detection rate
Figure 2 STD Terrain
Ranges and Input Image
Position
Figure 4 Sand Terrain
Photo used in figure 1
Figure 3 Vector Distance
from Input Image to
Terrains Averages
Figure 5 Sample grass
terrain image
Project summary
In this project an investigation has been
accomplished to examine modern terrain
detection systems, their main advantages and
disadvantages and come up with another
desirably better terrain detection system. The
developed low-cost terrain detection system
was planned to be utilized as an after-market
add-on, and through good production design
could be sold on Amazon and other similar
retail outlets.
Project Objectives
• Existing terrain detection systems related
literature review
• Learn to use Matlab as image analysing
tool
• Take photos of different terrain types
• Develop and test algorithms to extract
image characteristic data
• Develop terrain classification procedure
based on image extracted data
Project Conclusion
Several terrain detection programmes were
developed in Matlab environment and tested
on photos of different terrain types.
The Method 2 was the most efficient out of
all tested single-dimensional and multidimensional
classification approaches;
nevertheless, further work on the system is
suggested in order to improve its reliability
and develop the software into a complete
commercially utilized system.