Autonomous Vehicles - KPIT

Autonomous
Intelligent Vehicles
Authors : Hong Cheng
BOOK REVIEW
Self-driving cars is an emerging field. Companies like Google, Nissan,
GM and many other are showing their interest in autonomous cars. Dr.
Hong Cheng is considered as a pioneer in this field. He is currently
working as a professor in School of Automation Engineering, and also
a founding director of the Pattern Recognition and Machine
Intelligence Lab at the University of Electronic Science and
Technology of China. His areas of interest include multi-signal
processing, human computer interaction, robotics, computer vision
and machine learning robotics. In his book “Autonomous Intelligent
Vehicles: Theory, Algorithms, and Implementation (Advances in
Computer Vision and Pattern Recognition)”, Prof. Cheng summarizes
his research on Intelligent Vehicles. This book is an essential
reference for researchers in the field of autonomous vehicles. The
broad coverage of all aspects of this research will also appeal to
researchers, professionals and graduate students who are interested
in signal-image processing, pattern recognition, object/obstacle
detection and recognition, vehicle motion control, Intelligent
Transportation Systems and more specifically state-of-the-art of
intelligent vehicles. The field of intelligent vehicles includes a wide
range of technologies ranging from vehicle dynamics to information,
computer vision, hardware, ergonomics and human factors.
Author has written this book with three goals. First goal is to create an
updated reference book of intelligent vehicles and relative
technologies. Second is, presenting object/obstacle detection and
recognition, and introducing vehicle lateral and longitudinal control
algorithms. As a final goal, Prof. Cheng emphasizes on high-level
concepts, and also provides the low-level details of implementation at
the same time. He tries to link theory (algorithms, models, ideas) with
practice (implementations, systems and applied research). This book
is divided in to four parts, as presented below.
The first part presents the framework of autonomous vehicles from A
to Z. Specifically, addressing intelligent vehicles as a set of intelligent
agents integrated with multi-sensor fusion based on distinctive
modules. Author also gives us an insight of different state-of-the-art of
autonomous vehicles, which took part in either the Grand Challenges
or the Urban Challenge supported by the DARPA in the USA. List of
autonomous vehicles discussed by the author include vehicles from
Carnegie Mellon University (Boss), Stanford University (Junior),
Virginia Polytechnic Institute and State University (Odin),
Massachusetts Institute of Technology (Talos), Cornell University
(Skynet), University of Pennsylvania and Lehigh University (Little
Ben), and Oshkosh Truck Corporation (TerraMax). Among these
TerraMax travels slowly because of its big size (27 feet long, 8 feet wide,
8 feet high and weighs around 30000 pounds) making it different from
others.
Second part of the book highlights the importance of environment
perception and modelling. Author describes the benefits of computer
vision systems for road detection and tracking including multiple-sensor
based multiple-object tracking. To this end, author analytically describes
the lane detection methods proposed by the author and his research
team, including lane model, particle filtering, dynamic system model and
algorithms. This part ends with explanation of vehicle detection approach
operating in two phases (i) hypothesis generation (ii) validation. In first
phase, determination of Region of Interest (ROI) in an image is done
using a vanishing point for the road. By analysing the vertical and
horizontal edges in an image, vehicle hypothesis lists for near, middle
and far ROI are generated. Combining these three lists, a hypothesis list
for whole image is attained. In validation phase, support vector machines
and Gabor features are used. Author also proposed an interactive road
situation analysis framework along with its implementation, namely the
multiple-sensor multi-object detection and tracking approach.
Third part of the book highlights Vehicle Localization and Navigation. For
vehicles with autonomous navigation determining their local and global
positions within the environment they are in (which is unstable, dynamic
and extremely unpredictable), is very important and a challenging issue.
In this part of the book, author proposes a method to enhance situation
awareness by dynamically providing a global view of surrounding for
drivers. Rather than using a catadioptric camera, which is used in most of
the existing intelligent vehicles, an omnidirectional vision system
(consisting of multiple cameras) at the top of a vehicle is used to capture
the surrounding of a vehicle. Author explains that this system would be
helpful to obtain high quality images of surroundings.
Finally in fourth part of the book, the author discusses Advanced Vehicle
Motion Control, introducing vehicle lateral and longitudinal motion
control. Author also explains about the proposed Mixed Lateral Control
Strategy in this part. Important issues such as relationship between
motor pulses and the front wheel lean angle for lateral control and first
order lag systems in longitudinal control are covered.
This book serves as a decent handbook for engineers to be informed on
cutting edge technology in the field. It also serves as an extremely
valuable aid to graduate students, who are interested in intelligent
vehicles. It could be a good reference book for an experienced
researcher, who wants to be introduced to specific issues in the field of
intelligent vehicles.
Naveen Boggarapu
Areas of Interest
Embedded Systems,
Linux,
Device Drivers
TechTalk@KPIT, Volume 6, Issue 4, 2013 545