VALEO EX. 1009
`
`

`
`Automatic Vehic
`Guidance:
`The Experience of the A R C 0
`Aestsnomous Vehicle
`
`

`
`Automatic Vehic
`Guidance:
`The Experience sf the ARC0
`Autonomous Vehic
`
`Al berto Broggi
`Massi mo Bertozzi
`Alessandra Fasciol i
`Gianni Conte
`University of Parma, Italy
`
`World Scientific
`b Singapore e New JerseyeLondon ~ H o n g Kong
`
`

`
`Published by
`
`World Scientific Publishing Co. Pte. Ltd.
`P 0 Box 128, Farrer Road, Singapore 912805
`USA ofice: Suite lB, 1060 Main Street, River Edge, NJ 07661
`UK once: 57 Shelton Street, Covent Garden, London WC2H 9HE
`
`British Library Cataloguing-in-Publication Data
`A catalogue record for this book is available from the British Library.
`
`AUTOMATIC VEHICLE GUIDANCE:
`The Experience of the ARGO Autonomous Vehicle
`
`Copyright O 1999 by World Scientific Publishing Co. Pte. Ltd.
`All rights reserved. This book, orparts thereox may not be reproduced in any form or by any means,
`electronic or mechanical, includingphotocopying, recording or any information storage and retrieval
`system now known or to be invented, without written permissionfrom the Publisher.
`
`For photocopying of material in this volume, please pay a copying fee through the Copyright
`Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to
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`
`ISBN 981-02-3720-0
`
`This book is printed on acid-free paper.
`
`Printed in Singapore by UtePrint
`
`

`
`Chapter 2
`Intelligent Vehicles
`and Machine Vision
`
`2.1 Evolution of Intelligent Transportation Systems
`
`After completion of the first explorative phase, that started around the
`beginning of the eighties, the field of Intelligent Transportation Systems
`(ITS) is now entering its second phase characterized by a maturity in its
`approaches and by new technological possibilities which allow the devel-
`opment of the first test products. Nevertheless, the evolution towards the
`third stage, i.e. the massive production and integration of ITS technology
`on commercial vehicles, is expected to begin not before a couple of decades.
`The interest in ITS technologies and their related issues was born about
`20 years ago, when the problem of people and goods mobility began to arise:
`the saturation of the most common way to increase mobility, namely the
`extension of the road network, focused the interest of governments and
`research institutions towards new alternative solutions.
`The main target of this first phase was to create a technical substrate to
`be used in the following prototyping stages, namely a formal basis covering
`different subjects. For this reason, automatic vehicle driving, intelligent
`route planning, and other extremely high-level functionalities were selected
`as main goals.
`Government institutions activated the initial explorative phase by means
`of different projects worldwide. The first results were a deep analysis of the
`problem and the development of a feasibility study to understand the re-
`quirements and possible effects of the application of ITS technology. One
`of the main advantages of these activities was the possibility to have a
`large number of different and complementary research units -with differ-
`
`

`
`I
`I
`1
`1
`
`I
`
`1
`
`I
`
`I
`
`Machine Vision
`
`17
`
`tions can be found in CMOS sensors, such as the possibility of dealing with
`pixels independently as in traditional memories. Another key advantage of
`CMOS-based sensors is that their integration on the processing chip seems
`t o be straightforward.
`As a result, not only did this advanced technology promote improved
`hardware devices, but also triggered off renewed interest in the techniques
`for the processing of iconic information, generally addressed by the field
`of Artificial Intelligence, who deals with image interpretation and -more
`generally- with perception, when the fusion of data coming from other
`sensors is also integrated.
`Nonetheless, when designing a vision system for automotive applica-
`tions, some important characteristics must be carefully considered.
`
`c ITS systems require faster processing than other applications, since
`the vehicle speed is proportional to the processing rate. The main
`problem that has to be faced when real-time imaging is concerned
`and which is intrinsic to the processing of images, is the large
`amount of data -and therefore computation- involved. As a result,
`specific computer architectures and processing techniques must be
`devised in order to achieve real-time performance. ru'evertheless,
`since the success of ITS apparata is tightly related to their cost,
`the computing engines cannot be based on expensive processors.
`Therefore, either off-the-shelf components or ad-hoc dedicated low-
`cost solutions must be considered.
`e Furthermore, in the automotive field no assumptions can be made
`on key parameters, for example the illumination or contrast of the
`scene, which are directly measured by the vision sensor. Hence,
`the subsequent processings must be robust enough to tolerate these
`changes and both adapt to different environmental conditions, such
`as sun (high brightness and contrast due t o shadows), rain (ex-
`tremely high contrast due to reflections), fog (low contrast) and t o
`their dynamic changes, such as transitions from sun to shadows or
`vice versa (the entrance or exit from a tunnel).
`
`Moreover, other key issues, such as robustness t o camera movements
`and drifts in its calibration must be handled as well.
`Nevertheless, although being extremely complex and highly demanding,
`thanks to the great deal of information that it can deliver, computer vision is
`
`

`
`I
`
`I
`
`1
`
`I
`
`18
`
`Intelligent Vehicles and Machine Vision
`
`a powerful means for sensing the environment and has been widely employed
`to deal with a large number of tasks in the automotive field. These tasks
`include: Road Following (which involves Lane Detection [85] and Obstacle
`Detection), Platooning (the automatic following of a manually driven vehicle
`
`To accomplish these tasks different quantities must be measured and/or
`patterns recognized before the closing of the control loop. These are the fol-
`lowing: the relative position of the vehicle with respect to the lane and the
`check for obstacles on the path or for known road signs [I] for Road Follow-
`ing; the recognition of specific vehicle's characteristics and the computation
`of the time-to-impact for Platooning; the sensing of multiple lanes [I561 as
`well as obstacle detection for Vehicle Overtaking and Collision Avoidance;
`the distance amongst parked vehicles for Automatic Parking; the position
`and movements of the driver's eyes and head for Driver Status Monitoring.
`Machine Vision systems are composed of two modules: the first, used
`for data acquisition, and the second used to process the acquired data.
`
`Image acquisition
`The acguisitlon of significant data is a basic requirement. Many
`different parameters must be evaluated for the design and choice
`of an image acquisition device. First of all, the spatial resolution
`as well as the depth (number of bitlpixel) of the images must be
`selected; this also includes the selection of color vs monochrome im-
`ages. Other parameters tightly coupled with the algorithms regard
`the choice of monocular vs binocular (stereo) vision and the sensors'
`angle of view. Some systems adopt a multi-camera approach, by
`using more than one camera with different viewing angles (e.g. fish
`eye or zoom).
`Apart from these, other parameters -intrinsic to the sensor- must
`be considered. Although the frame rate is generally fixed for CCD-
`based devices (25 or 30 Hz), the dynamics of the sensor is of basic
`importance: conventional cameras allow an intensity contrast of
`500:l within the same image frame, whilst most ITS applications
`require a 10,000:l dynamic range for each frame and 100,000:l for
`a short image sequence. Different approaches have been studied
`to meet this requirement, ranging from the design of CMOS-based
`cameras with a loga~ithmically compressed dynamic [134], to the
`
`

`
`Machine Vzsion
`
`19
`
`interpolation and superimposition regarding values of two subse-
`quent images taken from the same camera [103].
`Furthermore, in the automotive field some additional problems have
`to be faced. The acquisition system is installed on a moving ve-
`hicle; therefore image stabilization becomes a key problem. Some
`expensive devices allow to perform images stabilization directly at
`sensor level; otherwise, a different solution is to perform it via soft-
`ware [52,104,160], trading the complexity of the sensor with a more
`powerful computing system.
`Moreover, the acquisition system must be robust enough not to
`interfere with the on-board electronics, especially with telecommu-
`nication apparata.
`Finally, for an acquisition system to be of use it must be properly
`calibrated in order to create the mapping between image pixels and
`the 3D world.
`Image processing systems
`Image processing hardware must meet two basic requirements: it
`has t o be powerful enough to support real-time processing, and
`compact and inexpensive to allow a massive integration on com-
`mercial vehicles.
`As mentioned, in the early years of ITS applications a great deal
`of custom solutions were proposed, based on ad-hoc, special-purpose
`hardware. This was motivated by the need for computational power.
`The most significant solutions were based on SIMD array proces-
`sors, composed of a large number of extremely simple processors
`working simultaneously on image windows [23].
`-4fter this first stage, when commercial hardware began to deliver
`sufficiently high computational power at a low cost, general-purpose
`solutions, based on off-the-shelf components, became competitive.
`In this phase the first NIIMD systems [151], composed of a rather
`small number of powerful, independent processors, were built and
`installed on prototype vehicles.
`Current trends, however, are moving towards a mixed architecture,
`in which a powerful processor is aided by specific hardware such as
`boards and chips implementing optical flow computation, pattern-
`matching, convolution, and morphological filters. Moreover, some
`SIMD capabilities are now being transferred into the instruction
`set of the last-generation CPUs, which has been tailored to ex-
`
`

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`book Automatic Vehicle Guidance (ISBN 978-981'°2‘372°'2)
`h t ti
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`' 1' and Gianni Conte was
`This 55 to C0“ "mt a
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`by Alberto Broggl, Massxnio Be1'ttc)>1.7rz]1l,.:i1eés)agt;‘2£t§1:::02l2 April 1999.
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`“Automatic Vehicle Guidance: The Experience of the ARGO Automous
`Vehicle” by Broggi A., Bertozzi M., Fascioli A., and Conte G., ISBN #
`9810237200 (1999).
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`AUTOMATIC VEHICLE GUIDANCE:THE
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