Overview Paper
Vision-based intelligent vehicles: State of the art and perspectives

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Abstract

Recently, a large emphasis has been devoted to Automatic Vehicle Guidance since the automation of driving tasks carries a large number of benefits, such as the optimization of the use of transport infrastructures, the improvement of mobility, the minimization of risks, travel time, and energy consumption.

This paper surveys the most common approaches to the challenging task of Autonomous Road Following reviewing the most promising experimental solutions and prototypes developed worldwide using AI techniques to perceive the environmental situation by means of artificial vision.

The most interesting results and trends in this field as well as the perspectives on the evolution of intelligent vehicles in the next decades are also sketched out.

Introduction

In the last decades in the field of transportation systems a large emphasis has been given to issues such as improving safety conditions, optimizing the exploitation of transport networks, reduce energy consumption and preserving the environment from pollution. The endeavors in solving these problems have triggered the interest towards a new field of research and application, automatic vehicle driving, in which new techniques are investigated for the entire or partial automation of driving tasks. These tasks include following the road and keeping within the correct lane, maintaining a safe distance among vehicles, regulating the vehicle’s speed according to traffic conditions and road characteristics, moving across lanes in order to overtake vehicles and avoid obstacles, finding the shortest route to a destination, and moving and parking within urban environments.

The interest in intelligent transportation systems (ITS) technologies was born about 20 years ago, when the problem of people and goods mobility began to arise, fostering the search for new alternative solutions. Automatic vehicle driving, intelligent route planning, and other extremely high-level functionalities were selected as main goals. Governmental institutions activated this initial explorative phase by means of various projects worldwide, involving a large number of research units who worked in a cooperative way producing several prototypes and possible solutions, all based on rather different approaches.

In Europe the PROMETHEUS project (PROgraM for a European Traffic with Highest Efficiency and Unprecedented Safety) started this explorative stage in 1986. The project involved more than 13 vehicle manufacturers and several research units from governments and universities of 19 European countries. Within this framework, a number of different approaches regarding ITS were conceived, implemented, and demonstrated.

In the United States a great deal of initiatives were launched to deal with the mobility problem, involving many universities, research centers, and automobile companies. After this pilot phase, in 1995 the US government established the National Automated Highway System Consortium (NAHSC) [2].

Also in Japan, where the mobility problem is much more intense and evident, some vehicle prototypes were developed within the framework of different projects. Similarly to what happened in the US, in 1996 the Advanced Cruise-Assist Highway System Research Association (AHSRA) was established amongst a large number of automobile industries and research centers [31], which developed different approaches to the problem of Automatic Vehicle Guidance.

The main results of this first stage were a deep analysis of the problem and the development of a feasibility study to understand the requirements and possible effects of the application of ITS technology.

The field of ITS is now entering its second phase characterized by a maturity in its approaches and by new technological possibilities which allow the development of the first experimental products. A number of prototypes of intelligent vehicles have been designed, implemented, and tested on the road. The design of these prototypes was preceded by the analysis of solutions deriving from similar and close fields of research, and exploded with a great flourishing of new ideas, innovative approaches, and novel ad hoc solutions. Robotics, artificial intelligence, computer science, computer architectures, telecommunications, control and automation, signal processing are just some of the principal research areas from which the main ideas and solutions were first derived. Initially, underlying technological devices — such as head-up displays, infrared cameras, radars, sonars — derived from expensive military applications, but, thanks to the increased interest in these applications and to the progress of industrial production, today’s technology offers sensors, processing systems, and output devices at very competitive prices. In order to test a wide spectrum of diverse approaches, these prototypes of automatic vehicles are equipped with a large number of different sensors and computing engines.

Section 2 of this paper describes the motivations which underlie the development of vision-based intelligent vehicles, and illustrates their requirements and peculiarities. Section 3 surveys the most common approaches to autonomous Road Following developed worldwide, while Section 4 ends the paper outlining our perspectives in the evolution of intelligent vehicles.

Section snippets

Improving vehicles or infrastructures?

Automatic driving functionalities can be achieved acting on infrastructures and vehicles. Depending on the specific application, either choice possesses advantages and drawbacks. Enhancing road infrastructure may yield benefits to those kind of transportation which are based on repetitive and prescheduled routes, such as public transportation and industrial robotics. On the other hand, it requires a complex and extensive organization and maintenance which can become cumbersome and extremely

Automatic Road Following: An overview of the approaches

Among the complex and challenging tasks that received most attention in Automatic Vehicle Guidance is Road Following. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle’s heading direction), and obstacle detection (which is mainly based on localizing possible obstacles on the vehicle’s path).

In this section, a survey on the most common approaches to Road Following is

Conclusions and perspectives on intelligent vehicles

Some common considerations can be drawn from the three experiments described: all of them succeeded in reaching an extremely high percentage of automatic driving, but although in the first experiments some special-purpose hardware was needed, more recent experiments benefited from the latest technological advances and used only commercial hardware.

Anyway, though for this kind of application computing power do not seem to be a problem any more, still some problems remain regarding data

Massimo Bertozzi received the Dr. Eng. (Master) degree in Electronic Engineering (1994) from the Università di Parma, Italy, discussing a Master’s Thesis about the implementation of Simulation of Petri Nets on the CM-2 Massive Parallel Architecture. From 1994 to 1997 he was a Ph.D. student in Information Technology at the Dipartimento di Ingegneria dell’Informazione, Università di Parma, where he chaired the local IEEE student branch. During this period his research interests focused mainly on

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      In 1995, the ‘Munich to Odense UBM Test’ was performed; the VaMP AV developed at the Universität der Bundeswehr München drove from Munich (Germany) to Odense (Denmark) on a long-distance road test (Bertozzi et al., 2000; Maurer et al., 1996). At the same time, researchers from Carnegie Mellon University performed the ‘No Hands Across America’ test, with the NavLab 5 AV, driving from Pittsburgh, Pennsylvania, to San Diego, California (Bertozzi et al., 2000). In 1998, the ‘ARGO Project’ was performed to test AV driving on rolling hills and in unpredictable weather conditions (Bertozzi et al., 1998, 2000).

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    Massimo Bertozzi received the Dr. Eng. (Master) degree in Electronic Engineering (1994) from the Università di Parma, Italy, discussing a Master’s Thesis about the implementation of Simulation of Petri Nets on the CM-2 Massive Parallel Architecture. From 1994 to 1997 he was a Ph.D. student in Information Technology at the Dipartimento di Ingegneria dell’Informazione, Università di Parma, where he chaired the local IEEE student branch. During this period his research interests focused mainly on the application of image processing to real-time systems and to vehicle guidance, the optimization of machine code at assembly level, and parallel and distributed computing. After obtaining the Ph.D. degree, in 1997, he has been holding a permanent position at the same department. He is a member of IEEE and IAPR.

    Alberto Broggi received both the Dr. Eng. (Master) degree in Electronic Engineering (1990) and Ph.D. degree in Information Technology (1994) from the Università di Parma, Italy. From 1994 to 1998 he was a Full Researcher at the Dipartimento di Ingegneria dell’Informazione, Università di Parma, Italy. Since 1998 he is Associate Professor of Artificial Intelligence at the Dipartimento di Informatica e Sistemistica, Università di Pavia, Italy. His research interests include real-time computer vision approaches for the navigation of unmanned vehicles, and the development of low-cost computer systems to be used on autonomous agents. He is the coordinator of the ARGO project, with the aim of designing, developing and testing the ARGO autonomous prototype vehicle, equipped with special active safety features and enhanced driving capabilities. Professor Broggi is the author of more than 100 refereed publications in international journals, book chapters, and conference proceedings. Actively involved in the organization of scientific events, Professor Broggi is on the Editorial Board and Program Committee of many international journals and conferences and has been invited to act as Guest-Editor of journals and magazines theme issues on topics related to Intelligent Vehicles, Computer Vision Application, and Computer Architectures for Real-Time Image Processing. Professor Broggi is the Newsletter Editor and member of the Conference and Publication Committees of the IEEE Intelligent Transportation Systems Council and will be the Program Chair of the next IEEE Intelligent Vehicles Symposium, Detroit, MI, 2000.

    Alessandra Fascioli received the Dr. Eng. (Master) degree in Electronic Engineering (1996) from the Università di Parma, Italy, discussing a Master Thesis on stereo vision-based obstacle localization in automotive environments. In January 2000 she received the Ph.D. degree in Information Technology at the Dipartimento di Ingegneria dell’Informazione, Università di Parma. Her research interests focus on real-time computer vision for automatic vehicle guidance and image processing techniques based on the Mathematical Morphology computational model. She is a member of IEEE and IAPR.

    This research has been partially supported by the Italian National Research Council (CNR) under the frame of the Progetto Finalizzato Trasporti 2 and Progetto Madess 2.

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