Getting a driver's license marks a milestone for most teenagers on their journey into adulthood. As a matter of speaking, robotics technology has also matured over the past three decades to the point where it too is ready to claim a driver's license.

Significant recent advances in information processing, machine vision, control theory, and signal processing—in both hardware and software—increase the capability to represent, analyze, perceive, and respond to dynamic road conditions. In this issue, we feature the latest developments in ground-based unmanned autonomous vehicles as seen in the highly publicized DARPA Grand Challenge. While the Grand Challenge focuses attention on current UAVs, this issue also devotes special attention to the future of unmanned vehicles and computational paradigms that might be used as part of a system of intelligent vehicles.

Driving is a demanding task requiring much more than precise, reliable, repetitive robotic behavior. As Herbert Simon noted, "What information consumes is rather obvious: It consumes the attention of its recipients. Hence, a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it."

Successful driving requires attention, alertness, and instinctive responses to varying road conditions, obstacles, and safety conditions. A car driving at 96 kph (60 mph) covers 26.5 meters per second. Most human drivers have a reaction time of three seconds, and the vehicle braking distance at that speed is typically 100 meters. A typical driver is alert to road conditions for about 11 seconds in advance of the vehicle. During this interval, the driver makes a stream of decisions based on 291 meters' worth of data spread across an arbitrary number of lanes. This does not include the processing of sensory data responsible for rearview image analysis that also impacts safety considerations. All these issues call for a steady stream of decisions without abandoning previous tactical choices for route navigation.

To enhance driver capabilities, autonomous computing can significantly aid in routine decision making: Manufacturers have successfully integrated automatic cruise control, automatic transmissions, and fully automatic parking in commercial cars. Remotely operated vehicles have performed successfully in space missions and hostile environments for more than two decades. Industry has used robotic systems for moving materials between locations on production lines for half a century. These accomplishments were mostly due to precise measurement, controlled response, and precise and reliable actuators with a manual override feature to ensure safety.

Are these robotics systems ready to become personal chauffeurs? Only recently has the scientific community taken up the challenge of autonomous driving. The recent DARPA Grand Challenge thrilled us by having machines driving in a fully autonomous mode. More exciting still is the realistic possibility that autonomous vehicles might be able to navigate urban settings in the near future.

The articles in this issue highlight advances in the field of autonomous vehicles as demonstrated by the 2005 DARPA Grand Challenge, but also in the context of the automotive industry in general. While the DARPA Challenge is pushing for fully autonomous solutions, there is a host of computer technology that could help minimize traffic accidents due to driver fatigue, road congestion, environmental effects such as snow and ice, and unavoidable machine failures such as tire blowouts. These technologies include precision GPS for navigation, coordinated control of LCD displays to monitor and report traffic conditions, and increased processor/sensor capabilities on a single vehicle. Technology can aid a driver, but it requires system-wide planning that mirrors the developments of the airline industry.

In "VisLab and the Evolution of Vision-Based UGVs," Massimo Bertozzi and coauthors provide a brief history of the evolution of autonomous vehicles during the past three decades. Early research focused on providing advanced assistance for drivers, the success of which has led to the bolder vision of complete autonomy. This article describes both the history of autonomous vehicle research worldwide and the history of research at VisLab at the University of Parma, which partnered with Oshkosh Truck Corporation to build TerraMax—one of five robots that finished the DARPA Grand Challenge.

"Perception and Planning Architecture for Autonomous Ground Vehicles" by Bob Touchton and colleagues describes the systems-level integration issues that the Grand Challenge poses. Team CIMAR of the University of Florida, now known as Team Gator Nation, consisted of multiple organizations working on different aspects of the problem. They were able to prevent the integration chaos typical of large, disjoint teams by using the standardized Joint Architecture for Unmanned Systems component systems and messaging framework, an architecture developed by the working group chartered by the United States Office of the Secretary of Defense. JAUS aims at creating plug-and-play unmanned systems, where sensors from one vendor can seamlessly be swapped with those from another.

In "Testing Driver Skill for High-Speed Autonomous Vehicles," Chris Urmson and coauthors outline a set of tracking and planning tests for autonomous vehicles that match the industry standard for driver skill. The CMU team used these tests to evaluate the performance of their two entries in the Grand Challenge—both of which finished—and determine the readiness of the technology, control solution, and robot's sensing capability to impact driving behavior.

"To Drive Is Human" by Isaac Miller and colleagues provides a deeper insight into the technical challenges of developing an autonomous ground vehicle. Based on Team Cornell's experience in the Grand Challenge, the authors decompose the overall problem into three parts—localization, sensing, and path planning—and then use this decomposition to discuss the sensor needs and the underlying algorithms. More importantly, the article articulates the challenges in developing an autonomous ground vehicle by contrasting it with human experience. An AGV operates in a discrete world, whereas humans operate in a continuous world. Discretization, performed for computational efficiency, introduces approximations that can lead to anomalous or unsafe behavior that developers must address.

In "On the Importance of Being Contextual," Paolo Lombardi and colleagues describe a framework for factoring "context" into processing continuous sensor data and in making decisions to keep a vehicle on its course. Instead of seeking one unified algorithm that works for all cases by varying the parameters, the authors suggest that the system must continuously evaluate its assumptions and choose from among many prescribed behaviors. They capture the dichotomy that exists in human drivers: what to do and how, placing a significant reliance on the sensory data, and what not to do, a reflex-driven behavior. The authors introduce a framework for analyzing video sensor data so that unmanned vehicles can navigate in a context-driven fashion. This approach emphasizes multimodal awareness as the key to improved vehicle control performance across a broad spectrum of future mission spaces.

It is also desirable that unmanned vehicles receive cues from other vehicles on the road and factor those cues into their decisions. Humans do this in traffic intuitively, and birds demonstrate such a collective behavior as well. In "Memory-Based In Situ Learning for Unmanned Vehicles," Patrick McDowell and coauthors describe preliminary research based on acoustic sensors and learning algorithms that seeks to demonstrate similar emergent behavior in unmanned underwater vehicles. Their work also points to an important issue for the urbanization of unmanned ground vehicles.

Finally, "A Vision for Supporting Autonomous Navigation in Urban Environments" by Vason P. Srini envisions a future infrastructure in which sensors placed on the road gather information and communicate with autonomous vehicles. The infrastructure would enable a vehicle to sense distant environments and dynamically replan its route while coordinating and negotiating between multiple other vehicles, allowing the traffic to move faster through intersections and while merging. Srini's vision—Web-inspired infrastructure—adds a new twist to current research into intelligent highways.

What can we expect next? The articles in this special issue give us a good sense of how far we have come as well as how far we have yet to go. As the " The DARPA Grand Challenge: Past and Future " sidebar describes, we are witnessing history in the making. The upcoming 2007 DARPA Urban Challenge may produce more innovations in an urban setting. We need to wait and see.

We hope that readers share the excitement of the robotics community in bringing together the next great social personal robot, the intelligent vehicle. While the DARPA Grand Challenge has revitalized interest in intelligent highway systems, autonomous vehicles, and sensing technology, a host of other novel issues afford interesting design and computer-engineering challenges for the future.

We hope you enjoy this issue dedicated to advances in robotics as seen from the application of solutions in the DARPA Grand Challenge.

The authors thank Col. Jack E. McCrae Jr., PhD, USAF, for coordinating our efforts with DARPA. We also thank the editorial staff for their outstanding support, understanding, and patience.

The authors' affiliation with the US Air Force does not imply the endorsement of the contents nor that this article represents stated or implied direction of technology emphases within the Air Force, the Department of Defense, or the US government.

Guna Seetharaman is an associate professor of computer science and engineering at the Air Force Institute of Technology, Wright Patterson AFB, Ohio. He is a cofounder of Team CajunBot and led its obstacle-detection algorithms development. His research interests include integrated micro-optoelectronic mechanical systems, computer vision, sensor networks, and high-performance algorithms for intelligent systems. He received a PhD in electrical and computer engineering from the University of Miami. He is a member of the IEEE Computer Society and the ACM. Contact him at guna@ieee.org.

Arun Lakhotia is a professor of computer science with the Center for Advanced Computer Studies at the University of Louisiana at Lafayette. He is a founding member and team leader of Team CajunBot, a contestant and finalist in the 2004 and 2005 DARPA Grand Challenges. His other research interests include the analysis of malicious programs such as computer viruses. He received a PhD in computer science from Case Western Reserve University. He is a member of the IEEE Computer Society and the ACM. Contact him at arun@louisiana.edu.

Erik Philip Blasch is the Fusion Evaluation Tech Lead for the Air Force Research Laboratory, Sensors Directorate, Dayton, Ohio; an adjunct professor at Wright State University; and a reserve major with the Air Force Office of Scientific Research in Arlington, Va. His research interests include information fusion, automatic target detection, and intelligent systems. Blasch received a PhD in electrical engineering from WSU. Contact him at Erik.Blasch@wpafb.af.mil.