Fully autonomous vehicles promise enormous gains in safety, efficiency, and economy for transportation. In previous work, the authors of this chapter have introduced a system for managing autonomous vehicles at intersections that is capable of handling more vehicles and causing fewer delays than modern- day mechanisms such as traffic lights and stop signs [Dresner & Stone 2005]. This system makes two assumptions about the problem domain: that special infrastructure is present at each intersection, and that vehicles do not experience catastrophic physical malfunctions. In this chapter, they explore two separate extensions to their original work, each of which relaxes one of these assumptions. They demonstrate that for certain types of intersections—namely those with moderate to low amounts of traffic—a completely decentralized, peer-to-peer intersection management system can reap many of the benefits of a centralized system without the need for special infrastructure at the intersection. In the second half of the chapter, they show that their previously proposed intersection control mechanism can dramatically mitigate the effects of catastrophic physical malfunctions in vehicles such that in addition to being more efficient, autonomous intersections will be far safer than traditional intersections are today.

Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field that focuses on integrating information technology with vehicles and transportation infrastructure to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated. The bottleneck for roadway efficiency will no longer be the drivers, but rather the mechanism by which those drivers' actions are coordinated. While open-road driving is a well-studied and more-or-less-solved problem, urban traffic scenarios, especially intersections, are much more challenging. We believe current methods for controlling traffic, specifically at intersections, will not be able to take advantage of the increased sensitivity and precision of autonomous vehicles as compared to human drivers. In this article, we suggest an alternative mechanism for coordinating the movement of autonomous vehicles through intersections. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this multiagent system, intersections use a new reservation-based approach built around a detailed communication protocol, which we also present. We demonstrate in simulation that our new mechanism has the potential to significantly outperform current intersection control technology -- traffic lights and stop signs. Because our mechanism can emulate a traffic light or stop sign, it subsumes the most popular current methods of intersection control. This article also presents two extensions to the mechanism. The first extension allows the system to control human-driven vehicles in addition to autonomous vehicles. The second gives priority to emergency vehicles without significant cost to civilian vehicles. The mechanism, including both extensions, is implemented and tested in simulation, and we present experimental results that strongly attest to the efficacy of this approach.

Fully autonomous vehicles promise enormous gains in safety, efficiency, and economy. Before such gains can be realized, safety and reliability concerns must be addressed. We have previously introduced a system for managing such vehicles at intersections that is capable of handling more vehicles and causing fewer delays than traffic lights and stop signs [2]. While the system is safe under normal operating conditions, we have not discussed the possibility or implications of unforeseen mechanical failures. Because the system orchestrates such precarious "close calls" the tolerance for such errors is small. In this paper, we introduce safety features of the system designed to deal with these types of failures, and perform a basic failure mode analysis, demonstrating that without these features, the system is unsuitable for deployment due to a propensity for catastrophic failure modes.

Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Recent advances in artificial intelligence, multiagent systems (MAS), and Intellgient Transportation Systems (ITS) point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated. The bottleneck for roadway efficiency will no longer be the drivers, but rather the mechanism by which those drivers' actions are coordinated. While open-road driving is a well-studied and more-or-less-solved problem, urban traffic scenarios, especially intersections, are much more challenging.

As computers replace humans as the drivers of automobiles, our current traffic management mechanisms will give way to hyper-efficient protocols designed to exploit the capabilities of fully autonomous vehicles. We have introduced such a system for coordinating large numbers of autonomous vehicles at intersections [2, 3]. Our experiments suggest that this system could alleviate many of the dangers and delays associated with intersections by allowing vehicles to "call ahead" to an agent stationed at the intersection and reserve time and space for their traversal. Unfortunately, such a system is not cost-effective at small intersections. In this paper, we propose an intersection control mechanism for autonomous vehicles designed specifically for low-traffic intersections where the previous system would not be practical. Our mechanism is based on purely peer-to-peer communication and thus requires no infrastructure at the intersection. We present experimental results demonstrating that our system, while not suited to large, busy intersections, can significantly outperform traditional stop signs at small intersections.

In modern urban settings, automobile traffic and collisions lead to endless frustration as well as significant loss of life, property, and productivity. Recent advances in artificial intelligence suggest that autonomous vehicle navigation may soon be a reality. In previous work, we have demonstrated that a reservation-based approach can efficiently and safely govern interactions of multiple autonomous vehicles at intersections. Such an approach alleviates many traditional problems associated with intersections, in terms of both safety and efficiency. However, the system relies on all vehicles being equipped with the requisite technology-a restriction that would make implementing such a system in the real world extremely difficult. In this paper, we extend this system to allow for incremental deployability. The modified system is able to accommodate traditional human-operated vehicles using existing infrastructure. Furthermore, we show that as the number of autonomous vehicles on the road increases, traffic delays decrease monotonically toward the levels exhibited in our previous work. Finally, we develop a method for switching between various human-usable configurations while the system is running, in order to facilitate an even smoother transition. The work is fully implemented and tested in our custom simulator, and we present detailed experimental results attesting to its effectiveness.

Few aspects of modern life inflict as high a cost on society as traffic congestion and automobile accidents. Current work in AI and Intelligent Transportation Systems aims to replace human drivers with autonomous vehicles capable of safely and efficiently navigating through the most hazardous city streets. Once such vehicles are common, interactions between multiple vehicles will be possible. Traffic lights and stop signs, which were designed for human drivers, may no longer be the best method for intersection control. Previously, we made the case for a reservation-based intersection control mechanism designed for autonomous vehicles, but compatible with human drivers. Including human drivers allows incremental deployability as well as support for those who drive for pleasure, but may result in significantly suboptimal performance, as human drivers may be present in dramatically varying proportions. In this paper, we develop a learning-based approach to determine which variant of the control mechanism will be most effective under given conditions, and then combine the resulting predictor with our multiagent intersection management mechanism, enabling it to determine when and how it should alter its configuration to best suit the current traffic conditions. Our extension is fully implemented and tested in simulation, and we provide experimental results demonstrating its efficacy.

Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. In previous work published at AAMAS, we have proposed a novel reservation-based mechanism for increasing throughput and decreasing delays at intersections [3]. In more recent work, we have provided a detailed protocol by which two different classes of agents (intersection managers and driver agents) can use this system [4]. We believe that the domain created by this mechanism and protocol presents many opportunities for multiagent learning on the parts of both classes of agents. In this paper, we identify several of these opportunities and offer a first-cut approach to each.

Few concepts embody the goals of artificial intelligence as well as fully autonomous robots. Countless films and stories have been made that focus on a future filled with autonomous agents that complete menial tasks or run errands that humans do not want or are too busy to carry out. One such task is driving automobiles. In this paper, we summarize the work we have dune towards a future of fully-autonomous vehicles, specifically coordinating such vehicles safely and efficiently at intersections. We then discuss the implications this work has for other areas of AI, including planning, multiagent learning, and computer vision.

Traffic congestion and automobile accidents are two of theleading causes of decreased standard of living and lost pro-ductivity in urban settings. Recent advances in artificial in-telligence suggest that autonomous vehicle navigation willbe possible in the near future. Individual cars can nowbe equipped with features of autonomy such as adaptivecruise control, GPS-based route planning (Rogers, Flechter,& Langley 1999; Schonberg

Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. Recent advances in artificial intelligence suggest vehicle navigation by autonomous agents will be possible in the near future. In a previous paper, we proposed a reservation-based system for alleviating traffic congestion, specifically at intersections. This paper extends our prototype implementation in several ways with the aim of making it more implementable in the real world. In particular, we 1) add the ability of vehicles to turn, 2) enable them to accelerate while in the intersection, and 3) augment their interaction capabilities with a detailed protocol such that the vehicles do not need to know anything about the intersection control policy. The use of this protocol limits the interaction of the driver agent and the intersection manager to the extent that it is a reasonable approximation of reliable wireless communication. Finally, we describe how different intersection control policies can be expressed with this protocol and limited exchange of information. All three improvements are fully implemented and tested, and we present detailed empirical results validating their effectiveness.

AbstractTraffic congestion is one of the leading causes of lost productivity and d ecreased standard of living in urbansettings. Recent advances in artificial intelligence suggest vehicle naviga tion by autonomous agents will be possiblein the near future. In a previous paper, we proposed a reservation-based system for alleviating traffic congestion,specifically at intersections. This paper extends our prototype implementa tion in several ways with the aim of makingit more implementable in the real world. In particular, we add the ability of vehicles to turn, enable them to acceleratewhile in the intersection, improve the efficiency and sensor model of the dr iver agents, and augment their interactioncapabilities with a detailed protocol such that the vehicles do not need to know anything about the intersection controlpolicy. The use of this protocol limits the interaction of the driver agent and the intersection manager to the extent thatit is a reasonable approximation of reliable wireless communication. Finally, we describe how different intersectioncontrol policies can be expressed with this protocol and limited exchange of information. All improvements are fullyimplemented and tested, and we present detailed empirical results validating their effectiveness.This technical report is written as a companion paper to [3]. It contains all the material from [3]. In addition, itincludes the full protocol for agent interaction (Section 4) and a description of the improved sensor model for driveragents (Section 6.3).

The UT Austin Villa Four-Legged Team for RoboCup 2004 was a second-time entry in the ongoing series of RoboCup legged league competitions. The team development began in mid-January of 2003 without any prior familiarity with the Aibos. After entering a fairly non-competitive team in RoboCup 2003, the team made several important advances. By the July 2004 competition place in Lisbon, Portugal, it was one of the top few teams. In this report, we describe both our development process and the technical details of its end result. In conjunction with our previous technical report [12] this paper provides full documentation of the algorithms behind our approach with the goal of making them fully replicable.

This paper investigates the problem of finding optimal multicast virtual topologies, with respect to minimizing the maximum hop distance, in wavelength-division multiplexing multicast trees. Although the problem of finding optimal multicast trees is itself known to be NP-complete under many optimization metrics, high-quality approximation algorithms are known for this problem. We investigate the case that a multicast tree has been selected and seek to embed an optimal virtual topology in this multicast tree. We show that the problem can be solved in polynomial time when tap-and-continue switches are employed, which allow a lightpath to be tapped by some number of intermediate nodes. However, the problem becomes NP-complete when fully multicast-capable switches are employed. Our results suggest that tap-and-continue switches can be used to obtain high-quality multicast virtual topologies, while heuristics will be required to find good solutions in fully multicast-capable networks.

In this paper we examine the problem of constructing optimal virtual topologies for one-to-many communication in optical networks employing wavelength-division multiplexing. A virtual topology is a collection of optical lightpaths embedded in a physical topology. A packet sent from the source node travels over one or more lightpaths en route to its destination. Within a lightpath, transmission is entirely optical. At the terminus of a lightpath the data is converted into the electronic domain where it may be retransmitted on another lightpath toward its destination. Since the conversion of the packet from the optical to the electronic domain introduces delays and uses limited physical resources, one important objective is to find virtual topologies which minimize either the maximum or average number of lightpaths used from the source to all destination nodes. Although this problem is NP-complete in general, we show that minimizing the maximum or average number of lightpaths in path and ring topologies can be solved optimally by efficient algorithms.

Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. Recent advances in artificial intelligence suggest that autonomous vehicle navigation will be possible in the near future. In this paper, we propose a reservation-based system for alleviating traffic congestion, specifically at intersections. First, we describe a custom simulator created to measure the different delays associated with conducting traffic through an intersection. Second, we specify a precise metric for evaluating the quality of traffic control at an intersection. Using this simulator and this metric, we show that the reservation-based system can perform two to three hundred times better than traffic lights. As a result, it can smoothly handle much heavier traffic conditions. We demonstrate that this system very closely approximates an overpass, which is the optimal solution for problem with which we are dealing.

Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. Recent advances in artificial intelligence suggest vehicle navigation by autonomous agents will be possible in the near future. In this paper, we propose a reservation-based system for alleviating traffic congestion, specifically at intersections, and under the assumption that the cars are controlled by agents. First, we describe a custom simulator that we have created to measure the different delays associated with conducting traffic through an intersection. Second, we specify a precise metric for evaluating the quality of traffic control at an intersection. Using this simulator and this metric, we show that our reservation-based system can perform two to three hundred times better than traffic lights. As a result, it can smoothly handle much heavier traffic conditions. We show that our system very closely approximates an overpass, which is the optimal solution for the problem with which we are dealing.

This paper addresses the problem of constructing optimal virtual topologies for multicast communication in optical networks employing wavelength-division multiplexing (WDM). For concreteness, we use the average hop distance as the metric of optimality. WDM networks supporting multicast communication typically employ multicast-capable switches which permit a path entering a switch on an incoming wavelength to be replicated or "split" optically to one or more output links. This splitting incurs a power loss which is frequently neglected in existing heuristics and algorithms. In this paper we show that the problem of finding optimal virtual topologies with splitting loss constraints can be solved in polynomial time in directed paths and rings, although the problem is NP-complete for general topologies.