An Introduction to MultiAgent Systems.

Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to our transportation system. This new technology has the potential to impact vehicle safety, congestion, and travel behavior. All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach $2000 to per year per AV, and may eventually approach nearly $4000 when comprehensive crash costs are accounted for. Yet barriers to implementation and mass-market penetration remain. Initial costs will likely be unaffordable. Licensing and testing standards in the U.S. are being developed at the state level, rather than nationally, which may lead to inconsistencies across states. Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm. The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain. To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy.

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Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations

Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to the transportation system This new technology has the potential to impact vehicle safety, congestion, and travel behavior All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach $2,000 to per year per AV, and may eventually approach nearly $4,000 when comprehensive crash costs are accounted for Yet barriers to implementation and mass-market penetration remain Initial costs will likely be unaffordable Licensing and testing standards in the US are being developed at the state level, rather than nationally, which may lead to inconsistencies across states Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy

Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations

Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions to MAS problems has spawned increasing interest in machine learning techniques to automate the search and optimization process. We provide a broad survey of the cooperative multi-agent learning literature. Previous surveys of this area have largely focused on issues common to specific subareas (for example, reinforcement learning, RL or robotics). In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including RL, evolutionary computation, game theory, complex systems, agent modeling, and robotics. We find that this broad view leads to a division of the work into two categories, each with its own special issues: applying a single learner to discover joint solutions to multi-agent problems (team learning), or using multiple simultaneous learners, often one per agent (concurrent learning). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition, scalability, and adaptive dynamics. We conclude with a presentation of multi-agent learning problem domains, and a list of multi-agent learning resources.

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Cooperative Multi-Agent Learning: The State of the Art

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.

/pdf/a-multiagent-approach-to-autonomous-intersection-management-ndv1gqtnse.pdf

A multiagent approach to autonomous intersection management

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.

/pdf/multiagent-traffic-management-a-reservation-based-32ofuv3f2f.pdf

Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism

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

/pdf/multiagent-traffic-management-an-improved-intersection-ctrbnzne2g.pdf

Multiagent traffic management: an improved intersection control mechanism

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.

/pdf/sharing-the-road-autonomous-vehicles-meet-human-drivers-4aw0ww44m3.pdf

Sharing the road: autonomous vehicles meet human drivers

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

/pdf/replacing-the-stop-sign-unmanaged-intersection-control-for-qckf20xkv5.pdf

Kurt Dresner

Papers

A multiagent approach to autonomous intersection management

Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism

Multiagent traffic management: an improved intersection control mechanism

Sharing the road: autonomous vehicles meet human drivers

Replacing the stop sign: unmanaged intersection control for autonomous vehicles