A Multiagent Approach to Autonomous Intersection Management

Abstract

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 Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated.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.

Ứng Dụng Lôgic Mờ Xây Dựng Hệ Thống Điều Khiển Tín Hiệu Đèn Giao Thông Thông Minh

“…Các phương pháp thường được các nhà khoa học nghiên cứu và ứng dụng, đó là giải thuật di truyền [1], [2], lôgic mờ [3], [4] và gần đây nhất là hệ thống đa tác tử [5], [6], [7], [8].…”

“…Tiếp cận đa tác tử trong việc điều khiển tín hiệu đèn GT ngày càng được ưa chuộng [5], [6], [7]. Hầu hết các mô hình đều tập trung vào việc phân tích mục tiêu, các nhiệm vụ của các tác tử và cơ chế giao tiếp của chúng.…”

“…Lôgic mờ sẽ sử dụng một tập luật kiểm tra ra quyết định mở rộng hay kết thúc đèn xanh hiện tại trước khi chu kỳ đèn xanh đó kết thúc một khoảng thời gian t (2s đến 5s) [6]. Nếu quyết định mở rộng thời gian đèn xanh nó sẽ tăng chu kỳ đèn xanh đó lên một đơn vị thời gian và không kiểm tra nữa, ngược lại nó sẽ đợi cho đến hết chu kỳ hiện tại hoặc dừng ngay lập tức để chuyển đến pha đèn tiếp theo.…”

Bringing simulation to life: A mixed reality autonomous intersection

“…In previous work, the estimation of arrival times and arrival velocities is based on a heuristic we called the optimistic/pessimistic heuristic, that derives the arrival time and arrival velocity based on a prediction about whether the vehicle can arrive at the intersection without the intervention of other vehicles [8]. However, this heuristic does not guarantee that the vehicle can arrive at the intersection at the estimated arrival time or the estimated arrival velocity; in fact, our experiments showed that vehicles were often unable to reach the intersection at the correct time, forcing them to cancel their reservations after holding the reservations for quite some time.…”

Information sharing among autonomous vehicles crossing an intersection

“…Navigation functions are practical tools introduced in robotics for solving collision avoidance problems [6] such as formation [7], rendezvous and consensus scenarios. Decentralized navigation functions have two great benefits.…”