GAMEOPT: Optimal Real-time Multi-Agent Planning and Control for Dynamic Intersections (original) (raw)
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GAMEOPT: Optimal Real-time Multi-Agent Planning and Control at Dynamic Intersections
2022
Unsignalized intersections are one of the more complex and prone to accident scenarios in modern transportation networks. Cooperation among Connected Autonomous Vehicles (CAVs) is a promising approach to unsignalized intersection control providing increased safety, efficiency and fairness. We propose a novel hybrid approach to navigating these dynamic, multi-lane, intersections. Our algorithm consists of a hybrid formulation that first uses an auction mechanism to generate a priority entrance sequence for all the agents, followed by an optimization-based trajectory planner that computes the optimal velocity commands that respects the priority sequence. This coupling allows for real-time capable operation in high density multi-agent traffic, while providing formal guarantees in terms of fairness, safety, and efficiency. Our approach can operate at real-time speeds (< 10 milliseconds), which is at least 100× faster than other fully optimization-based methods. Tested on the SUMO sim...
IEEE Robotics and Automation Letters, 2022
We present a new method for multi-agent planning involving human drivers and autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging. In multi-agent planning, the main challenge is to predict the actions of other agents, especially human drivers, as their intentions are hidden from other agents. Our algorithm uses game theory to develop a new auction, called GAMEPLAN, that directly determines the optimal action for each agent based on their driving style (which is observable via commonly available sensors). GAMEPLAN assigns a higher priority to more aggressive or impatient drivers and a lower priority to more conservative or patient drivers; we theoretically prove that such an approach is game-theoretically optimal prevents collisions and deadlocks. We compare our approach with prior state-of-the-art auction techniques including economic auctions, time-based auctions (first-in first-out), and random bidding and show that each of these methods result in collisions among agents when taking into account driver behavior. We additionally compare with methods based on deep reinforcement learning, deep learning, and game theory and present our benefits over these approaches. Finally, we show that our approach can be implemented in the real-world with human drivers.
Multiagent Traffic Management: An Improved 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.
A reservation-based multiagent system for intersection control
2004
Abstract: 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.
Autonomous cooperative driving: A velocity-based negotiation approach for intersection crossing
16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 2013
In this article, a scenario where several vehicles have to coordinate among them in order to cross a traffic intersection is considered. In this case, the control problem relies on the optimization of a cost function while guaranteeing collision avoidance and the satisfaction of local constraints.
Intelligent Autonomous Intersection Management
2022
Connected Autonomous Vehicles will make autonomous intersection management a reality replacing traditional traffic signal control. Autonomous intersection management requires time and speed adjustment of vehicles arriving at an intersection for collision-free passing through the intersection. Due to its computational complexity, this problem has been studied only when vehicle arrival times towards the vicinity of the intersection are known beforehand, which limits the applicability of these solutions for real-time deployment. To solve the real-time autonomous traffic intersection management problem, we propose a reinforcement learning (RL) based multiagent architecture and a novel RL algorithm coined multi-discount Q-learning. In multi-discount Q-learning, we introduce a simple yet effective way to solve a Markov Decision Process by preserving both short-term and long-term goals, which is crucial for collision-free speed control. Our empirical results show that our RL-based multiage...
A multiagent approach to autonomous intersection management
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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.
Turning the corner: improved intersection control for autonomous vehicles
2005
Abstract 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.
Optimal cooperative motion planning for vehicles at intersections
We consider the problem of cooperative intersection management. It arises in automated transportation systems for people or goods but also in multi-robots environment. Therefore many solutions have been proposed to avoid collisions. The main problem is to determine collision-free but also deadlock-free and optimal algorithms. Even with a simple definition of optimality, finding a global optimum is a problem of high complexity, especially for open systems involving a large and varying number of vehicles. This paper advocates the use of a mathematical framework based on a decomposition of the problem into a continuous optimization part and a scheduling problem. The paper emphasizes connections between the usual notion of vehicle priority and an abstract formulation of the scheduling problem in the coordination space. A constructive locally optimal algorithm is proposed. More generally, this work opens up for new computationally efficient cooperative motion planning algorithms.