Pay for Intersection Priority: A Free Market Mechanism for Connected Vehicles

Lin, Dianchao; Jabari, Saif Eddin

doi:10.1109/tits.2020.3048475

Cited by 17 publications

(12 citation statements)

References 84 publications

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“…However, it assumed vehicles interacting with a central agent that made decisions for them and vehicles obeyed that central system. Lin and Jabari [17] introduced a free market mechanism to set the intersection priority. One could pay for faster movement in their model.…”

Section: Related Workmentioning

confidence: 99%

Rock-Paper-Scissors-Hammer: A Tie-Less Decentralized Protocol for IoT Resource Allocation

Dutta

2021

IoT

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With the rapid development of the autonomous world, local decision making between devices is becoming important. This article provides a new paradigm (Rock-Paper-Scissors-Hammer: RPSH) that can reduce the number of conflicts or decision draws and thus increase the throughput of autonomous devices while reducing the kept number of records or transactions. The paradigm requires a sealed envelope protocol and sequential message passing between both parties to decide unanimously a winner between the two participants without a third-party mediation. The message passing proposes a detailed record in a blockchain-like format that is not corruptible and is verifiable for conflict resolution. A simulated IoT environment is created to show the advantage of the proposed protocol and it shows significant reduction in mean efforts due to the elimination of draws or undecided situations. Autonomous devices, such as cars, need to maintain meticulous, lightweight, but blockchain-like record keeping for insurance settlements or conflict resolutions; that archival data size is significantly reduced by the RPSH protocol.

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Section: Related Workmentioning

confidence: 99%

Rock-Paper-Scissors-Hammer: A Tie-Less Decentralized Protocol for IoT Resource Allocation

Dutta

2021

IoT

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“…Vasirani and Ossowski [39] proposed a combinatorial auction for assigning turns at intersections. Censi et al [4] introduced a karmabased auction and Lin and Jabari [24] proposed a mechanism for pricing intersection priority based on transferable utility games. However, these auctions are not incentive-compatible.…”

Section: Prior Workmentioning

confidence: 99%

“…To prevent collisions and deadlocks, it is necessary to determine the order in which agents take turns to navigate the traffic scenario [3], [16], [24], [34], [35], [39]. Thus, the goal of this paper (formalized shortly via Problem III.1) is to compute an optimal turn-based ordering which is defined as follows, Definition III.1.…”

Section: E Problem Statementmentioning

confidence: 99%

“…Auction-based methods, on the other hand, model non-ideal agents in unsignaled traffic scenarios effectively albeit using a monetary-based bidding strategy that is not realizable in real-world scenarios [3], [34], [24], [39]. Our formulation, 1 These terms are defined in Section IV-B GAMEPLAN, differs in this regard wherein we use a novel online driving behavior-based bidding strategy using the CMetric model [8].…”

Section: Our Algorithm: Gameplanmentioning

confidence: 99%

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GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging

Chandra¹,

Manocha²

2021

Preprint

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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 like lidars and cameras). 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, although counter-intuitive, is gametheoretically optimal. Our approach successfully 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.

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“…TSE is a critical precursor to a number of real-time traffic control strategies with either conventional vehicles or a mix with connected and autonomous vehicles [27,39,61]. Such strategies include, but are not limited to, ramp metering, perimeter control, traffic signal control, and vehicle routing [11,25,32,45,62,67]. Existing TSE methods are categorized and discussed below, along with their advantages and drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation

Thodi,

Khan,

Jabari

et al. 2021

Preprint

Self Cite

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We propose a kinematic wave based Deep Convolutional Neural Network (Deep CNN) to estimate high resolution traffic speed dynamics from sparse probe vehicle trajectories. To that end, we introduce two key approaches that allow us to incorporate kinematic wave theory principles to improve the robustness of existing learning-based estimation methods. First, we use an anisotropic traffic-based kernel for the CNN. This kernel is designed to explicitly take forward and backward traffic wave propagation characteristics into account during reconstruction in the space-time domain. Second, we use simulated data for training the CNN. This implicitly imposes physical constraints on the patterns learned by the CNN, providing an alternate, unrestricted way to integrate complex traffic behaviors into learning models. We present the speed fields estimated using the anisotropic kernel and highlight its advantages over its isotropic counterpart in terms of predicting shockwave dynamics. Furthermore, we test the transferability of the trained model to real traffic by using two datasets: the Next Generation Simulation (NGSIM) program and the Highway Drone (HighD) dataset. Finally, we present an ensemble version of the CNN that allows us to handle multiple (and unknown) probe vehicle penetration rates. The results demonstrate that anisotropic kernels can reduce model complexity while improving the correctness of the estimation, and that simulation-based training is a viable alternative to model fitting using real-world data. This suggests that exploiting prior traffic knowledge adds value to learning-based estimation methods, and that there is great potential in exploring broader approaches to do so.

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Pay for Intersection Priority: A Free Market Mechanism for Connected Vehicles

Cited by 17 publications

References 84 publications

Rock-Paper-Scissors-Hammer: A Tie-Less Decentralized Protocol for IoT Resource Allocation

Rock-Paper-Scissors-Hammer: A Tie-Less Decentralized Protocol for IoT Resource Allocation

GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging

Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation

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