Information-Driven Autonomous Intersection Control via Incentive Compatible Mechanisms

Sayin, Muhammed O.; Lin, Chung-Wei; Shiraishi, Shinichi; Shen, Jiajun; Başar, Tamer

doi:10.1109/tits.2018.2838049

Cited by 48 publications

(33 citation statements)

References 16 publications

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“…We can derive the expected speed of the traffic flow E[v], i.e., the upper limit of vehicles' average speed, from the formula (30) and 31:…”

Section: Performance Evaluation a Theoretical Upper Limitmentioning

confidence: 99%

“…In addition to ensuring collision-free between vehicles, pedestrian safety was also taken into consideration. Sayin et al [30] proposed an information-driven AIC to enable the agent to consider driver-exclusive information reported by drivers to enhance the quality of transportation. To avoid the misreporting of the driver-exclusive information, a payment-based incentive and compatible mechanism which guarantees truthful utility and maximizes the social welfare was designed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles

Zhang

Liu

et al. 2020

IEEE Access

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Traffic light-free intersection control is envisioned to alleviate congestion and manage vehicles intelligently. With the help of vehicle-to-infrastructure (V2I) communication and edge computing (EC), vehicles are instructed to cross the intersection with high vehicle safety and traffic efficiency without traffic lights. However, unstable channel conditions can lead to the reduction of traveling safety. In this paper, we propose a robust autonomous intersection control (AIC) approach with global optimization scheduling, which protects connected autonomous vehicles from collision under any channel conditions while achieving decent traffic efficiency. In particular, we propose an AIC model that gives vehicles certain autonomy under centralized control to ensure the traveling safety in case of some emergencies. By conducting an interference graph, we simplify the AIC problem as a weighted maximal clique problem with restriction. To improve the fairness and efficiency in terms of vehicle passage, multiple factors such as travel delay, traffic of the current lane and passengers' desired speed are considered. Furthermore, we propose a heuristic algorithm to search the solution space. For further optimization, a particle swarm optimization algorithm is proposed, achieving a near-optimal result with adjustable overhead. Finally, we build the simulation model and conduct a comparative performance evaluation. Simulation results demonstrate the superiority of our proposed scheme. INDEX TERMS Connected autonomous vehicles, autonomous intersection control, edge computing, robustness, vehicular network, optimization.

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“…We can derive the expected speed of the traffic flow E[v], i.e., the upper limit of vehicles' average speed, from the formula (30) and 31:…”

Section: Performance Evaluation a Theoretical Upper Limitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles

Zhang

Liu

et al. 2020

IEEE Access

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show abstract

“…It is assumed that the urgency u i (t) is a piece of private information of each vehicle i, and is therefore not accessible to other vehicles. This kind of scenario is typically tackled via auctions, which can be designed in order to induce selfish agents to disclose their true urgency [9]- [16]. For example, in [9], the earliest time-slot in an intersection is auctioned off by an intersection manager among all vehicles at the front of each lane.…”

Section: Related Workmentioning

confidence: 99%

Today Me, Tomorrow Thee: Efficient Resource Allocation in Competitive Settings using Karma Games

Censi

Bolognani

Zilly

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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We present a new type of coordination mechanism among multiple agents for the allocation of a finite resource, such as the allocation of time slots for passing an intersection. We consider the setting where we associate one counter to each agent, which we call karma value, and where there is an established mechanism to decide resource allocation based on agents exchanging karma. The idea is that agents might be inclined to pass on using resources today, in exchange for karma, which will make it easier for them to claim the resource use in the future. To understand whether such a system might work robustly, we only design the protocol and not the agents' policies. We take a game-theoretic perspective and compute policies corresponding to Nash equilibria for the game. We find, surprisingly, that the Nash equilibria for a society of self-interested agents are very close in social welfare to a centralized cooperative solution. These results suggest that many resource allocation problems can have a simple, elegant, and robust solution, assuming the availability of a karma accounting mechanism.

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“…And when managing sensors, the target tracking accuracy is optimized as the objective function or satisfied as one of the constraints. The second category is the information based sensor management method [19]- [23]. In this method, the information gain obtained by observations is maximized in sensor management.…”

Section: Introductionmentioning

confidence: 99%

Distributed Sensor Management Based on Target Losing Probability for Maneuvering Multi-Target Tracking

Shan

Pang

2020

IEEE Access

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This paper proposed a distributed sensor management method in the constraint of target losing probability for maneuvering multi-target tracking. Three main contributions existed in this paper, including adding the emission interception risk to the sensor management objective function, analyzing actual working modes of radars and regarding the target losing probability as one of constraints in sensor management. Two kind of radar working modes were introduced. They were the single target tracking mode and the multiple target tracking mode, leading to different kinds of sensor management methods. Then two sensor management models, containing sensor management in a large defending scope and in a small defending scope, were built. After that, the distributed sensor management process was introduced. Finally, simulations were conducted to show the effectiveness and efficiency of the proposed sensor management methods. By applying the proposed sensor management method, targets couldn't be easily lost and radar could be well protected at the same time. INDEX TERMS Sensor management, target tracking, distributed sensor networks, target losing probability.

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Information-Driven Autonomous Intersection Control via Incentive Compatible Mechanisms

Cited by 48 publications

References 16 publications

Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles

Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles

Today Me, Tomorrow Thee: Efficient Resource Allocation in Competitive Settings using Karma Games

Distributed Sensor Management Based on Target Losing Probability for Maneuvering Multi-Target Tracking

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