Quality-of-Experience-Oriented Autonomous Intersection Control in Vehicular Networks

Dai, Penglin; Li, Kai; Zhuge, Qingfeng; Sha, Edwin H.-M.; Lee, Victor C. S.; Son, Sang H.

doi:10.1109/tits.2016.2514271

Cited by 98 publications

(37 citation statements)

References 34 publications

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“…In particular, there is a large and growing body of literature based on multi-agent simulations [5], [13]- [17], genetic algorithms [18], tokenbased approaches [19], [20], auction-based approaches [21], and discrete-time occupancy theory [22]. A comprehensive recent summary and comparison of some of these approaches are provided by Dai et al [23]. These approaches propose practical algorithms that seem to perform well in simulation studies.…”

Section: Introductionmentioning

confidence: 99%

Polling-Systems-Based Autonomous Vehicle Coordination in Traffic Intersections With No Traffic Signals

Miculescu

Karaman

2020

IEEE Trans. Automat. Contr.

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The rapid development of autonomous vehicles spurred a careful investigation of the potential benefits of allautonomous transportation networks. Most studies conclude that autonomous systems can enable drastic improvements in performance. A widely studied concept is all-autonomous, collision-free intersections, where vehicles arriving in a traffic intersection with no traffic light adjust their speeds to cross safely through the intersection as quickly as possible. In this paper, we propose a coordination control algorithm for this problem, assuming stochastic models for the arrival times of the vehicles. The proposed algorithm provides provable guarantees on safety and performance. More precisely, it is shown that no collisions occur surely, and moreover a rigorous upper bound is provided for the expected wait time. The algorithm is also demonstrated in simulations. The proposed algorithms are inspired by polling systems. In fact, the problem studied in this paper leads to a new polling system where customers are subject to differential constraints, which may be interesting in its own right.

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Section: Introductionmentioning

confidence: 99%

Polling-Systems-Based Autonomous Vehicle Coordination in Traffic Intersections With No Traffic Signals

Miculescu

Karaman

2020

IEEE Trans. Automat. Contr.

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“…Centralized coordination approaches collect the global information of the entire intersection to regulate the vehicles at the intersection. Dai et al solve the intersection control problem with convex optimization [14]. Guan et al proposed a centralized conflict-free cooperation method for multiple connected vehicles at unsignalized intersection using model accelerated proximal policy optimization [15].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection

Jiang

Zhang

2020

Mathematical Problems in Engineering

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Unsignalized intersection control is one of the most critical issues in intelligent transportation systems, which requires connected and automated vehicles to support more frequent information interaction and on-board computing. It is very promising to introduce reinforcement learning in the unsignalized intersection control. However, the existing multiagent reinforcement learning algorithms, such as multiagent deep deterministic policy gradient (MADDPG), hardly handle a dynamic number of vehicles, which cannot meet the need of the real road condition. Thus, this paper proposes a Cooperative MADDPG (CoMADDPG) for connected vehicles at unsignalized intersection to solve this problem. Firstly, the scenario of multiple vehicles passing through an unsignalized intersection is formulated as a multiagent reinforcement learning (RL) problem. Secondly, MADDPG is redefined to adapt to the dynamic quantity agents, where each vehicle selects reference vehicles to construct a partial stationary environment, which is necessary for RL. Thirdly, this paper incorporates a novel vehicle selection method, which projects the reference vehicles on a virtual lane and selects the largest impact vehicles to construct the environment. At last, an intersection simulation platform is developed to evaluate the proposed method. According to the simulation result, CoMADDPG can reduce average travel time by 39.28% compared with the other optimization-based methods, which indicates that CoMADDPG has an excellent prospect in dealing with the scenario of unsignalized intersection control.

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“…For the purpose of collision avoidance of autonomous vehicles at intersections, the optimization problem is reformulated as a convex program and solved by [4]. An intersection control model is introduced not only to guarantee collision avoidance but the travel experience, see [5]. The intersection is divided into collision areas and a schedule rule is designed to determine the priorities of the vehicles.…”

Section: Introductionmentioning

confidence: 99%

Control methods for the coordination of autonomous vehicles at intersections

Farkas

Mihály

Gáspár

2020

2020 European Control Conference (ECC)

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The paper defines the main challenges related to the determination of the ordering of vehicles at an intersection and proposes a general coordination method for autonomous vehicles to avoid collisions in intersection scenarios. The minimization of traveling time and energy optimality as performances of vehicles are incorporated in the iterative control procedure, and the goal of the research is to secure the collision-free passage of autonomous vehicles through the intersection. The proposed coordination method is demonstrated by several simulation examples made in a high-accuracy simulation environment to present its efficiency.

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Quality-of-Experience-Oriented Autonomous Intersection Control in Vehicular Networks

Cited by 98 publications

References 34 publications

Polling-Systems-Based Autonomous Vehicle Coordination in Traffic Intersections With No Traffic Signals

Polling-Systems-Based Autonomous Vehicle Coordination in Traffic Intersections With No Traffic Signals

Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection

Control methods for the coordination of autonomous vehicles at intersections

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