Title | ||
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V2x Enabled Non-Signalized Intersections Management: A Function Approximation Approach |
Abstract | ||
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The significant enhancement of vehicular communications together with artificial intelligence (AI) have opened up new horizons for innovative data-driven traffic management solution within intelligent transportation system (ITS). In this paper, to alleviate progressively worse urban traffic, we propose an efficient vehicle-to-everything (V2X) communications enabled non-signalized intersection management framework for automated vehicles. First, a resource reservation model involved with different functional planes has been developed for vehicle collision avoidance in V2X enabled non-signalized intersections. Second, reinforcement learning (RL) solution is leveraged to enhance management efficiency of non-signalized scheduling. Furthermore, considering the dimensionality disaster problem of vehicle state caused by complicated traffic environment, we propose a function approximation based non-signalized intersection control (FA-NIC) algorithm to obtain optimal scheduling strategy. Simulation results are provided to demonstrate the effectiveness of our proposed non-signalized intersection management solution. |
Year | DOI | Venue |
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2020 | 10.1109/GLOBECOM42002.2020.9322095 | 2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) |
Keywords | DocType | ISSN |
Vehicular Communication, Non-Signalized Intersections Control, Reinforcement Learning, Gradient Descent | Conference | 2334-0983 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yunting Xu | 1 | 5 | 2.44 |
Haibo Zhou | 2 | 203 | 14.10 |
Bo Qian | 3 | 26 | 4.48 |
Hanlin Wu | 4 | 0 | 0.34 |
Ting Ma | 5 | 8 | 2.15 |
Xuemin Sherman Shen | 6 | 0 | 1.01 |