Simulation of indirect-reciprocity-based lane-changing in Internet of Vehicles environment

Zhouzhou Yao; Xianyu Wu; Ning Li

doi:10.1117/12.2636578

8 May 2022 Simulation of indirect-reciprocity-based lane-changing in Internet of Vehicles environment

Zhouzhou Yao, Xianyu Wu, Ning Li

Proceedings Volume 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）; 1224904 (2022) https://doi.org/10.1117/12.2636578
Event: 2022 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 2022, Xiamen, China

Abstract

This paper proposes a simulation model of lane-changing that considered indirect reciprocity at intersection in Internet of Vehicles environment. The model attempts to address the problem of drivers' reluctance to cooperate in lane changes at intersections by introducing an image scoring system. The core of the system is that lagging vehicles can improve their image score by yielding to lane changing vehicles, thus gaining a greater chance of being yielded to in the future. This paper proposes a Q-learning based reinforcement learning algorithm that extends the model to a repeated evolutionary game, which means that drivers are able to continually evaluate gains and adjust their strategies. The conclusions are summarised below. Such a lane change mechanism, which considers indirect reciprocity, does improve the cooperation of non-fully rational drivers and saves lane change time. driver cooperation improves the operational efficiency of intersections, especially at high penetration of connected vehicles and medium to high traffic densities.

Citation Download Citation

Zhouzhou Yao, Xianyu Wu, and Ning Li "Simulation of indirect-reciprocity-based lane-changing in Internet of Vehicles environment", Proc. SPIE 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 1224904 (8 May 2022); https://doi.org/10.1117/12.2636578

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