基于Level-k博弈的入匝道合并社会意识决策算法

Daofei Li, Hao Pan, Yang Xiao, Bo Li, Linhui Chen, Houjian Li, Hao Lyu
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引用次数: 3

摘要

匝道入路合并通常与自我和其他车辆之间的高度动态交互有关,这在密集的交通中更具挑战性。考虑到整体交通状况和其他交互驾驶员的个体特征,提出了一种基于level-k博弈论的社会感知分层决策算法。为适应动态的互动情境,在线估算互动驱动者的社会价值取向,并进一步整合路权和尝试性合并尝试,提高决策模型的社会认知度。建立了无人机自然驾驶数据集,对模型的有效性进行了标定和验证。仿真实验进一步表明,该模型可以提高匝道合并的安全性和成功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social-Aware Decision Algorithm for On-ramp Merging Based on Level-k Gaming
On-ramp merging is often associated with highly dynamic interactions between ego and other vehicles, which are more challenging in dense traffic. Considering both the overall traffic situation and the individual characteristics of other interacting drivers, we propose a social-aware hierarchical decision algorithm based on level-k game theory. To adapt to dynamic interactive situations, the social value orientation of interacting drivers is estimated on-line, while the right of way and tentative merging attempts are further integrated to improve the social-awareness of the decision model. A drone dataset of naturalistic driving is built to calibrate and validate the model effectiveness. Simulator experiments with drivers in the loop further show that the model can improve the safety and success rate in ramp merging.
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