Effect of optimization time-scale on learning-based cooperative merging control at a nonsignalized intersection
Effect of optimization time-scale on learning-based cooperative merging control at a nonsignalized intersection
复制标题
优化时间尺度对无信号交叉口基于学习的协同并道控制的影响
DOI：
10.1109/access.2023.3263118
复制
发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
T.
中科院分区：
文献类型：
--
作者：
Katagiri;S.;Miwa;T.;Tashiro;M. and Morikawa;T.
自动驾驶和大规模通信基础设施的广泛使用有望促进高度协作驾驶。尽管大量的研究集中在开发有效的非信号交叉口协同控制方法上，但对冲突目标车辆的协同控制对未来交通流的影响尚未得到研究。因此，我们的目的是研究是否应该考虑这种协同控制对未来交通的影响。我们建立了交通模拟器和几种机器学习方法来选择最优的合作方式。决策树和深度神经网络在评估短期/长期预测控制的两个指标上进行了训练：最小化冲突车辆的行驶时间和2)包括未来交通流的所有车辆。仿真分析结果表明，这些指标之间的总行程时间没有显著差异。这一发现表明，有效的交通流，包括未来的交通流，是可以实现的短期合作控制方法，可以很容易地建立。
Automated driving and the widespread use of large-scale communication infrastructure are expected to facilitate highly cooperative driving. Although considerable research has focused on developing efficient cooperative control methods for nonsignalized intersections, the effect of cooperative control for conflicting target vehicles on future traffic flow is yet to be investigated. Therefore, we aim to investigate whether the impact of such cooperative control on future traffic should be considered. We established a traffic simulator and several machine-learning methods to select the optimal cooperative method. The decision tree and deep neural network were trained on two indices that evaluate short-term/long-term predictive control: to minimize the travel time of the 1) conflicting vehicles and 2) all vehicles including future traffic flow. Simulation analysis results indicated that there were no significant differences in the total travel times between these indices. This finding indicates that efficient traffic flow, which includes future traffic flow, is achievable by short-term cooperative control methods that can be established easily.