论文标题
如何选择特征以改善改变道路的预测性能：一项荟萃分析
How to choose features to improve prediction performance in lane-changing intention: A meta-analysis
论文作者
论文摘要
车道变化是一种基本的驾驶行为，与各种类型的碰撞高度相关，例如后端碰撞，侧叶碰撞和角度碰撞以及交通事故崩溃的风险增加。这项研究调查了不同特征类别的有效性在改变车道的意图预测中。已经选择了与改变车道的意图预测有关的研究，遵循严格的标准。然后，使用荟萃分析来评估不同特征类别合并在改变巷的意图中的有效性，而且还捕获了异质性，效果大小组合和发布偏见。根据荟萃分析和回顾的研究论文，结果表明，使用来自不同类型的输入特征会导致不同的性能。与环境甚至驱动程序组合输入类型相比，车辆输入类型在改变车道的意图，预测方面具有更好的性能。最后，根据本文的发现提出了一些潜在的未来研究方向。
Lane-change is a fundamental driving behavior and highly associated with various types of collisions, such as rear-end collisions, sideswipe collisions, and angle collisions and the increased risk of a traffic crash. This study investigates effectiveness of different features categories combination in lane-changing intention prediction. Studies related to lane-changing intention prediction have been selected followed by strict standards. Then the meta-analysis was employed to not only evaluate the effectiveness of different features categories combination in lane-changing intention but also capture heterogeneity, effect size combination, and publication bias. According to the meta-analysis and reviewed research papers, results indicate that using input features from different types can lead to different performances. And vehicle input type has a better performance in lane-changing intention, prediction, compared with environment or even driver combination input type. Finally, some potential future research directions are proposed based on the findings of the paper.
