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.

翻译：换道是一种基本的驱动行为,与各种类型的碰撞密切相关,例如后端碰撞、两侧碰撞、角撞和交通事故风险增加。这项研究调查了不同特征类别在改变车道的意向预测中的各种组合的有效性。与改变车道的意图预测有关的研究经过严格的标准选择。随后,采用元分析不仅评估不同特征类别组合在改变车道的意向中的有效性,而且还捕捉异质、影响大小组合和出版偏差。根据元分析和经审查的研究论文,结果显示使用不同类型输入的特征可以导致不同的性能。车辆输入类型在改变车道的意向、预测、比环境甚至驾驶器组合输入类型方面表现更好。最后,根据文件的调查结果提出了一些潜在的未来研究方向。