Situation assessment and decision making for lane change assistance using ensemble learning methods
作者：
Highlights：
• Lane change assistance system was developed using ensemble learning methods.
• Proposed system has potential to prevent lane change crashes and thus reducing injuries and fatalities.
• Random forest and AdaBoost outperformed Bayes/Decision tree classifier.
• Higher classification accuracy and lower false positive rates achieved.
• Accuracies of 99.1% and 98.7% were achieved for lane keeping.
摘要
•Lane change assistance system was developed using ensemble learning methods.•Proposed system has potential to prevent lane change crashes and thus reducing injuries and fatalities.•Random forest and AdaBoost outperformed Bayes/Decision tree classifier.•Higher classification accuracy and lower false positive rates achieved.•Accuracies of 99.1% and 98.7% were achieved for lane keeping.
论文关键词：Random forest,Adaboost,Lane changing assistance,Intelligent transportation system
论文评审过程：Available online 17 January 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.01.029
