On and off-ramps are understudied road sections even though they introduce a higher level of variation in highway interactions. Predicting vehicles' behavior in these areas can decrease the impact of uncertainty and increase road safety. In this paper, the difference between this Area of Interest (AoI) and a straight highway section is studied. Multi-layered LSTM architecture to train the AoI model with ExiD drone dataset is utilized. In the process, different prediction horizons and different models' workflow are tested. The results show great promise on horizons up to 4 seconds with prediction accuracy starting from about 76% for the AoI and 94% for the general highway scenarios on the maximum horizon.

翻译：匝道区域作为高速公路交互中变化性较高的路段，其研究尚不充分。预测车辆在此类区域的行为有助于降低不确定性影响并提升道路安全性。本文研究了该兴趣区域与直线高速公路路段之间的差异。通过采用多层LSTM架构，利用ExiD无人机数据集对兴趣区域模型进行训练。研究过程中测试了不同预测时间跨度与模型工作流程。结果表明：在长达4秒的预测跨度内，模型展现出良好性能——最大时间跨度下，兴趣区域的预测准确率约为76%，而常规高速公路场景的预测准确率达到94%。