Abstract
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.
Abstract (translated)
匝道（上、下坡路段）虽然是高速公路互动中变异性较高的部分，但一直以来却未得到充分的研究。预测这些区域内的车辆行为可以降低不确定性的影响，并提高道路安全。本文研究了这一兴趣区域（AoI）与直线高速公路段之间的差异。利用ExiD无人机数据集和多层LSTM架构来训练AoI模型，在此过程中测试了不同的预测时长和不同模型的工作流程。结果显示，对于长达4秒的预测范围，AoI的预测准确率可达到约76%，而一般高速公路场景在最大预测时长下的预测准确率为94%左右，这表明该方法具有很大的潜力。
URL
https://arxiv.org/abs/2601.14848
