用LSTM预测高速出入口车辆变道，提升行车安全
From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps
- 基于多层LSTM建模出入口区域车辆行为
- 4秒内预测准确率达76%（出入口）和94%（直道）
- 适合自动驾驶与交通系统安全研究者参考
出入口路段虽存在更高复杂性，但研究较少。预测该区域车辆行为可降低不确定性并提升道路安全。本文对比了出入口区域（AoI）与直道段的差异，采用多层LSTM架构，基于ExiD无人机数据集训练模型，并测试不同预测时长与模型流程。结果表明，在最大4秒预测时长下，出入口场景预测准确率可达约76%，直道场景达94%，表现优异。
原文摘要 · Abstract (English)
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.
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