用三种神经网络预测司机变道意图，变压器模型表现最佳。
Prediction of Lane Change Intentions of Human Drivers using an LSTM, a CNN and a Transformer
- 融合LSTM、CNN与变压器结构，基于highD数据集建模变道意图。
- 最高准确率达96.73%，且变压器抗过拟合能力更强。
- 适合自动驾驶系统安全规划，尤其复杂路况下变道预判。
前车变道对自动驾驶车辆的运动规划有重大影响，尤其在复杂交通场景中。预测变道意图有助于提升行车安全与效率。尽管已有诸多研究，但多数聚焦于固定时间点预测，而非特定时间区间内的行为预测。此外，不同网络架构的比较不足，输入特征选择也缺乏系统评估。本文对比了LSTM、CNN和Transformer三种网络结构，在公开数据集highD上构建变道意图预测模型。详细说明了数据预处理、特征选取、网络设计及多组输入配置下的实验结果。结果显示，变压器模型性能最优，且受过拟合影响较小。在不同输入配置下，准确率介于82.79%至96.73%之间，整体表现优异，兼顾精确率与召回率。
原文摘要 · Abstract (English)
Lane changes of preceding vehicles have a great impact on the motion planning of automated vehicles especially in complex traffic situations. Predicting them would benefit the public in terms of safety and efficiency. While many research efforts have been made in this direction, few concentrated on predicting maneuvers within a set time interval compared to predicting at a set prediction time. In addition, there exist a lack of comparisons between different architectures to try to determine the best performing one and to assess how to correctly choose the input for such models. In this paper the structure of an LSTM, a CNN and a Transformer network are described and implemented to predict the intention of human drivers to perform a lane change. We show how the data was prepared starting from a publicly available dataset (highD), which features were used, how the networks were designed and finally we compare the results of the three networks with different configurations of input data. We found that transformer networks performed better than the other networks and was less affected by overfitting. The accuracy of the method spanned from $82.79\%$ to $96.73\%$ for different input configurations and showed overall good performances considering also precision and recall.
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