用Transformer预测车道变道意图，发现跨地区数据表现下降但合并训练可提升准确率
Lane Change Intention Prediction of two distinct Populations using a Transformer
- 将车辆位置转为弗雷内坐标，用Transformer预测变道意图
- 跨数据集测试准确率降至71.94%，单数据集训练达85.44%
- 融合德港数据训练可提升至86.84%，适合多区域自动驾驶研究
在复杂交通场景中，预测周边车辆意图可提升自动驾驶策略性能。现有研究多基于单一地区或国家的数据集。本文采用Transformer网络，根据纵向和横向的位置与距离特征（转换为弗雷内坐标后输入），预测目标车辆是否进行左变道、右变道或保持车道。使用两个来自LevelXData的高速公路数据集：德国高速与香港高速数据。跨数据集评估显示，准确率降至71.94%，而单一数据集训练时可达85.44%。当联合训练两数据集时，准确率最高达86.84%。
原文摘要 · Abstract (English)
In complex traffic scenarios, intention prediction of surrounding vehicles can improve the strategy of automated driving functions. Existing work on intention prediction is often trained on datasets of single regions or countries. In this article, a transformer network for lane change intention prediction was trained to predict whether target vehicles perform a left lane change, right lane change or keep their lane. Features inputs used were vehicle positions and distances, each for longitudinal and lateral direction. Before being inputted, these features were converted to Frenet coordinates. Two different datasets from leveLXData collected on highways were used: one from German highways and one from Hong Kong highways. Through cross-dataset evaluation, we show that the accuracy values drop to 71.94%, compared to 85.44% when doing dataset-specific training. When training on both datasets, accuracy levels up to 86.84% were achieved.
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