基于非平衡学习技术的车辆行为预测与泛化
Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques
摘要：
使用基于学习的方法进行车辆行为预测是一个很有前途的研究课题。然而，许多公开可用的数据集遭受类分布偏斜，如果不解决，这将限制学习性能。本文提出了一种由LSTM自动编码器和SVM分类器组成的交互感知预测模型。此外，提出了一种不平衡学习技术，多类平衡集成。评估表明，该方法增强了模型的性能，从而提高了分类精度。学习模型的良好泛化特性很重要，因此进行泛化研究，其中模型在看不见的交通数据上进行评估，这些数据具有源于不同道路配置的不同交通行为。这是通过使用两个不同的公路交通记录，公开可用的NGSIM US-101和I80数据集来实现的。此外，结构和静态特征编码到学习过程中，以提高泛化的方法进行评估。由此产生的方法在分类和泛化性能方面都有很大的改进。
英文摘要：
The use of learning-based methods for vehicle behavior prediction is a promising research topic. However, many publicly available data sets suffer from class distribution skews which limits learning performance if not addressed. This paper proposes an interaction-aware prediction model consisting of an LSTM autoencoder and SVM classifier. Additionally, an imbalanced learning technique, the multiclass balancing ensemble is proposed. Evaluations show that the method enhances model performance, resulting in improved classification accuracy. Good generalization properties of learned models are important and therefore a generalization study is done where models are evaluated on unseen traffic data with dissimilar traffic behavior stemming from different road configurations. This is realized by using two distinct highway traffic recordings, the publicly available NGSIM US-101 and I80 data sets. Moreover, methods for encoding structural and static features into the learning process for improved generalization are evaluated. The resulting methods show substantial improvements in classification as well as generalization performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
