Driving behavior monitoring plays a crucial role in managing road safety and
decreasing the risk of traffic accidents. Driving behavior is affected by
multiple factors like vehicle characteristics, types of roads, traffic, but,
most importantly, the pattern of driving of individuals. Current work performs
a robust driving pattern analysis by capturing variations in driving patterns.
It forms consistent groups by learning compressed representation of time series
(Auto Encoded Compact Sequence) using a multi-layer seq-2-seq autoencoder and
exploiting hierarchical clustering along with recommending the choice of best
distance measure. Consistent groups aid in identifying variations in driving
patterns of individuals captured in the dataset. These groups are generated for
both train and hidden test data. The consistent groups formed using train data,
are exploited for training multiple instances of the classifier. Obtained
choice of best distance measure is used to select the best train-test pair of
consistent groups. We have experimented on the publicly available UAH-DriveSet
dataset considering the signals captured from IMU sensors (accelerometer and
gyroscope) for classifying driving behavior. We observe proposed method,
significantly outperforms the benchmark performance.

本文使用多层 seq-2-seq 自动编码器以及分层聚类来进行精确的驾驶模式分析，得出特定训练数据集的可靠分类器。研究表明，该方法显著优于基准性能。