A Review of Driving Style Recognition Methods From Short-Term and Long-Term Perspectives
Hongqing Chu, Hejian Zhuang, Wenshuo Wang, Xiaoxiang Na, Lulu Guo, Jia Zhang, Bingzhao Gao, Hong Chen
Abstract
Driving style recognition provides an effective way to understand human driving behaviors and thereby plays an important role in the automotive sector. However, most works fail to consider the influence of deploying the recognition results on the vehicle side, which requires real-time recognition performance. To facilitate the application of driving styles in automotive, we survey related advances in driving style recognition along short- and long-term pipelines. We first defined short- and long-term driving styles and then described the input data used by the recognition models and related data-processing techniques. Furthermore, we also revisited existing evaluation metrics for different recognition algorithms. Finally, we discussed the potential applications of driving style recognition in intelligent vehicles.