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

doi:10.1109/TIV.2023.3279425

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

16 Citations (Scopus)

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.

Original language	English
Pages (from-to)	4599-4612
Number of pages	14
Journal	IEEE Transactions on Intelligent Vehicles
Volume	8
Issue number	11
DOIs	https://doi.org/10.1109/TIV.2023.3279425
Publication status	Published - 1 Nov 2023
Externally published	Yes

Keywords

Driving style recognition
evaluation metric
intelligent vehicles
short-term and long-term

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10.1109/TIV.2023.3279425

Cite this

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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.",

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AU - Guo, Lulu

AU - Zhang, Jia

AU - Gao, Bingzhao

AU - Chen, Hong

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AB - 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.

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A Review of Driving Style Recognition Methods From Short-Term and Long-Term Perspectives

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Keywords

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