Driving style recognition plays a crucial role in improving the operational safety, fuel efficiency, and intelligent control of commercial vehicles. Under real-world driving conditions, Controller Area Network (CAN) bus data from commercial vehicles simultaneously contain rapid transient variations induced by pedal and braking operations, as well as long-term behavioral trends reflecting driving habits, exhibiting pronounced multi-temporal characteristics. In addition, such data are typically affected by high noise levels, high dimensionality, and highly variable operating conditions, which makes it difficult for methods relying on single-scale features or handcrafted rules difficult to maintain robust and stable performance in complex scenarios. To address these challenges, this paper proposes a driving style classification network, termed the Multi-Scale Convolution and Efficient Channel Attention Network (MSCA-Net). By employing parallel convolutional branches with different temporal receptive fields, the proposed network is able to capture fast driver responses, local temporal dependencies, and long-term behavioral evolution, enabling unified modeling of cross-scale temporal patterns in driving behavior. Meanwhile, the Efficient Channel Attention mechanism adaptively emphasizes CAN signal channels that are highly relevant to driving style discrimination, thereby enhancing the discriminative capability and robustness of the learned feature representations. Experiments conducted on real-world multi-dimensional CAN time-series data collected from commercial vehicles demonstrate that the proposed MSCA-Net achieves improved classification performance in driving style recognition. Furthermore, the potential application of the recognized driving styles in adaptive Automated Manual Transmission shift strategy adjustment is discussed, providing a feasible engineering pathway toward behavior-aware intelligent control of commercial vehicle powertrains.
Nie et al. (2026) studied this question.