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- W3162018790 abstract "To achieve efficient shared autonomy, driver behavior detection (DBD) is undoubtedly required. This paper investigates a deep driver behavior detection (DDBD) model. To overcome the low accuracy of DBD due to a lack of driver behavior data, the similarity of some driver behavior characteristics, and the ignorance of multi-scale structure and texture information, a DDBD model based on human brain consolidated learning (HBCL) is proposed. First, multiple DBD models with input information of different scales are trained based on transfer learning. Then, a new model called consolidation training (CT) using the Mish is trained based on the weight data from the first step. Finally, a novel method for the visualization of the attention area is proposed. The experimental results demonstrate that the proposed model achieved the highest accuracy (94.72% on the Kaggle-driving test dataset), generalization and real-time performance, the attention area is more anthropomorphic as compared with existing state-of-the-art models." @default.
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- W3162018790 date "2021-07-01" @default.
- W3162018790 modified "2023-09-27" @default.
- W3162018790 title "Deep driver behavior detection model based on human brain consolidated learning for shared autonomy systems" @default.
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- W3162018790 doi "https://doi.org/10.1016/j.measurement.2021.109463" @default.
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