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- Title
E-DNet: An End-to-End Dual-Branch Network for Driver Steering Intention Detection.
- Authors
Fu, Youjia; Xue, Huixia; Fu, Junsong; Xu, Zihao
- Abstract
An advanced driving assistant system (ADAS) is critical for improving traffic efficiency and ensuring driving safety. By anticipating the driver's steering intentions in advance, the system can alert the driver in time to avoid a vehicle collision. This paper proposes a novel end-to-end dual-branch network (EDNet) that utilizes both in-cabin and out-of-cabin data. In this study, we designed an in-cabin driver intent feature extractor based on 3D residual networks and atrous convolution, which is applicable to video data and is capable of capturing a larger range of driver behavior. In order to capture the long-term dependency of temporal data, we designed the depthwise-separable max-pooling (DSMax) module and combined it with a convolutional LSTM to obtain the road environment feature extractor outside the cabin. In addition, to effectively fuse different features inside and outside the cockpit, we designed and propose the dynamic combined-feature attention fusion (D-CAF) module. EDNet employs a freeze-training method, which enables the creation of a lightweight model while simultaneously enhancing the final classification accuracy. Extensive experiments on the Brain4Cars dataset and the Zenodo dataset show that the proposed EDNet was able to recognize the driver's steering intention up to 3 s in advance. It outperformed the existing state of the art in most driving scenarios.
- Subjects
TRAFFIC safety; INTENTION; RECURRENT neural networks
- Publication
Electronics (2079-9292), 2024, Vol 13, Issue 13, p2477
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics13132477