Driver Attention Area Extraction Method Based on Deep Network Feature Visualization
Source abstract
Abstract
The current intelligent driving technology based on image data is being widely used. However, the analysis of traffic accidents occurred in intelligent driving vehicles shows that there is an explanatory difference between the intelligent driving system based on image data and the driver’s understanding of the target information in the image. In addition, driving behavior is the driver’s response based on the analysis of road information, which is not available in the current intelligent driving system. In order to solve this problem, our paper proposes a driver attention area extraction method based on deep network feature visualization. In our method, we construct a Driver Behavior Information Network (DBIN) to map the relation between image information and driving behavior. Then we use the Deep Network Feature Visualization method (DNFV) to determine the driver’s attention area. The experimental results show that our method can extract effective road information from a real traffic scene picture and obtain the driver’s attention area. Our method can provide a useful theoretical basis and related technology of visual perception for future intelligent driving systems, driving training and assisted driving systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Visual Attention and Saliency Detection · Autonomous Vehicle Technology and Safety · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
References (27)
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