https://orcid.org/0000-0002-1018-518X
https://orcid.org/0000-0001-5123-4681
https://orcid.org/0000-0003-4795-8569
https://orcid.org/0000-0002-1018-518X
https://orcid.org/0000-0001-5123-4681
https://orcid.org/0000-0003-4795-8569
The ability to sense and understanding the driving environment is a key technology for ADAS and autonomous driving. Human drivers have to pay more visual attention to important or target elements and ignore unnecessary ones present in their field of sight. A model that computes this visual attention of targets in a specific driving environment is essential and useful in supporting autonomous driving, object-specific tracking & detection, driving training, car collision warning, traffic sign detection, etc. In this paper, we propose a new framework of visual attention that can predict important objects in the driving scene using a conditional generative adversarial network. A large scale Visual Attention Driving Database (VADD) of saliency heat-maps is built from existing driving datasets using a saliency mechanism. The proposed framework model takes its strength from these saliency heat-maps as conditioning label variables. The results show that the proposed approach makes us able to predict heat-maps of most important objects in a driving environment.
Saliency Heat-Map as Visual Attention for Autonomous Driving Using Generative Adversarial Network (GAN)
IEEE Transactions on Intelligent Transportation Systems ; 23 , 6 ; 5360-5373
2022-06-01
25085654 byte
Article (Journal)
Electronic Resource
English
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