CUEING: a lightweight model to Capture hUman attEntion In driviNG
- URL: http://arxiv.org/abs/2305.15710v2
- Date: Fri, 13 Oct 2023 07:04:13 GMT
- Title: CUEING: a lightweight model to Capture hUman attEntion In driviNG
- Authors: Linfeng Liang, Yao Deng, Yang Zhang, Jianchao Lu, Chen Wang, Quanzheng
Sheng, Xi Zheng
- Abstract summary: We propose a novel adaptive cleansing technique for purging noise from existing gaze datasets, coupled with a robust, lightweight convolutional self-attention gaze prediction model.
Our approach not only significantly enhances model generalizability and performance by up to 12.13% but also ensures a remarkable reduction in model complexity by up to 98.2% compared to the state-of-the art.
- Score: 6.310770791023399
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Discrepancies in decision-making between Autonomous Driving Systems (ADS) and
human drivers underscore the need for intuitive human gaze predictors to bridge
this gap, thereby improving user trust and experience. Existing gaze datasets,
despite their value, suffer from noise that hampers effective training.
Furthermore, current gaze prediction models exhibit inconsistency across
diverse scenarios and demand substantial computational resources, restricting
their on-board deployment in autonomous vehicles. We propose a novel adaptive
cleansing technique for purging noise from existing gaze datasets, coupled with
a robust, lightweight convolutional self-attention gaze prediction model. Our
approach not only significantly enhances model generalizability and performance
by up to 12.13% but also ensures a remarkable reduction in model complexity by
up to 98.2% compared to the state-of-the art, making in-vehicle deployment
feasible to augment ADS decision visualization and performance.
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