AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems

Improving decision-making capabilities in Autonomous Intelligent Vehicles (AIVs) has been a heated topic in recent years. Despite advancements, training machines to capture regions of interest for comprehensive scene understanding, like human perception and reasoning, remains a significant challenge. This study introduces a novel framework, Human Attention-based Explainable Guidance for Intelligent Vehicle Systems (AEGIS). AEGIS utilizes human attention, converted from eye-tracking, to guide reinforcement learning (RL) models to identify critical regions of interest for decision-making. AEGIS uses a pre-trained human attention model to guide RL models to identify critical regions of interest for decision-making. By collecting 1.2 million frames from 20 participants across six scenarios, AEGIS pre-trains a model to predict human attention patterns.

@article{zhuang2025_2504.05950,
  title={ AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems },
  author={ Zhuoli Zhuang and Cheng-You Lu and Yu-Cheng Fred Chang and Yu-Kai Wang and Thomas Do and Chin-Teng Lin },
  journal={arXiv preprint arXiv:2504.05950},
  year={ 2025 }
}