AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917980718235648 |
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| author | Zhuang, Zhuoli Lu, Cheng-You Chang, Yu-Cheng Fred Wang, Yu-Kai Do, Thomas Lin, Chin-Teng |
| author_facet | Zhuang, Zhuoli Lu, Cheng-You Chang, Yu-Cheng Fred Wang, Yu-Kai Do, Thomas Lin, Chin-Teng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05950 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems Zhuang, Zhuoli Lu, Cheng-You Chang, Yu-Cheng Fred Wang, Yu-Kai Do, Thomas Lin, Chin-Teng Artificial Intelligence 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. |
| title | AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.05950 |