用人类注视数据指导自动驾驶决策，让机器更像人思考。
AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems
- 用眼动数据训练模型预测人类关注区域
- 在6种场景中采集120万帧数据构建注意力模型
- 提升自动驾驶系统对关键信息的识别能力
近年来，提升自主智能车辆（AIVs）的决策能力成为研究热点。尽管取得进展，但如何让机器像人类一样感知和推理，准确捕捉感兴趣区域，仍是重大挑战。本文提出一种新框架——基于人类注意力的可解释引导系统（AEGIS），利用眼动追踪获取的人类注意力数据，指导强化学习（RL）模型识别决策中的关键区域。通过在6种场景下对20名参与者采集总计120万帧数据，AEGIS预训练了一个能够预测人类注意力模式的模型，从而为自动驾驶系统提供可解释的决策引导。
原文摘要 · Abstract (English)
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.
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