在低风险认知状态下，驾驶员的注意力主要集中在前方区域和后视镜上，注视和扫视频率较低，注视点在各区域间循环转移，扫视路径呈现出明显的规律性，如图11所示。
图11 眼动轨迹与热点图（低风险认知状态）
在高风险认知状态下，驾驶员往往倾向于将注意力集中在与自车交互的周围车辆上。对应有更高的注视和扫视频率，并呈现出重复注视和来回扫视的特点，如图12所示。
图12 眼动轨迹与热点图（高风险认知状态）
5、总结与展望
针对驾驶数据集缺少与人类认知相关信息的问题，研究采用驾驶模拟器实验的方法构建RISEE数据集，提供自然驾驶场景、人类主观风险认知及眼动等数据。
对场景主客观行驶风险及眼动特征的分析表明，RISEE数据集涵盖各种交互类型的数据，在决策规划系统的研发和测试中具有潜在的应用价值。具体应用包括：
基于风险认知的决策规划：行驶过程中，驾驶员通过视觉感知周围环境，并在评估各类环境信息后形成认知风险。因此，眼动数据可用于识别影响人类风险认知的关键环境因素，揭示风险认知的内在规律，从而帮助决策规划系统发出与人类认知相协调的决策指令。智能驾驶舒适性评估：在高等级智能驾驶汽车中，驾驶员角色发生了变化，对舒适性的评价也不再局限于车辆本身，而是会受到认知风险的影响[1]。因此，可以基于RISEE探究眼动信息与驾驶员主观认知风险之间的关系，评估高等级智能驾驶的舒适性。
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责编丨高炳钊
来源：同济智能汽车研究所
