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<h2>Abstract</h2> <p><strong>PassengerEEG</strong> is a brain-signal dataset designed to study how human passengers perceive and cognitively respond to potential traffic hazards in highly automated vehicles (AVs). As AVs increasingly replace human drivers, understanding passenger cognition becomes essential for improving vehicle safety and adaptive decision-making.</p > <p>The dataset was collected in a controlled driving simulation lab, where participants passively observed first-person traffic video clips rendered using the Virtual Test Drive (VTD) platform. The traffic scenarios include diverse real-world events such as pedestrian crossings, vehicle cut-ins, and emergency braking. Throughout the sessions, participants’ brain activity was recorded using EEG, capturing their implicit cognitive responses without requiring any manual action, except for spacebar presses at designated auditory cues to maintain attention.</p > <p>The experiment consisted of four sessions per participant, conducted across multiple days. Each session included four video clips (~15 minutes each), with each clip containing 25 traffic events interleaved with safe intervals. In total, the dataset comprises approximately <strong>4 hours of EEG recordings per participant</strong> and around <strong>400 event-related EEG segments</strong>.</p > <p>Each event-specific EEG segment is 8 seconds long: 2 seconds serve as baseline, followed by 6 seconds capturing the neural activity around the traffic event. Segments are labeled based on their temporal relevance for tasks such as <em>Danger Identification</em> (purple), <em>Risk Prediction</em> (blue), and <em>Safe Monitoring</em> (green), enabling multi-granularity decoding of passenger cognition.</p > <p><strong>PassengerEEG</strong> is the first dataset to focus on <em>passengers</em> rather than drivers, in the context of EEG-based traffic hazard perception, and supports research in passive brain-computer interfaces (BCIs), human-AV interaction, and neural decoding in real-world traffic scenarios.</p >   <h3>Traffic Event Details</h3>   <table border="1" cellpadding="6" cellspacing="0" style="border-collapse: collapse; font-size: small;">     <thead style="background-color: #f2f2f2;">       <tr>         <th>ID</th>         <th>Type</th>         <th>Action</th>         <th>Dir.<sup>†</sup></th>         <th>Dist.</th>         <th>Risk</th>       </tr>     </thead>     <tbody>       <tr><td>1</td><td>Pedestrian</td><td>Cross</td><td>L</td><td>N/A</td><td>High</td></tr>       <tr><td>2</td><td>Pedestrian</td><td>Cross</td><td>R</td><td>N/A</td><td>High</td></tr>       <tr><td>3</td><td>Pedestrian</td><td>Stand</td><td>L</td><td>N/A</td><td>Low</td></tr>       <tr><td>4</td><td>Pedestrian</td><td>Stand</td><td>R</td><td>N/A</td><td>Low</td></tr>       <tr><td>5</td><td>Vehicle</td><td>Non-Cut-in</td><td>L</td><td>N/A</td><td>Low</td></tr>       <tr><td>6</td><td>Vehicle</td><td>Non-Cut-in</td><td>R</td><td>N/A</td><td>Low</td></tr>       <tr><td>7</td><td>Vehicle</td><td>Cut-in</td><td>L</td><td>Close</td><td>High</td></tr>       <tr><td>8</td><td>Vehicle</td><td>Cut-in</td><td>R</td><td>Close</td><td>High</td></tr>       <tr><td>9</td><td>Vehicle</td><td>Cut-in</td><td>L</td><td>Far</td><td>High</td></tr>       <tr><td>10</td><td>Vehicle</td><td>Cut-in</td><td>R</td><td>Far</td><td>High</td></tr>       <tr><td>11</td><td>Vehicle</td><td>Cut-out w/o EB<sup>‡</sup></td><td>F</td><td>Close</td><td>Low</td></tr>       <tr><td>12</td><td>Vehicle</td><td>Cut-out w/o EB</td><td>F</td><td>Far</td><td>Low</td></tr>       <tr><td>13</td><td>Vehicle</td><td>Cut-out w/ EB</td><td>F</td><td>Close</td><td>High</td></tr>       <tr><td>14</td><td>Vehicle</td><td>Cut-out w/ EB</td><td>F</td><td>Far</td><td>High</td></tr>     </tbody>   </table>       <p style="font-size: small;">     <sup>†</sup> Dir.: L = Left, R = Right, F = Front. <br>     <sup>‡</sup> EB stands for Emergency Braking.   </p >

<h2>摘要</h2> <p><strong>乘客脑电数据集（PassengerEEG）</strong>是一款脑信号数据集，旨在研究人类乘客在高度自动驾驶汽车（AVs）中对潜在交通危险的感知与认知反应。随着自动驾驶汽车逐步替代人类驾驶员，理解乘客的认知状态对于提升车辆安全性与自适应决策能力至关重要。</p> <p>本数据集采集于受控驾驶模拟实验室，实验中受试者被动观看由虚拟测试驾驶（Virtual Test Drive, VTD）平台渲染的第一人称交通视频片段。交通场景涵盖多种真实世界事件，包括行人横穿、车辆切入以及紧急制动等。实验过程中，研究人员通过脑电（EEG）设备记录受试者的脑活动，捕捉其隐性认知反应，受试者仅需在指定听觉提示下按下空格键以维持注意力，无需执行其他手动操作。</p> <p>每位受试者需完成多日内的四次实验环节。每个实验环节包含四段视频片段（每段时长约15分钟），每段视频中穿插25个交通事件与安全间隔时段。整体而言，本数据集每位受试者的脑电记录时长约<strong>4小时</strong>，包含约<strong>400个事件相关脑电片段</strong>。</p> <p>每个事件专属的脑电片段时长为8秒：前2秒作为基线时段，后续6秒用于捕捉交通事件发生前后的神经活动。脑电片段根据其与任务的时间相关性进行标注，包括<em>危险识别</em>（紫色）、<em>风险预测</em>（蓝色）以及<em>安全监控</em>（绿色），支持对乘客认知进行多粒度解码。</p> <p><strong>乘客脑电数据集（PassengerEEG）</strong>是首个在基于脑电的交通危险感知研究中以<em>乘客</em>而非驾驶员为研究对象的数据集，可用于被动脑机接口（BCIs）、人类-自动驾驶汽车交互以及真实交通场景下的神经解码等相关研究。</p>    <h3>交通事件详情</h3>   <table border="1" cellpadding="6" cellspacing="0" style="border-collapse: collapse; font-size: small;">    <thead style="background-color: #f2f2f2;">    <tr>    <th>编号</th>    <th>类型</th>    <th>动作</th>    <th>方向<sup>†</sup></th>    <th>距离</th>    <th>风险等级</th>    </tr>    </thead>    <tbody>    <tr><td>1</td><td>行人</td><td>横穿</td><td>L</td><td>N/A</td><td>高</td></tr>    <tr><td>2</td><td>行人</td><td>横穿</td><td>R</td><td>N/A</td><td>高</td></tr>    <tr><td>3</td><td>行人</td><td>站立</td><td>L</td><td>N/A</td><td>低</td></tr>    <tr><td>4</td><td>行人</td><td>站立</td><td>R</td><td>N/A</td><td>低</td></tr>    <tr><td>5</td><td>车辆</td><td>非切入</td><td>L</td><td>N/A</td><td>低</td></tr>    <tr><td>6</td><td>车辆</td><td>非切入</td><td>R</td><td>N/A</td><td>低</td></tr>    <tr><td>7</td><td>车辆</td><td>切入</td><td>L</td><td>近距离</td><td>高</td></tr>    <tr><td>8</td><td>车辆</td><td>切入</td><td>R</td><td>近距离</td><td>高</td></tr>    <tr><td>9</td><td>车辆</td><td>切入</td><td>L</td><td>远距离</td><td>高</td></tr>    <tr><td>10</td><td>车辆</td><td>切入</td><td>R</td><td>远距离</td><td>高</td></tr>    <tr><td>11</td><td>车辆</td><td>无紧急制动的驶出</td><td>F</td><td>近距离</td><td>低</td></tr>    <tr><td>12</td><td>车辆</td><td>无紧急制动的驶出</td><td>F</td><td>远距离</td><td>低</td></tr>    <tr><td>13</td><td>车辆</td><td>带紧急制动的驶出</td><td>F</td><td>近距离</td><td>高</td></tr>    <tr><td>14</td><td>车辆</td><td>带紧急制动的驶出</td><td>F</td><td>远距离</td><td>高</td></tr>    </tbody>   </table>       <p style="font-size: small;">    <sup>†</sup> 方向说明：L=左（Left），R=右（Right），F=前（Front）。<br>    <sup>‡</sup> EB代表紧急制动（Emergency Braking）。   </p>

应用场景：

PassengerEEG: An EEG Dataset for Passenger Hazard Perception in AVs