高自动化车辆中乘客危险感知的脑电驱动解码框架
EEG-Driven Decoding Framework for Passenger Hazard Perception in Highly Automated Vehicles
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中文总结 AI 辅助
本研究提出基于脑电的乘客危险感知解码框架，通过3D-CRNN模型实现风险预测与危险识别，显著提升准确率，为自动驾驶决策提供辅助监督。
中文摘要 AI 辅助
可靠的风险评估仍然是自动驾驶汽车（AVs）面临的核心挑战。尽管自动化技术取得了进步，乘客认知提供了一种非侵入性的辅助信号，无需主动的人类干预即可提高客观和感知安全性。我们引入了一种基于脑电图（EEG）的脑机接口（BCI），用于解码乘客的神经反应，以进行风险预测（RP）和危险识别（DI），明确地将人类建模为乘客，以匹配真实世界的自动驾驶汽车使用场景。为实现这一目标，我们提出了乘客认知模型（PCM）、风险感知序列标注（RSL）和乘客脑电解码策略（PEDS），该策略集成了一个3D卷积循环神经网络（3D-CRNN）模型用于联合脑电解码。实验结果表明，3D-CRNN在RP中实现了$95.3\\% \pm 2.7\\%$的平衡准确率（BA），并通过RSL将单受试者DI从$80.9\\% \pm 3.9\\%$提高到$85.0\\% \pm 3.2\\%$。事件级分析进一步表明，3D-CRNN在RP和DI的不同事件类型中始终优于其他模型。在泛化实验中，3D-CRNN在跨会话DI中实现了$77.0\\% \pm 5.3\\%$的BA，在跨受试者评估中，对已见受试者实现了$77.4\\% \pm 1.1\\%$的BA，同时对未见受试者保持了$64.9\\% \pm 8.5\\%$的BA，展示了在受试者内和受试者间变异性上的良好泛化性和可迁移性。这些发现建立了一个用于自动驾驶汽车乘客危险感知的脑电解码框架，并表明乘客认知信号可以为未来自动驾驶汽车决策和预期功能安全（SOTIF）支持提供辅助监督。
英文摘要
Reliable risk assessment remains a central challenge for Autonomous Vehicles (AVs). Despite advances in automation, passenger cognition provides a non-intrusive auxiliary signal that improves both objective and perceived safety without requiring active human intervention. We introduce an Electroencephalogram (EEG)-based Brain-Computer Interface (BCI) that decodes passenger neural responses for both Risk Prediction (RP) and Danger Identification (DI), explicitly modeling humans as passengers to match real-world AV use. To achieve this, we propose the Passenger Cognitive Model (PCM), Risk-aware Sequential Labeling (RSL), and the Passenger EEG Decoding Strategy (PEDS), which integrates a 3D Convolutional Recurrent Neural Network (3D-CRNN) model for joint EEG decoding. Experimental results show that 3D-CRNN achieves a Balanced Accuracy (BA) of $95.3\% \pm 2.7\%$ in RP and improves single-subject DI from $80.9\% \pm 3.9\%$ to $85.0\% \pm 3.2\%$ with RSL. Event-wise analyses further show that 3D-CRNN consistently outperforms other models across different event types in RP and DI. In generalization experiments, 3D-CRNN achieves $77.0\% \pm 5.3\%$ BA in cross-session DI and $77.4\% \pm 1.1\%$ BA on seen subjects in cross-subject evaluation, while maintaining a $64.9\% \pm 8.5\%$ BA on unseen subjects, demonstrating promising generalizability and transferability across both intra-subject and inter-subject variability. These findings establish an Electroencephalogram (EEG) decoding framework for AV passenger hazard perception and suggest that passenger cognitive signals can provide auxiliary supervision for future AV decision-making and Safety of the Intended Functionality (SOTIF) support.
发表机构
- School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）
- Department of Electrical and Electronic Engineering, Imperial College London（帝国理工学院电气与电子工程系）
- School of Medicine, Tsinghua University（清华大学医学院）
- School of Automation Engineering, University of Electronic Science and Technology of China（电子科技大学自动化工程学院）
- State Key Laboratory of Automotive Simulation and Control, Jilin University（吉林大学汽车仿真与控制国家重点实验室）
- College of Information Sciences and Technology, Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。
