EEG-Driven Decoding Framework for Passenger Hazard Perception in Highly Automated Vehicles
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 ...
| Authors | Yingkai Yang, Ashton Yu Xuan Tan, Bowen Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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