Decoding Passenger’s Brain Signals to Detect and Analyze Emergency Road Events

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  • In this thesis, passengers' brain signals, including electroencephalography (EEG) and near-infrared spectroscopy (fNIRS), were analyzed to extract road information to potentially prevent car accidents and provide public trust in high-level autonomous vehicles. For the EEG part, event-related potential (ERP) and machine learning techniques were used to analyze and classify the signals of two road events. Results show that the responses are 454 ± 234 ms before the reaction, and the average recognition accuracy of the regularized linear discriminant analysis (RLDA) classifier reached 95.81%. For the fNIRS part, a quantification method, which is based on cerebral oxygen exchange in the prefrontal cortex of passengers and a risk field is introduced. We also verified our findings in a real-car automatic emergency braking and cut-in experiment. Overall, the results illustrate that EEG-based human-centric assistant driving systems have the potential of being deployed in autonomous vehicles to enhance the safety of passengers.

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  • Copyright © 2022 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
Date Created
  • 2022


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