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

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.

Creator: 

Fu, Junwen

Date: 

2022

Abstract: 

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.

Subject: 

Engineering - Biomedical
Engineering - Electronics and Electrical
Artificial Intelligence

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Applied Science: 
M.App.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).