Advanced driver assistance systems using EEG
Organization
Software Engineering and Information Systems
Abstract
Summary:
Establishing of a state of the art on use of brain signals for advanced driver assistance systems (ADAS), and devising such a system using EEG.
Description:
Context: Advanced driver assistance systems (ADAS) have been investigates by researchers and industry alike, for a variety of applications ranging from assisted breaking to drowsiness detection.
Details: The project aims to:
1.\tEstablish a state of the art for brain-signal based ADAS.
2.\tDevise an ADAS system using EEG brain signals for applications such as (but not limited to) drowsiness detection, alertness etc.
Some relevant literature:
[1] Adnan Shaout, Dominic Colella, S. Awad, “Advanced Driver Assistance Systems – Past, Present and Future” (https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6153935&tag=1)
[2] Arun Sahayadhas, Kenneth Sundaraj and Murugappan Murugappan, “Detecting Driver Drowsiness Based on Sensors: A Review”, Journal: sensors, MDPI, 2012 (https://www.mdpi.com/1424-8220/12/12/16937/htm )
[3] Jain, A.; Koppula, H.S.; Raghavan, B.; Soh, S.; Saxena, A., "Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models". In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 13–16 December 2015.
[4] Yar M. Mughal, “A Parametric Framework for Modelling of Bioelectrical Signals”, Book, ISBN 978-981-287-969-1, Springer, 2015
[5] Ziebinski,Adam and Cupek,Rafal and Grzechca,Damian and Chruszczyk,Lukas, “Review of advanced driver assistance systems (ADAS)”, AIP Conference Proceedings, 2017.
Establishing of a state of the art on use of brain signals for advanced driver assistance systems (ADAS), and devising such a system using EEG.
Description:
Context: Advanced driver assistance systems (ADAS) have been investigates by researchers and industry alike, for a variety of applications ranging from assisted breaking to drowsiness detection.
Details: The project aims to:
1.\tEstablish a state of the art for brain-signal based ADAS.
2.\tDevise an ADAS system using EEG brain signals for applications such as (but not limited to) drowsiness detection, alertness etc.
Some relevant literature:
[1] Adnan Shaout, Dominic Colella, S. Awad, “Advanced Driver Assistance Systems – Past, Present and Future” (https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6153935&tag=1)
[2] Arun Sahayadhas, Kenneth Sundaraj and Murugappan Murugappan, “Detecting Driver Drowsiness Based on Sensors: A Review”, Journal: sensors, MDPI, 2012 (https://www.mdpi.com/1424-8220/12/12/16937/htm )
[3] Jain, A.; Koppula, H.S.; Raghavan, B.; Soh, S.; Saxena, A., "Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models". In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 13–16 December 2015.
[4] Yar M. Mughal, “A Parametric Framework for Modelling of Bioelectrical Signals”, Book, ISBN 978-981-287-969-1, Springer, 2015
[5] Ziebinski,Adam and Cupek,Rafal and Grzechca,Damian and Chruszczyk,Lukas, “Review of advanced driver assistance systems (ADAS)”, AIP Conference Proceedings, 2017.
Graduation Theses defence year
2019-2020
Supervisor
Yar Muhammad
Spoken language (s)
English
Requirements for candidates
Level
Bachelor, Masters
Application of contact
Name
Yar Muhammad
Phone
E-mail