Flow sensing for applications in autonomous driving
Organization
Autonomous Driving Lab
Abstract
Context: The usual sensing modalities that are available to autonomous vehicles i.e. vision, radar and lidar have their limitations. For example, none of them is very suitable for estimating the length of a leading vehicle. Additional sensing modalities, such as wind flow sensing, might have the potential to meaningfully complement the usual sensing modalities in autonomous driving.
Description: You will build upon the innovative work done on flow-sensing applications in autonomous driving by Roman Matvejev [1] and Matis Ottan [2]. There are multiple directions that can be investigated. For example, (i) working in simulations and proposing new applications of flow sensing in autonomous driving, (ii) investigating new feature extraction and classification/regression methods etc. or (iii) expanding out of simulations with physical validation of flow sensing in autonomous driving.
The topic has a significantly high level of novelty. Both the previous bachelor thesis works [1, 2] resulted in conference publications [3, 4].
References:
[1] Roman Matvejev, “Air-flow sensing for applications in autonomous driving”, Bachelor thesis, University of Tartu, 2021. Available at: https://comserv.cs.ut.ee/ati_thesis/datasheet.php?id=71873&year=2021
[2] Matis Ottan, “Leading Vehicle Length Estimation Using Pressure Data for Use in Autonomous Driving”, Bachelor thesis, University of Tartu, 2022. Available at: https://comserv.cs.ut.ee/ati_thesis/datasheet.php?id=74510&year=2022
[3] Roman Matvejev, Yar Muhammad, Naveed Muhammad, "Air-flow sensing for vehicle length estimation in autonomous driving applications", International Conference on Emerging Technologies and Factory Automation (ETFA), Sweden, 2021.
[4] Matis Ottan, Naveed Muhammad, "Leading Vehicle Length Estimation Using Pressure Data for Use in Autonomous Driving", International Conference on Emerging Technologies and Factory Automation (ETFA), Germany, 2022.
Description: You will build upon the innovative work done on flow-sensing applications in autonomous driving by Roman Matvejev [1] and Matis Ottan [2]. There are multiple directions that can be investigated. For example, (i) working in simulations and proposing new applications of flow sensing in autonomous driving, (ii) investigating new feature extraction and classification/regression methods etc. or (iii) expanding out of simulations with physical validation of flow sensing in autonomous driving.
The topic has a significantly high level of novelty. Both the previous bachelor thesis works [1, 2] resulted in conference publications [3, 4].
References:
[1] Roman Matvejev, “Air-flow sensing for applications in autonomous driving”, Bachelor thesis, University of Tartu, 2021. Available at: https://comserv.cs.ut.ee/ati_thesis/datasheet.php?id=71873&year=2021
[2] Matis Ottan, “Leading Vehicle Length Estimation Using Pressure Data for Use in Autonomous Driving”, Bachelor thesis, University of Tartu, 2022. Available at: https://comserv.cs.ut.ee/ati_thesis/datasheet.php?id=74510&year=2022
[3] Roman Matvejev, Yar Muhammad, Naveed Muhammad, "Air-flow sensing for vehicle length estimation in autonomous driving applications", International Conference on Emerging Technologies and Factory Automation (ETFA), Sweden, 2021.
[4] Matis Ottan, Naveed Muhammad, "Leading Vehicle Length Estimation Using Pressure Data for Use in Autonomous Driving", International Conference on Emerging Technologies and Factory Automation (ETFA), Germany, 2022.
Graduation Theses defence year
2023-2024
Supervisor
Naveed Muhammad
Spoken language (s)
English
Requirements for candidates
Level
Bachelor, Masters
Keywords
Application of contact
Name
Naveed Muhammad
Phone
E-mail