Rethinking vehicle autonomy with full infrastructural support and connectivity to road agents
Organization
Autonomous Driving Lab
Abstract
Autonomous vehicles research has progressed rapidly during the last decade, but challenges remain in their wide-spread deployment. Modular as well as end-to-end [1] approaches to vehicle autonomy, alike, suffer from the limitation of not being able to cover edge cases that arise in everyday traffic. At the same time, it is also believed that more and more infrastructural support and interconnectivity between vehicles will be available to autonomous vehicles of the future over time.
In this project, you will have the possibility to propose an autonomous-driving system which has access to as much information from infrastructural sensors and other road agents (including, but not limited to, vehicles, pedestrians, bicycles etc.) as needed to be fail safe in all (usual as well as edge) situations. After a thorough literature review of the open challenges in vehicle autonomy you will investigate methods for meeting those challenges given the connectivity with infrastructure and road agents. You will then validate the proposed system in simulation.
The results will also potentially be published in the form of a research paper.
Reference:
[1] Ardi Tampuu, Tambet Matiisen, Maksym Semikin, Dmytro Fishman, Naveed Muhammad, "A Survey of End-to-End Driving: Architectures and Training Methods," in IEEE Transactions on Neural Networks and Learning Systems, 2021.
In this project, you will have the possibility to propose an autonomous-driving system which has access to as much information from infrastructural sensors and other road agents (including, but not limited to, vehicles, pedestrians, bicycles etc.) as needed to be fail safe in all (usual as well as edge) situations. After a thorough literature review of the open challenges in vehicle autonomy you will investigate methods for meeting those challenges given the connectivity with infrastructure and road agents. You will then validate the proposed system in simulation.
The results will also potentially be published in the form of a research paper.
Reference:
[1] Ardi Tampuu, Tambet Matiisen, Maksym Semikin, Dmytro Fishman, Naveed Muhammad, "A Survey of End-to-End Driving: Architectures and Training Methods," in IEEE Transactions on Neural Networks and Learning Systems, 2021.
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