P5: Sustainable Safe Transportation Project with Smart City Elements and Smart Vehicles | SUIT | Sustainable Urbanization Through Innovative Technologies

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Principal Investigator: Dr. Ahmet Saracoğlu

Institution(s): Kuartis (Leader), METU

Strategic Target Name: Developing a multi-sensor sensing, detection and tracking platform for autonomous systems, developing low-latency communication methods that are resistant to cyber-attacks in autonomous systems, developing electric public transportation, safe intersection and smart road technologies for safe transportation with a low carbon footprint, and conducting pilot applications by 2025.

Scope of Activity of the Project: Safety in the Autonomous Driving Environment in the City, Secure and Low Latency Communication for V2X in Autonomous Vehicles, Development of Autonomous Vehicle and Smart Transportation Integration Field.

Project Summary: Our project aims to make transportation in cities more efficient and safer by enabling autonomous vehicles to communicate with each other and smart intersections using safe and low-latency V2V/V2X communication technologies. Within the scope of the project, it is aimed to create environmental perception and awareness by combining information obtained from sensors on autonomous vehicles and smart city elements, interpreted with advanced algorithms and shared between stakeholders with V2V / V2X methods. In this way, many usage scenarios will be realized, from reducing fuel consumption and emissions to ensuring increased pedestrian and passenger safety.

In this project, the sensor data that the vehicles transmit to each other and to the infrastructure will be processed using innovative target tracking algorithms that are ahead of classical approaches, and a larger common picture will be created by combining this processed information. In this way, it is aimed for vehicles to have awareness and predictions about traffic beyond their field of vision. By selecting infrastructure sensors with different and complementary types and capabilities than those on the vehicle, highly accurate and precise estimates of the common picture can be produced.

Thanks to the technologies to be developed in the project, it will be possible to create a high level of environmental perception and awareness, reduce carbon emissions by increasing traffic efficiency, and increase both in-vehicle passenger safety and the safety of other traffic stakeholders.

Project Team:

Dr. Osman Serdar Gedik - Kuartis - Researcher

Research Areas: Sensor fusion and tracking

Botan Kaya - Kuartis - Employee (Project Management)

Alper Küçükkömürler - Kuartis - Employee

Dr. Kamil Berker Loğoğlu - Kuartis - Researcher

Research Areas: Computer vision and machine learning

Eren Şener - Kuartis - Researcher

Research Areas: Computer vision and machine learning

Hilal Köksal - Kuartis - Researcher (Computer vision and machine learning)

Murat Kumru - Kuartis - Researcher

Research Areas: Sensor fusion, tracking and prediction