DC12. Harmonisation and hybrid application of AI datasets for road safety

Muhammad Shahid

Education
M.Eng in Computer Science and Technology from Hefei University of Technology, with focus on Machine Learning and Computer Vision.
About
A passionate researcher specializing in Machine Learning and Computer Vision. Love developing and implementing advanced AI solutions to solve day-to-day problems. Enjoys translating complex technical concepts into practical applications, working in dynamic and fast-paced environments. Strongly believes in teamwork and collaborations with people from diverse backgrounds for more robust and scalable growth.

Hosts: FPZ & iRAP

Objectives:

To create a taxonomy for classifying datasets generated by AI algorithms applied to data from various sources (e.g., Lidar, 360 imagery, geo-spatial, telematics, satellite imagery and traffic management) and assess their transferability
To create a new methodology for harmonising datasets generated by both AI algorithms and manual approaches
To develop new hybrid road safety assessment models that effectively combine the advantages of various data sources, for determining the road crash occurrence risks and the risks of serious injuries to motorised road users and VRUs

Expected results:

Knowledge and methodologies for the alignment and harmonisation of datasets generated by AI algorithms from various sources
New AI models for extracting road and traffic information from various geo-referenced sources and their correlation with risk rates
Application of the methodology in an appropriate case study or application compatible with the mission and activities of iRAP

Planned secondment(s):

EIRA-SI, Purpose: to test the developed methods on additional datasets