DC3. AI for road safety in LMICs

Amirhossein Hassani

Education
M.Sc. in Computer Engineering – Artificial Intelligence and Robotics from Shahid Bahonar University of Kerman
About
Pioneering AI researcher specializing in computer vision, with extensive expertise in machine learning and deep learning. Adept at developing and implementing a wide range of AI and ML models, with particular proficiency in convolutional neural networks for visual recognition tasks. Driven by a passion for leveraging cutting-edge AI technologies to tackle complex real-world challenges, consistently pushing the boundaries of what’s possible in artificial intelligence.

Hosts: FPZ & AGILYSIS

Objectives:

  • Define a framework for exploiting available data sources and adapting existing road survey databases (e.g., SLAIN) inLMICs
  • To perform AI-supported knowledge discovery in databases utilising extraction of implicit, previously unknown, and potentially useful
  • information from data collected for other purposes
  • Facilitate transport modelling with AI deep learning to establish predictive crash models to support transport infrastructure investment
  • decisions in LMIC on road networks where traditional data is non-existing or unreliable

Expected results:

  • Identification and classification of potential structured and unstructured big data sources for the development of road safety
  • performance assessments and predictive crash modelling
  • Innovative and cost-effective data acquisition, processing and analysis methods to support road safety management in
  • LMIC (performance indicators, star rating, risk mapping, road safety audit and road safety inspection)
  • A taxonomy of data, methods and tools tailored for AI road safety support to less resourced countries

Planned secondment(s):

  • EIRA-SI, Supervisor: Purpose: to test the developed methods on datasets from LMICs