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Session

Case Study

Monday, June 16

11:50 AM - 12:15 PM

Live in Berlin

Less Details

In the pursuit of achieving Level 3 automated driving, the necessity for a driver’s constant availability to resume control remains crucial. Addressing this, an in-cabin smart system must effectively monitor and interpret the driver’s readiness. Current challenges include the accuracy of driver monitoring systems (DMS) in gauging attentiveness solely through eye gaze or steering wheel sensing. This may not be sufficient to assess the driver’s level of situational awareness. With a focus on multi-modal data fusion and deep learning models for simultaneous evaluation of in-cabin data and traffic scenes, these challenges can be tackled. By integrating in-cabin sensors and considering human factors, the model aims to revolutionize DMS enablers for a seamless transition between automated and manual driving. This presentation will also showcase research that emphasizes that assessing driver readiness requires a comprehensive approach beyond traditional methods, offering a promising solution to enhance the safety and efficiency of automated vehicle operation.

Discover more about:

  • The critical need for accurate driver monitoring
  • The challenges in current driver monitoring systems
  • Multi-modal fusion and deep learning
  • The role of human factor research in improving AI-based DMS
  • The emergence of vision language models in DMS
Presentation

Speaker

Dr. Mahdi Rezaei

Associate Professor in Machine Learning and Computer Vision, University of Leeds

Dr. Madhi Rezaei is an Associate Professor at the University of Leeds, specializing in Computer Vision and Autonomous Vehicles. With over 15 years of experience in both academia and industry, he earned his Ph.D. from the University of Auckland and several awards and publications in prestigious venues. Dr. Rezaei is a recognized expert in computer science, focusing on areas like computer vision, AI, machine learning, and autonomous vehicles. Currently, he is establishing a new computer vision-based research group at Leeds and welcomes motivated PhD students, postdocs, and visiting researchers to join in developing smart and safe vehicle technologies.

Company

University of Leeds

Leeds stands among the UK's top ten universities for research power, boasting academic breadth and a commitment to quality that fosters extraordinary outcomes. With a student body exceeding 34,000, a staff of over 7,000, and a global alumni network surpassing 240,000, Leeds is a hub for knowledge creation, dissemination through exceptional education, and impactful application in society, culture, and the economy. At the core of our strategy is the seamless integration of research, learning, and teaching. Our courses, delivered by staff actively engaged in world-class research and professional practice, reflect our dedication to providing an enriching academic experience. With strong industry connections and an impressive entrepreneurial track record, including launching more AIM stock market spin-out companies than any other UK university, Leeds drives innovation and real-world impact.

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