Human-Centered AI for Autonomous Vehicles

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Human-Centered AI for Autonomous Vehicles

Intelligent/autonomous vehicles, such as self-driving cars, intelligent robots and Unmanned Aerial Vehicles (UAVs) must seamlessly interact with humans, e.g.,  their drivers/operators/pilots or people in their vicinity, whether being obstacles to be avoided (e.g., pedestrians) or targets to be followed and interact with (e.g., when filming a performing athelete). Furthermore, intelligent vehicles and robots have been increasingly employed to assist humans in real-world applications (e.g., for , autonomous transportation, warehouse logistics, or infrastructure inspection) To this end, autonomous vehicles should be equipped with advanced vision systems that allow them to understand and interact with humans in their surrounding environment.

This lecture overviews human-centric AI methods that can be utilized to facilitate visual interaction between humans and autonomous vehicles (e.g., through gestures captured by RGB cameras), in order to ensure their safe and successful cooperation in real-world scenarios. Such methods should: a) demonstrate increased visual perception accuracy to understand human visual cues, b) be robust to input data variations, in order to successfully handle illumination/background/scale changes that are typically encountered in real-world scenarios, and c) produce timely predictions to ensure safety, which is a critical aspect of autonomous vehicles’ applications. Deep learning and neural networks play an important role towards this end, covering the following topics: a) human pose/posture estimation from RGB images, b) human action/activity recognition recognition from RGB images/skeleton data, and c) gesture recognition from RGB images/skeleton data. Finally, embedded execution is extremely important, as it facilitates vehicle autonomy, e.g., in communication-denied environments. Application areas include driver/operator/pilot activity recognition, gesture-based control of autonomous vehicles, or gesture recognition for traffic management. The lecture will offer an overview of all the above plus other related topics and will stress the related algorithmic aspects. Some issues on embedded CNN computation (e.g., through fast convolution algorithms) will be overviewed as well.

Should you require access to the resource, please contact the author directly.

Electrical Infrastructure Inspection

This lecture overviews the use of drones for electrical infrastructure inspection and maintenance. Various types of inspection, e.g., using visual cameras, LIDAR or thermal cameras are reviewed. Primary application area is electric line inspection. Line detection and tracking and drone perching are examined. Human action recognition and co-working assistance are overviewed.

Should you require access to the resource, please contact the author directly.

Elongated object detection and segmentation

The application of computer vision to industrial inspection poses a unique challenge in identifying elongated objects that extend beyond the image frame. This lecture offers a comprehensive overview of detection and segmentation techniques, with a particular emphasis on recent advancements in deep learning-based approaches. Throughout the lecture, we delve into the capabilities of these algorithms, showcasing their potential in enhancing the inspection of pipelines and powerlines. By doing so, we aim to demonstrate how these advanced techniques can substantially reduce the human workload and alleviate stress in industrial inspection processes.

Should you require access to the resource, please contact the author directly.

ENFIELD AI Summer School 2025 (ENFIELD educational material)

The AI Summer School was organized in the framework of the Horizon Project ENFIELD, having taken place at the Budapest University of Technology and Economics, Hungary, from July, 28 to August, 1 2025.

This five-day program offered a comprehensive learning experience, combining expert-led lectures, hands-on workshops, and interactive challenges designed to enhance both theoretical understanding and practical skills. Participants engaged with cutting-edge AI techniques, gained insights from leading researchers, and collaborated on practical applications of AI in various domains: Green AI, Energy AI, Adaptive AI, Healthcare AI, Human-Centric AI,  Manufacturing AI, Trustworthy AI, Space AI.

ENFIELD Hackathon 2025: AI for Energy Efficiency (ENFIELD educational material)

The ENFIELD Hackathon 2025 was organized in the framework of the Horizon Project ENFIELD, having taken place in Tallinn, Estonia, on May, 29 and 30 2025.

The Hackathon brought together students and young researchers to develop AI-driven solutions for energy efficiency. Using real-world electricity consumption and weather data, teams competed to predict building energy consumption with precision, efficiency, and clarity.

The event included mentorship, workshops, and public demos, fostering innovation in sustainable AI.

Bias in Medical AI: Identifying Risks and Ensuring Fairness (ENFIELD educational material)

The second ENFIELD webinar was organized in the framework of the Horizon Project ENFIELD, having taken place online on May 23, 2025.

The objective of the webinar was to discuss the Bias in Medical AI.

During the webinar, 10 presentations were given:

  • Pankaj Pandey – Presentation of the ENFIELD project
  • Bjørn Morten Hofmann – The Ethics of the Inexorable Biases in Medical AI
  • Sören Möller – Pseudo-Individual Predictions as Interventional Health Programs – Shattering the Individual into Data Points
  • Sofia Couto da Rocha – Synthetic Data Bias Amplification in Healthcare
  • Barbara Draghi – Detect and Mitigate Bias in Patient Data Using Synthetic Data Generators
  • Konstantina Remoundou – Biases in EHR Databases; a Medical vs Statistical Approach through the ICU Readmission Case
  • Chiara Bellatreccia – Addressing Bias and Data Scarcity in AI-Based Skin Disease Diagnosis with Non-Dermoscopic Images
  • Panagiotis Tsakanikas – APPO – Building AI Trust through Bias Identification
  • Andrei Olaru – Towards a Framework for Bias Analysis in Data
  • Juulia Jylhävä – AI-Driven NLP Models to Identify Aging-Related Health Issues in Free-Text EHR Data