AI for the real world

AI Projects

EAISI focuses on the use of data and algorithms in machines, such as robots, autonomous cars and medical equipment, which has always been a strong aspect of TU/e and the Eindhoven Brainport region. In addition, we focus on the interaction between humans and systems, including trustworthy and transparent methods resulting in moral and ethical AI. EAISI has defined three main application areas: Health, High-Tech Systems and Mobility.

AI projects in Health

Health related AI applications are:

  • Improved diagnostics
  • Personalized and wearable health systems
  • Better understanding of personal health states
  • Preventive health management

AI projects in High-Tech systems and robotics

High-Tech Systems and Robotics applications are:

  • Guaranteed machine performance
  • Autonomous machine operation and smart
  • human-operator support
  • Multi-agent robotics
  • Predictive maintenance
Project of research program Smart Manufacturing & Maintenance


Cyber Physical System based Proactive Collaborative Maintenance

Digital Twins and Linked Building Data

Web-based information systems are developed, which collect various sorts of data about a building in a decentralized manner, including detailed 3D object models, point clouds, image data, semantic data, sensor data. By Pieter Pauwels

AI in manufacturing

Manufacturing of complex geometries (parametric and structurally optimized structures with minimal material use) will be enabled by digital manufacturing techniques, e.g. robotics in construction and additive manufacturing. By Rob Wolfs

Automated fault detection in photovoltaic (PV) systems

Large-scale monitoring of distributed PV systems in comparison with expected PV output generated by a digital twin network, taking into account dynamic weather conditions, partial shading due to urban surroundings (e.g. from LiDAR data) and the non-linear characteristics of inverters and power systems. By Roel Loonen

Expert systems for spatial-structural-physics design generation

The built environment is responsible for about 40% of the worldwide energy and material resources, and housing demands are growing, due to an increase of population, and become more complex. This requires fully optimized renovated and new buildings, and, of utmost importance, also optimized design processes: the products and their conceptualization need to be revolutionized. An open source available C++ toolbox has been developed, which provides super structured and super structure free spatial design representations; expert systems to generate, modify, and assess discipline specific representations; and data analysis and visualization tools. By Hèrm Hofmeyer

AI in Mobility projects

Mobility AI applications are:

•    Vision and sensor capabilities for automated driving
•    Data analytics for effective transportation systems and distributed traffic management
•    Advanced Driver Assistance Systems
•    Battery Management Systems
•    Energy use optimization, smart grids
•    Multi-modal transport applications and planning


5G for cooperative & connected automated MOBility on X-border corridors. 5G-MOBIX consist of 55 partners from 10 countries from the EU and Turkey, China and Korea, representing European ICT industry.

Machine learning and sensor data

Based on various machine learning algorithms including Bayesian belief networks and decision trees, an integrated data analytics tool is developed to generate the spatial and temporal information related to travel and activities. By Tao Feng


Fail-safe electronics for automated driving. This is a Horizon2020 EU funded research project.


Accelerating C-ITS mobility, innovation and deployment in Europe is a Horizon 2020 EU funded research project.


Programmable Systems for Intelligence in Automobiles is a Horizon2020 EU funded project.


SAFEty systems and tools for a constantly UPgrading road environment is a EU Horizon2020 funded project in cooperation with many academic and industrial partners within the EU.

Self-adaptive personal information systems

A Bayesian method for incremental learning an individual’s preferences based on his or her choice behavior is developed and applied in personal travel information systems. By Theo Arentze

HiFi Elements

High Fidelity Electric Modelling and Testing is a EU funded research project.

SWT and Linked Data in Autonomous Mobility

Using ideas behind Linked Data and Semantic Web, allows the AI in charge of handling Autonomous Vehicles to consume valuable information from the surrounding ecosystem in order to achieve more optimal calculation of control parameters of the vehicle while using minimal amount of network resources. By Milos Viktorovic


LONGRUN is the acronym for the development of efficient and environmental friendly LONG distance poweRtrain for heavy dUty trucks aNd coaches. This is a Horizon2020 EU funded project including many academic and industrial partners within the EU.