The Mobile Perception Systems Lab researches methods in Artificial Intelligence that allow mobile autonomous systems to perceive their environment. Our long-term goal is to realize AI that can anticipate on future events in highly dynamic and complex environments. We always validate our AI methods 'in the loop', meaning in the context of challenging real-world applications, mainly from the industry domains of automotive, transportation, and logistics.

MPS is part of the Signal Processing Systems group and we work closely with the Embedded Systems group, the Dynamics and Control group, the Control Systems Technology group, and the TU/e-wide Strategic Area for Smart Mobility.

Perception Through Anticipation

The MPS lab specializes in the following AI methods: deep learning, multi-modal computer vision, and simultaneous localization and mapping. These are key enabling technologies that allow mobile sensor platforms to perceive and interpret their environments from past and current sensory data, in essence estimating a dynamic digital world-model in real-time. In coming years, we aim to make a step in the direction of spatio-temporal reasoning engines that allow mobile sensor platforms to predict possible future events and thereby achieve anticipation capabilities. Currently, the lack of anticipation capabilities, is a key bottleneck in deploying mobile autonomous systems in complex and dynamic environments, such as self-driving cars in crowded inner cities. We firmly believe that in order to advance AI and its applications, both an inter-disciplinary approach and a strong cooperation with industry are required. Hence, we are involved in many cross-disciplinary European projects and initiatives, such as the International Connected and Automated Driving Institute (https://icadi.net), and we recently started the company AI In Motion to bring our technologies to the market (https://aiim.ai).