• De Groene Loper 19, Flux

    5612 AP Eindhoven

    Netherlands

  • P.O. Box 513, Department of Electrical Engineering

    5600 MB Eindhoven

    Netherlands

Organization profile

Introduction / mission

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.

Highlighted phrase

Perception Through Anticipation

Organisational profile

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). 

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    Projects

    Research Output

    Architecture Design and Development of an On-board Stereo Vision System for Cooperative Automated Vehicles

    Kemsaram, N., Das, A. & Dubbelman, G., 20 Sep 2020, (Accepted/In press) Proceedings of 23rd IEEE International Conference on Intelligent Transportation Systems 2020 (IEEE ITSC 2020), Rhodes, Greece.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    A Stereo Perception Framework for Autonomous Vehicles

    Kemsaram, N., Das, A. & Dubbelman, G., 25 May 2020, (Accepted/In press) Proceedings of 2020 IEEE 91st Vehicular Technology Conference, Spring (2020 IEEE VTC-Spring), Antwerp, Belgium.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    ASTEROIDS: A Stixel Tracking Extrapolation-based Relevant Obstacle Impact Detection System

    Sanberg, W. P., Dubbelman, G. & de With, P. H. N., 13 May 2020, (Accepted/In press) In : IEEE Transactions on Intelligent Vehicles.

    Research output: Contribution to journalArticleAcademicpeer-review

  • Prizes

    Best Paper Award

    Willem P. Sanberg (Recipient), 17 Jan 2019

    Prize: OtherCareer, activity or publication related prizes (lifetime, best paper, poster etc.)Scientific

    Press / Media

    GCDC 2016

    Jos den Ouden

    31/05/16

    1 Media contribution

    Press/Media: Public Engagement Activities

    Student theses

    Design of a track detection algorithm for a driverless formula student racing car

    Author: van der Smagt, T., 23 Apr 2019

    Supervisor: van de Molengraft, R. (Supervisor 1), Dubbelman, G. (Supervisor 2) & Barosan, I. (Supervisor 2)

    Student thesis: Master

    Lane detection and tracking for self-driving vehicles

    Author: Szutenberg, M., 30 Sep 2019

    Supervisor: Das, A. (Supervisor 1) & Dubbelman, G. (Supervisor 2)

    Student thesis: Master

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