• De Groene Loper 19, Flux

    5612 AP Eindhoven


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

    5600 MB Eindhoven


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). 

Fingerprint Dive into the research topics where Mobile Perception Systems Lab is active. These topic labels come from the works of this organisation's members. Together they form a unique fingerprint.

Semantics Engineering & Materials Science
Pixels Engineering & Materials Science
Intelligent vehicle highway systems Engineering & Materials Science
Labels Engineering & Materials Science
Color Engineering & Materials Science
Cameras Engineering & Materials Science
Supervised learning Engineering & Materials Science
Stereo vision Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2016 2021

PRYSTINE: PRogrammable sYSTems for INtelligence in AutomobilEs

Sartorius - ter Haar, M., Keijzers, A., Keijzers, A., Dubbelman, G., Das, A., Tigrek, R. F. & Bighashdel, A.

Infineon Technologies AG


Project: Research direct

AUTOmated driving Progressed by Internet Of Things

Dubbelman, G., den Ouden, J., Dubbelman, G., Roomi Zadeh, A., van Hout, J., Keijzers, A. & Keijzers, A.


Project: Research direct

Research Output 2014 2019

ABC analysis of an aerospace business case on ALM echnologies in aerospace and defence supply chain

Kemsaram, N., Kishore Kumar Maley & Raghunandan Mahadevan, 29 Apr 2019, In : Small Enterprises Development, Management & Extension Journal. 46, 1, p. 24-34 11 p.

Research output: Contribution to journalArticleProfessional

Supply chains
3D printers

A domain agnostic normalization layer for unsupervised adversarial domain adaptation

Romijnders, R. R. F. M., Meletis, P. & Dubbelman, G., 4 Mar 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway: Institute of Electrical and Electronics Engineers, p. 1866-1875 10 p. 8658995. (Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019).

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

Network performance
Neural networks
2 Downloads (Pure)

An integrated framework for autonomous driving: object detection, lane detection, and free space detection

Kemsaram, N., Das, A. & Dubbelman, G., 31 Jul 2019, 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). Yang, X-S., Dey, N. & Joshi, A. (eds.). Piscataway: Institute of Electrical and Electronics Engineers, p. 260-265 6 p. 8904020

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

Open Access
Advanced driver assistance systems
Traffic signs
Railroad tracks


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


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