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Autonomous Driving

The history of autonomous driving at MRT began with the Darpa Grand Challenge of 2005, a competition for autonomous off road vehicles, where MRT supplied vision components for Team ION, please click here.


In 2007, MRT entered the VW Passat "AnnieWAY", please click here, into the Darpa Urban Challenge, and advanced to the final stage. The competition was set in a mock up urban environment.


In 2011, the same vehicle won the Grand Cooperative Driving Challenge, please click here . This was the first international competition to implement highway platooning scenarios of cooperating vehicles connected with communication devices.


In 2013, MRT supplied localization, trajectory planning and control components for the vehicle that completed the 103 km of the historic Bertha-Benz-Memorial-Route autonomously, please click here and here.


In the field of autonomous driving, MRT is cooperating tightly with its sister department Mobile Perception Systems (MPS) at FZI Research Center for Information Technology, please click here.

The MRT currently contributes with various projects to this field of research. 

Many other projects focus more specifically on the perception part and are in general not limited to the context of autonomous vehicles. Pleaser refer to “Environment perception” for an overview!

Cooperative Motion Planning for Automated Vehicles in Mixed Traffic

While motion planning techniques for automated vehicles in a reactive and anticipatory manner are already widely presented, approaches to cooperative motion planning are still remaining. Thus, we focus on the enhancement of common motion planning algorithms, allowing for cooperation with human-driven vehicles.

The blue (automated) vehicle enters the narrowing first, as it is closer to the narrowing. However, if the black (human-driven) vehicle has the right of way, it drives first despite the blue vehicle being closer.


Contact: M.Sc. Maximilian Naumann

Simultaneously object tracking and shape estimation


Traditional object tracking methods approximate vehicle geometry using simple bounding boxes. We develop methods that simultaneously estimate object motion and reconstruct detailed object shape using laser scanners. The resulting shape information has benefits for the tracking process itself, but can also be vital for subsequent processing steps such as trajectory planning in evasive steering.


Contact: M.Sc. Stefan Krämer

Automatic Verification of High Precision Maps for Highly Automated Driving

Trajectory planning with verifiable safety

We perform research to investigate the metrics that enable reliable evaluation of the level of criticality and subsequently integrate them in our criticality-aware motion planner.


Contact: Ömer Sahin Tas

Project Details

Lane marking based localization and mapping

For vehicle localization we generate in a first (offline) step a map containing all visual lane markings. Onboard the vehicle we then match lane markings detected in the current camera images with the map to obain the position of the vehicle.

Contact: Markus Schreiber

Project Details



Modelling of traffic situations at urban intersections

We are researching on a practical framework for modeling traffic situations at urban intersections, which can handle two problems: maneuver recognition and trajectory prediction of moving vehicles

Contact: Hong Quan Tran

Project Details

Video Localization

We developed a method for high accuracy vision-only localization in a map of visual landmarks.

Contact: Henning Lategahn

Project Details

Lane track detection in urban Environments

Lane estimation of the ego vehicle plays a key role in navigating a car through unknown areas. In fact, solving this problem is a prerequisite for any vehicle driving autonomously in previously unmapped areas. In this project, the goal is to identify features in urban environments using low cost sensors and develop methods for estimating lanes based on these features.

Contact: Johannes Beck

Project Details