Institut für Mess- und Regelungstechnik (MRT)

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!

 

Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme

Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is confronted with numerous tactical and strategical choices. Most state-of-the-art AV platforms are implementing tactical and strategical behavior generation using finite state machines. However, these usually result in poor explainability, maintainability and scalability. Research in robotics has raised many architectures to mitigate these problems, most interestingly behavior-based systems and hybrid derivatives.
Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving. It is a generalizing and scalable decision-making framework, utilizing modular behavior blocks to compose more complex behaviors in a bottom-up approach. The system is capable of combining a variety of scenario- and methodology-specific solutions, like POMDPs, RRT* or learning-based behavior, into one understandable and traceable architecture. We extend the hierarchical behavior-based arbitration concept to address scenarios where multiple behavior options are applicable, but have no clear priority against each other. Then, we formulate the behavior generation stack for automated driving in urban and highway environments, incorporating parking and emergency behaviors as well. Finally, we illustrate our design in an explanatory evaluation.

Contact: M.Sc. Annika Meyer and M.Sc. Piotr Orzechowski

 

Learning Path Tracking from Simulation

We focus on applying machine learning techniques for planning and control of automated vehicles. Off-line simulation can provide plenty of training data in order to learn challenging problems efficiently. However, one of the most important concerns is transferring learned algorithms from simulation to the real car. In this work, we investigate learning a path tracking agent on the simulation using several random training episodes to cover different situations and provide a generally applicable solution. The trained agent is then tested on a small racing car without further modification to evaluate its effectiveness for accurate and smooth control of the vehicle.

Contact: M.Sc. Danial Kamran

 

Scalable Reinforcement Learning for Automated Driving at Occluded Intersections

Contact: M.Sc. Danial Kamran

 

 

 

Life-Long Vision based Mapping and Localization

Current intelligent vehicles require robust and accurate self-localization in a multitude of scenarios. Common approaches couple inertial measurement units (IMU) with global navigation satellite systems (GNSS). However, such solutions are not reliable in urban environments due to multipath, shadowing and atmospheric perturbations.
To overcome these drawbacks we investigate in life-long iterative mapping and high-precision localization in six degrees of freedom using multiple cameras mounted on the vehicle. The approach yields centimeter accuracy even under challenging conditions.

Contact: M.Sc. Haohao Hu

 

Meaningful features for localization and more

A common approach for localization is to use abstract features detected in e.g. camera images or Lidar scans. These features enable precise localization but have no other use. We develop meaningful features for localization which can further be used for other tasks such as planning or behavior generation.
Groundtruth path (red), localization result (orange arrows), detections of facades, poles and road markings.

Contact: Dr. rer. nat. Martin Lauer

 

Probabilistic Motion Planning for Automated Vehicles

As a prerequisite for their on-road deployment, automated vehicles must show an appropriate and reliable driving behavior in mixed traffic, i.e. alongside human drivers. Besides the uncertainties resulting from imperfect perception, occlusions and limited sensor range, also the uncertainties in the behavior of other traffic participants have to be considered.
Related approaches for motion planning in mixed traffic often employ a deterministic problem formulation. The solution of such formulations is restricted to a single trajectory. Deviations from the prediction of other traffic participants are accounted for during replanning, while large uncertainties lead to conservative and over-cautious behavior. As a result of the shortcomings of these formulations in cooperative scenarios and scenarios with severe uncertainties, probabilistic approaches are pursued. Due to the need for real-time capability, however, a holistic uncertainty treatment often induces a strong limitation of the action space of automated vehicles. Moreover, safety and traffic rule compliance are often not considered.
Thus, we focus on motion planning approaches are targeted towards the different predominant uncertainties in different scenarios, while operating in a continuous action space and ensuring safety.

Contact: Dr.-Ing. Maximilian Naumann

 

Safety in Motion Planning and Decision Making

Since human lives must never be put at risk, the safety of automated vehicles must be ensured before their on-road deployment. As shown in many exemplary calculations, a validation solely via test drives is not expedient. In order to validate the safety of automated vehicles, formal methods are promising. Here, we focus on analyzing and extending existing concepts such as Responsibility Sensitive Safety (RSS) by Intel/Mobileye and set-based methods as proposed by Matthias Althoff et al. Further, we focus on motion planning and decision making that is safe, yet not over-cautious, since over-cautious behavior might entice human drivers to risky maneuvers (such as overtaking) and thus jeopardize the safety concept.

Contact: Dr.-Ing. Maximilian Naumann

 

Inverse Reinforcement Learning from Human Behavior

In order to better understand and predict human behavior, but also to generate human-like behavior, we created the Interaction Dataset, a dataset that contains human driving, cycling and walking behavior, along with high-definition lanelet2 maps. We now focus on analyzing this behavior using inverse reinforcement learning.

Contact: Dr.-Ing. Maximilian Naumann

 

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: Dr.-Ing. Maximilian Naumann

 

Simultaneous 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: Dr. rer. nat. Martin Lauer or Dr. Carlos Fernández López

 

Automatic Verification of High Precision Maps for Highly Automated Driving

The recent progress in the area of autonomous cars has shown that high precision digital maps are crucial to steer a car safely and comfortably through a complex dynamic environment. To plan a trajectory which guides a self-driving car as smoothly as a foresightedly driving human driver would do, details on a sub-lane level are needed. However, the more details are stored in a map, the faster it becomes outdated.
Thus, the goal of our research is to use sensor data from sensors, which are needed for autonomous driving, anyway, to locally verify the map that is stored on the car. This can either verify the map or mark it - or parts of it - as invalid.
As part of our work, we are developing methods and a framework to compare map and sensor data. Furthermore, we are trying to identify features that are suitable for map verification. Another challenge is to model static and dynamic occlusions which limit the range within which the map can be assessed.
Parts of the map which are eventually marked as changed can not only be invalidated for all other components of an autonomous car, but also sent back to a remote server. When permanent changes are identified, they could either trigger a remapping process or be used as a map update directly.

Contact: M.Sc. Jan-Hendrik Pauls

 

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.  -> Project Details

Contact: M.Sc. Ömer Sahin Tas

 

Former Projects