Lehrstuhl für Mechatronik in Maschinenbau und Fahrzeugtechnik (MEC)

Autonomous Driving

Description

As a key emerging technology, autonomous (or machine) driving (AD) represents a key applied research area of our chair. Developing AD solutions requires a mindset of mechatronics, which goes far beyond its interpretation in mechanical engineering. Perspectives of control engineering, computer science and communication engineering offer versatality and dominate the mechanical domain as solutions climb towards higher levels of autonomy and connected driving. Our to-date AD solutions conjunct adaptive control algorithms with various modules for envinroment perception. Control algorithms are developed using not only the well established concepts such as model predictive control (MPC), but also the modern machine learning techniques such as IL, RL and DNN based stochastic MPC. The developed algorithms target various driving scenarios, including city driving, inter-city and highway driving, valet parking and more. In the scope of city driving, we also focus on connected driving for which collaborative and distributed decision making algorithms are needed. The developed algorithms are tested in real-world scenarios using two AD facilitated vehicles, which are equipped with high-end sensory and computation resources, as well as a software stack for perception, delivering accurate real-time information, preception and understanding of the environment.

Keywords

Autonomous Driving
Environment Perception
Decision Making
Vehicle Control

 

Contact

Prof. Naim Bajcinca
Gottlieb-Daimler-Str. 42
67663, Kaiserslautern
Phone: +49 (0)631/205-3230
Fax: +49 (0)631/205-4201
naim.bajcinca@mv.uni-kl.de

 

Funding

BMVI, Bundesministerium für Verkehr und digitale Infrastruktur


Autonomous Driving

Architecture

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