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Stochastic driving behavior estimation of opposing race cars



Alexander Liniger, Xiaojing (George) Zhang

For autonomous driving as well as autonomous racing, one of the key ingredients for a crash free interaction with opposing cars on the track is a reliable estimation of their future behavior.

The goal of this project is to investigate different method to estimate the driving behavior. This may include approaches such as hidden Markov chain models and interactive multi model filters or machine learning approaches. However, as the computation time is limited the method should be able to efficiently estimate the behavior within few milliseconds.

The student should also investigate how this estimates can be incorporated into the existing controllers. Which can be hard as considering all possible driving behaviors often lead to extremely conservative driving, whereas not including enough information may lead to accidents.

Weitere Informationen


John Lygeros

Art der Arbeit: Masterthesis

Estimation Theory

C programming

Anzahl StudentInnen: 1
Status: done
Semester: HS16