A Probabilistic Framework for Imitating Human Race Driver Behavior

Understanding and modeling human driver behavior is crucial for advanced vehicle development. However, unique driving styles, inconsistent behavior, and complex decision processes render it a challenging task, and existing approaches often lack variability or robustness. To approach this problem, we...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2020-04, Vol.5 (2), p.2085-2092
Main Authors: Lockel, Stefan, Peters, Jan, van Vliet, Peter
Format: Article
Language:English
Subjects:
Adaptation models
Algorithms
Automobiles
autonomous vehi-cle navigation
Computer simulation
Driver behavior
Drivers
Learning from demonstration
Machine learning
Modelling
Neural networks
Probabilistic logic
Probability theory
Racing
Robustness
Target tracking
Task analysis
Trajectory
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Summary:Understanding and modeling human driver behavior is crucial for advanced vehicle development. However, unique driving styles, inconsistent behavior, and complex decision processes render it a challenging task, and existing approaches often lack variability or robustness. To approach this problem, we propose Probabilistic Modeling of Driver behavior (ProMoD), a modular framework which splits the task of driver behavior modeling into multiple modules. A global target trajectory distribution is learned with Probabilistic Movement Primitives, clothoids are utilized for local path generation, and the corresponding choice of actions is performed by a neural network. Experiments in a simulated car racing setting show considerable advantages in imitation accuracy and robustness compared to other imitation learning algorithms. The modular architecture of the proposed framework facilitates straightforward extensibility in driving line adaptation and sequencing of multiple movement primitives for future research.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.2970620