Georg Adrian Novotny

Georg Adrian Novotny

Georg Novotny is a part-time lecturer at UAS Technikum Vienna, where he teaches mobile robotics and is actively involved in the development of search and rescue robots. Alongside his work at UAS, he works as a platform engineer at MIC-Cust. Georg is also currently pursuing his PhD at JKU, where he is focused on system identification and parameter estimation of vehicles and robots. With his expertise in robotics and engineering, Georg is dedicated to advancing the field of autonomous systems and developing cutting-edge technologies that improve our lives.

Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics

Published in Applied Sciences, 2020

Abstract: On-board sensory systems in autonomous vehicles make it possible to acquire information about the vehicle itself and about its relevant surroundings. With this information the vehicle actuators are able to follow the corresponding control commands and behave accordingly. Localization is thus a critical feature in autonomous driving to define trajectories to follow and enable maneuvers. Localization approaches using sensor data are mainly based on Bayes filters. Whitebox models that are used to this end use kinematics and vehicle parameters, such as wheel radii, to interfere the vehicle’s movement. As a consequence, faulty vehicle parameters lead to poor localization results. On the other hand, blackbox models use motion data to model vehicle behavior without relying on vehicle parameters. Due to their high non-linearity, blackbox approaches outperform whitebox models but faulty behaviour such as overfitting is hardly identifiable without intensive experiments. In this paper, we extend blackbox models using kinematics, by inferring vehicle parameters and then transforming blackbox models into whitebox models. The probabilistic perspective of vehicle movement is extended using random variables representing vehicle parameters. We validated our approach, acquiring and analyzing simulated noisy movement data from mobile robots and vehicles. Results show that it is possible to estimate vehicle parameters with few kinematic assumptions.

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Bibtex: download

@article{,
  author    = {Wilfried W{\"o}ber and Georg Novotny and Lars Mehnen and Cristina Olaverri-Monreal},
  doi       = {10.3390/APP10186317},
  issn      = {20763417},
  issue     = {18},
  journal   = {Applied Sciences (Switzerland)},
  keywords  = {Probabilistic robotics,Variational bayes,Vehicle parameter estimation},
  month     = {9},
  publisher = {MDPI AG},
  title     = {Autonomous vehicles: Vehicle parameter estimation using variational bayes and kinematics},
  volume    = {10},
  year      = {2020}
}

Recommended citation: Wöber, W., Novotny, G., Mehnen, L., & Olaverri-Monreal, C. (2020). Autonomous vehicles: Vehicle parameter estimation using variational bayes and kinematics. Applied Sciences (Switzerland), 10(18). https://doi.org/10.3390/APP10186317