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Comparing Model-Based and Data-Driven Controllers for an Autonomous Vehicle Task

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Erwin José López Pulgarín
  • Tugrul Irmak
  • Joel Variath Paul
  • Arisara Meekul
  • Guido Herrmann
  • Ute Leonards
Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems
Subtitle of host publication19th Annual Conference, TAROS 2018, Bristol, UK July 25-27, 2018, Proceedings
Publisher or commissioning bodySpringer, Cham
Pages170-182
Number of pages13
ISBN (Electronic)9783319967288
ISBN (Print)9783319967271
DOIs
DateAccepted/In press - 2 Apr 2018
DatePublished (current) - 21 Jul 2018

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume10965
ISSN (Print)0302-9743

Abstract

The advent of autonomous vehicles comes with many questions from an ethical and technological point of view. The need for high performing controllers, which show transparency and predictability is crucial to generate trust in such systems. Popular data-driven, black box-like approaches such as deep learning and reinforcement learning are used more and more in robotics due to their ability to process large amounts of information, with outstanding performance, but raising concerns about their transparency and predictability. Model-based control approaches are still a reliable and predictable alternative, used extensively in industry but with restrictions of their own. Which of these approaches is preferable is difficult to assess as they are rarely directly compared with each other for the same task, especially for autonomous vehicles. Here we compare two popular approaches for control synthesis, model-based control i.e. Model Predictive Controller (MPC), and data-driven control i.e. Reinforcement Learning (RL) for a lane keeping task with speed limit for an autonomous vehicle; controllers were to take control after a human driver had departed lanes or gone above the speed limit. We report the differences between both control approaches from analysis, architecture, synthesis, tuning and deployment and compare performance, taking overall benefits and difficulties of each control approach into account.

    Structured keywords

  • Brain and Behaviour
  • Cognitive Science
  • Visual Perception

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Springer at https://link.springer.com/chapter/10.1007/978-3-319-96728-8_15 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 726 KB, PDF document

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