Comparing Model-Based and Data-Driven Controllers for an Autonomous Vehicle Task
| Autor: | Guido Herrmann, Tugrul Irmak, Ute Leonards, Joel Variath Paul, Arisara Meekul, Erwin Jose Lopez Pulgarin |
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| Rok vydání: | 2018 |
| Předmět: |
050210 logistics & transportation
0209 industrial biotechnology Process (engineering) Computer science business.industry Deep learning 05 social sciences Control (management) Control engineering Robotics 02 engineering and technology Data-driven Task (project management) 020901 industrial engineering & automation 0502 economics and business Reinforcement learning Artificial intelligence Predictability business |
| Zdroj: | Towards Autonomous Robotic Systems ISBN: 9783319967271 TAROS |
| DOI: | 10.1007/978-3-319-96728-8_15 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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