RL-based path planning for controller performance validation

Lukas Schichler*, Karin Festl, Michael Stolz, Daniel Watzenig

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Autonomous vehicles (AVs) will be part of everyday life in the near future. In order to accelerate this process, many subsystems need to be optimised and validated. One of the most important subsystem of AVs is the steering controller. It's task is to keep the vehicle on track, which is the reason, why many steering controllers have been designed for a large variety of applications. However, the validation of such controllers is a labour-intensive task, which is why in this paper, an Artificial Intelligence (AI) is trained to find an edge case path that brings the steering controller to its limits. This path is a sufficient substitute for a large set of paths and enables fast validation of steering controllers. This contribution describes the development of a reinforcement learning (RL) based path planner using the PPO-Algorithm to train a so called agent. Comparing the resulting key feature maps shows that the agent adapts to each controllers characteristics during the learning process. The result is demonstrated for three different state of the art path tracking controllers. For each controller the agent finds a path that leads to the controllers failure within seconds.

Original languageEnglish
Title of host publication2023 31st Mediterranean Conference on Control and Automation, MED 2023
PublisherIEEE
Pages416-421
Number of pages6
ISBN (Electronic)9798350315431
DOIs
Publication statusPublished - 2023
Event31st Mediterranean Conference on Control and Automation: MED 2023 - Limassol, Cyprus
Duration: 26 Jun 202329 Jun 2023

Conference

Conference31st Mediterranean Conference on Control and Automation
Abbreviated titleMED 2023
Country/TerritoryCyprus
CityLimassol
Period26/06/2329/06/23

ASJC Scopus subject areas

  • Aerospace Engineering
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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