AMZFormulaStudent | Learning-based Model Predictive Control for Autonomous Racing @AMZFormulaStudent | Uploaded July 2019 | Updated October 2024, 2 hours ago.
Presented paper can be downloaded here: https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/351561/08754713.pdf?sequence=1&isAllowed=y
Abstract—In this paper, we present a learning-based control
approach for autonomous racing with an application to the AMZ
Driverless race car gotthard. One major issue in autonomous
racing is that accurate vehicle models that cover the entire
performance envelope of a race car are highly nonlinear, complex
and complicated to identify, rendering them impractical for
control. To address this issue, we employ a relatively simple
nominal vehicle model, which is improved based on measurement
data and tools from machine learning. The resulting formulation
is an online learning data-driven Model Predictive Controller,
which uses Gaussian Processes regression to take residual model
uncertainty into account and achieve safe driving behavior. To
improve the vehicle model online, we select from a constant
in-flow of data points according to a criterion reflecting the
information gain, and maintain a small dictionary of 300 data
points. The framework is tested on the full-size AMZ Driverless
race car, where it is able to improve the vehicle model and reduce
lap times by 10% while maintaining safety of the vehicle.
_____________________________________________________
Visit our website and follow us on social media!
AMZ driverless Website: driverless.amzracing.ch
AMZ Racing on Facebook: facebook.com/amzracing
AMZ Racing on Twitter: twitter.com/amzracing?lang=en
AMZ Racing on Instagram: instagram.com/amzracing/?hl=en
Presented paper can be downloaded here: https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/351561/08754713.pdf?sequence=1&isAllowed=y
Abstract—In this paper, we present a learning-based control
approach for autonomous racing with an application to the AMZ
Driverless race car gotthard. One major issue in autonomous
racing is that accurate vehicle models that cover the entire
performance envelope of a race car are highly nonlinear, complex
and complicated to identify, rendering them impractical for
control. To address this issue, we employ a relatively simple
nominal vehicle model, which is improved based on measurement
data and tools from machine learning. The resulting formulation
is an online learning data-driven Model Predictive Controller,
which uses Gaussian Processes regression to take residual model
uncertainty into account and achieve safe driving behavior. To
improve the vehicle model online, we select from a constant
in-flow of data points according to a criterion reflecting the
information gain, and maintain a small dictionary of 300 data
points. The framework is tested on the full-size AMZ Driverless
race car, where it is able to improve the vehicle model and reduce
lap times by 10% while maintaining safety of the vehicle.
_____________________________________________________
Visit our website and follow us on social media!
AMZ driverless Website: driverless.amzracing.ch
AMZ Racing on Facebook: facebook.com/amzracing
AMZ Racing on Twitter: twitter.com/amzracing?lang=en
AMZ Racing on Instagram: instagram.com/amzracing/?hl=en
