English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We consider the problem of online adaptation of a neural network designed torepresent vehicle dynamics. The neural network model is intended to be used byan MPC control law to autonomously control the vehicle. This problem ischallenging because both the input and target distributions are non-stationary,and naive approaches to online adaptation result in catastrophic forgetting,which can in turn lead to controller failures. We present a novel onlinelearning method, which combines the pseudo-rehearsal method with locallyweighted projection regression. We demonstrate the effectiveness of theresulting Locally Weighted Projection Regression Pseudo-Rehearsal (LW-PR$^2$)method in simulation and on a large real world dataset collected with a 1/5scale autonomous vehicle.

Locally Weighted Regression Pseudo-Rehearsal for Online Learning of Vehicle Dynamics