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Data-Driven Vehicle Dynamic Model for Autonomous Vehicle Applications

conference contribution
posted on 2024-04-29, 02:11 authored by J Gregory, M Rokonuzzaman, Navid MohajerNavid Mohajer, M Ghafarian
Vehicle Dynamics Models (VDMs) face a trade-off scenario between accuracy and speed. More complex models can generate more accurate predictions of vehicle state but are more computationally slow. Additionally, many VDMs rely on the explicit estimation of unknown parameters. To avoid these limitations, we propose a feed-forward Time Delay Neural Network (TDNN) which surpasses the physics-based VDMs in accuracy and speed without explicit estimation of unknown quantities. Notably, the proposed TDNN model was able to accurately predict the vehicle state on various road surfaces despite no knowledge of the tyre-road friction. The TDNN predicts the longitudinal and yaw accelerations of the vehicle with a Root Mean Square Error (RMSE) of as low as 0.0387 rad/s2

History

Volume

00

Pagination

2850-2855

Start date

2023-10-01

End date

2023-10-04

ISSN

1062-922X

ISBN-13

9798350337020

Title of proceedings

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Event

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Publisher

IEEE

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