Physics-Informed Neural Networks for Vehicle Lateral Dynamics Modeling
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Accurate estimation of vehicle lateral dynamics is important for advanced driver
assistance systems (ADAS) and autonomous driving applications. This thesis proposes
a Physics-informed Neural Network (PINN) framework that combines fundamental
vehicle dynamics, based on simplified single-track models and Pacejka tire
formulations, with data-driven recurrent neural networks that incorporate attention
mechanisms. By embedding physical constraints within the learning process, the hybrid
model aims to improve interpretability, robustness, and real-time performance
without relying on additional sensor inputs. Evaluations using high-fidelity simulations
and real-world datasets suggest that the model can estimate latent states,
such as lateral velocity, with promising accuracy. While the approach generalizes
well across a variety of simulated driving scenarios, it faces challenges in maintaining
comparable performance on real-world data mainly due to modeling uncertainties
and measurement noise. These limitations highlight the need for further investigation
into robustness and domain adaptation techniques. Future work will explore
on-board deployment, adaptation to individual vehicle characteristics, and the integration
of more detailed physical models. These developments could enhance the
model’s reliability and utility in practical applications, contributing to improved
vehicle safety and operational efficiency.
Beskrivning
Ämne/nyckelord
Physics-Informed Neural Networks (PINN), Vehicle Dynamics, Lateral Dynamics Modeling, Parameter Estimation, Recurrent Neural Networks, Attention Mechanism, System Identification, Hybrid Modeling, Autonomous Driving, State Reconstruction