Advanced estimation algorithms for connected and autonomous vehicle applications
- Authors
- Publication Date
- Sep 19, 2024
- Source
- HAL
- Keywords
- Language
- English
- License
- Unknown
- External links
Abstract
This thesis is dedicated to the development of advanced estimation algorithms specifically designed for autonomous vehicle applications. Initially, we provide a comprehensive overview of various vehicle controllers and advanced driving assistance systems, setting the stage for an in-depth discussion of vehicle dynamics and kinematics models. We then explore both classical (model-based) and machine learning-based (data-driven) observers, examining their literature and applications within vehicular and robotics contexts. Our research introduces several novel methodologies: first, a finite time interval estimation approach for discrete Linear Parameter Varying (LPV) systems, applied to the vehicle's lateral dynamics to estimate side slip despite uncertainties in cornering stiffness. Next, we propose a neuro-adaptive observer that combines neural networks with concurrent learning to estimate unknown forces in the vehicle's longitudinal model. Furthermore, we present a generalized high-gain observer, incorporating Linear Matrix Inequality (LMI) conditions and a threshold constraint on the high-gain parameter, designed to handle additional measurements and constraints. This observer ensures Input-to-State Stability (ISS) bounds on measurement noise and adapts to non-canonical systems via output transformation and augmented system design. Finally, we validate our methods through extensive simulations using the CARLA simulator and trajectory estimation with the KITTI dataset, demonstrating superior performance in terms of accuracy, convergence speed, and robustness in various vehicular scenarios. The outcomes illustrate significant improvements over traditional methods, highlighting the practical potential of our advanced estimation techniques in enhancing autonomous vehicle performance.
