Adaptive Path Following Driver Model

Abstract

The evolution of advanced driver assistance systems (ADAS) and autonomous driving technologies has heightened the need for robust and adaptive driver models. This thesis focuses on developing an adaptive driver model within a Software-in-the-Loop (SIL) framework, designed to handle dynamic environments, complex scenarios, and disturbances with high precision. A state-space model is formulated to capture vehicle dynamics and error dynamics, essential for precise trajectory tracking. The error dynamics state-space model updates in real time, accounting for deviations in lateral position, yaw angle, and other key variables. This real-time updation enables the model to compute optimal control inputs using both a Linear Quadratic Regulator (LQR)-based controller and a Model Predictive Control (MPC)-based approach. MPC, with its ability to anticipate future states and optimize control inputs over a finite horizon, complements LQR by providing enhanced performance in managing constraints and nonlinearities, especially in dynamic environments. The SIL framework integrates real-time data exchange between components, leveraging middleware to maintain simulation fidelity and responsiveness. By iteratively refining error dynamics, adapting to changes in each simulation setup, and leveraging both LQR and MPC for trajectory tracking, the proposed driver model enhances precision and adaptability. This research contributes to advancing SIL frameworks, supporting safer and more reliable autonomous driving technologies while meeting industry standards.