Adaptive Path Following Driver Model
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Mobility engineering (MPMOB), MSc
Publicerad
2025
Författare
Sathiya Venkata Narayanan, Balaji
Manickam, Muralikrishna
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
AD & ADAS , Path following , Adaptive driver model , Software-in-the-Loop , Error Dynamics , State-space model , Model Predictive Control , Linear Quadratic Regulator , Trajectory Tracking , Lateral control , Real-time Control , LQR Controller Tuning , Dynamic Environments , Vehicle Dynamics , Middleware Integration , Tracking accuracy , MPC