Autonomous driving technology aims to improve road safety, reduce energy consumption, and increase traffic efficiency. Developing a full-scale autonomous vehicle is costly and subject to strict regulations, making model-scale autonomous vehicle platforms more accessible alternatives for research. In recent years, an autonomous vehicle platform has been developed as a testbed for autonomous driving at the Delft Center for Systems and Control (DCSC).

However, several challenges are encountered during the development phase due to sensor malfunctions and inaccuracies in vehicle system modeling. The external vision-based localization system (Motion Capture System) generates inaccurate measurements, and the communication between the central controller and the sensors can be unstable, leading to packet loss. Additionally, there is a Radio Control (RC) input layer between the controller and the DCSC vehicles, and the dynamics of the RC inputs are unknown. These challenges have limited the implementation of general control architectures in the system.

To address these challenges, an Outlier-Robust Path-Tracking Framework is proposed in this thesis to perform path-tracking tasks under simulated sensor malfunctions and unknown vehicle dynamics. Within the framework, the motion of the vehicle is simulated using an identified DCSC vehicle model.
A first-order system is employed to model the relationship between the RC commands and the vehicle's rear wheel angular velocity and front wheel steering angle. Adaptive Outlier-Robust Kalman Filter is proposed to accommodate both the observational outliers and the model mismatch between the process model and the simulation model.

The performance of the framework is evaluated using synthetic datasets. Three types of observational disturbances—Gaussian noise, observational outliers, and packet loss—are introduced to the observations. The results show that, compared to the basic Kalman filter, the proposed filter provides more precise state estimation under observational disturbances. Additionally, compared to other non-adaptive outlier-robust Kalman filters, the proposed method can adjust the process noise covariance without requiring an accurate prior for the process noise covariance. The proposed framework demonstrated promising results for the path-tracking task under disturbances. Although the framework is tailored to our platform, the approach can be adapted to other systems with noisy measurements and model discrepancy.