Research direction
Autonomous driving controllers are normally evaluated on path-following accuracy, obstacle avoidance, comfort, and constraint satisfaction. My work adds a second regime: if avoidance is no longer physically feasible, the controller should choose the remaining action that minimizes the severity of the resulting collision.
Control architecture
The baseline controller is structured around model predictive control. The vehicle model, actuator limits, road boundaries, and obstacle constraints are represented explicitly so the optimization can reason about feasible future motion over a finite horizon.
Why computation time matters
A theoretically better trajectory is not useful if the controller takes too long to compute it. One part of the project therefore examines the tradeoff between horizon length, model fidelity, re-planning frequency, and the reaction time available before impact.
Validation plan
The control stack is being developed and tested in simulation first, using MATLAB/Simulink for controller development and MuJoCo for vehicle dynamics and contact studies. The architecture is intentionally structured so that a future physical-damage metric and real-time collision-response optimizer can be added without rebuilding the rest of the system.
Related work
Alongside the vehicle-control research, I have worked on nonlinear state estimation, including CKF/EKF comparisons for localization and radar tracking and a quaternion-based UKF pipeline for underwater sensor fusion.