Robust Safe Control for Automated Driving Systems With Perception Uncertainties

Sammanfattning: Autonomous Driving Systems (ADS), a subcategory of Cyber-Physical Systems (CPS) are becoming increasingly popular with ubiquitous deployment. They provide advanced operational functions for perception and control, but this also raises the question of their safety capability. Such questions include if the vehicle can stay within its lane, keep a safe distance from the leading vehicle, or avoid obstacles, especially under the presence of uncertainties. In this master thesis, the operational safety of ADS will be addressed, more specifically on the Adaptive Cruise Control (ACC) system by modeling an optimal control problem based on Control Barrier Function (CBF) unified with Model Predictive Control (MPC). The corresponding optimal control problem is robust against measurement uncertainties for an Autonomous Vehicle (AV) driving on a highway, where the measurement uncertainties will represent the common faults in the perception system of the AV. A Kalman Filter (KF) is also added to the system to investigate the performance difference. The resulting framework is implemented and evaluated on a simulation scenario created in the open-source autonomous driving simulator CARLA. Simulations show that MPC-CBF is indeed robust against measurement uncertainties for well-selected horizon and slack variable values. The simulations also show that adding a KF improves the overall performance. The higher the horizon, the more confident the system becomes as the distance to the leading vehicle decreases. However, this may cause infeasibility where there are no solutions to the optimal control problem during sudden braking as the AV cannot brake fast enough before it crashes. Meanwhile, the smaller the slack variable, the more restrictive becomes CBF where it impacts more on the control input than desired which could also cause infeasibility. The results of this thesis will help to facilitate safety-critical CPS development to be deployed in real-world applications.