Rule-based Evaluation and Optimal Control for Autonomous Driving

Understand

We develop optimal control strategies for autonomous vehicles (AVs) that are required to meet complex specifications imposed as rules of the road (ROTR) and locally specific cultural expectations of reasonable driving behavior.

• We formulate these specifications as rules, and specify their priorities by constructing a priority structure, called \underline{T}otal \underline{OR}der over e\underline{Q}uivalence classes (TORQ).
• We propose a recursive framework, in which the satisfaction of the rules in the priority structure are iteratively relaxed in reverse order of priority.
• Central to this framework is an optimal control problem, where convergence to desired states is achieved using Control Lyapunov Functions (CLFs) and clearance with other road users is enforced through Control Barrier Functions (CBFs).