Rule-based optimal control for autonomous driving
METADATA ONLY
Loading...
Author / Producer
Date
2021-05
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
We develop optimal control strategies for Autonomous Vehicles (AVs) that are required to meet complex specifications imposed by traffic laws and cultural expectations of reasonable driving behavior. We formulate these specifications as rules, and specify their priorities by constructing a priority structure, called Total ORder over eQuivalence classes (TORQ). We propose a recursive framework, in
which the satisfaction of the rules in the priority structure are iteratively relaxed based on their priorities. Central to this framework is an optimal control problem, where convergence to desired states is achieved using Control Lyapunov Functions (CLFs), and safety is enforced through Control Barrier Functions (CBFs). We also show how the proposed framework can be used for after-the-fact, pass/fail evaluation of trajectories - a given trajectory is rejected if we can find a controller producing a trajectory that leads to less violation of the rule priority structure. We present case studies with multiple driving scenarios to demonstrate the effectiveness of the proposed framework.
Permanent link
Publication status
published
External links
Book title
Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems (ICCPS '21)
Journal / series
Volume
Pages / Article No.
143 - 154
Publisher
Association for Computing Machinery
Event
12th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2021)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Autonomous driving; Lyapunov methods; Safety; Priority structure
Organisational unit
09574 - Frazzoli, Emilio / Frazzoli, Emilio