The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections

Abstract

The design of the motion of autonomous vehicles in non-signalized intersections with the consideration of multiple criteria and safety constraints is a challenging problem with several tasks. In this paper, a learning-based control solution with guarantees for collision avoidance is proposed. The design problem is formed in a novel way through the division of the control problem, which leads to reduced complexity for achieving real-time computation. First, an environment model for the intersection was created based on a constrained quadratic optimization, with which guarantees on collision avoidance can be provided. A robust cruise controller for the autonomous vehicle was also designed. Second, the environment model was used in the training process, which was based on a reinforcement learning method. The goal of the training was to improve the economy of autonomous vehicles, while guaranteeing collision avoidance. The effectiveness of the method is presented through simulation examples in non-signalized intersection scenarios with varying numbers of vehicles.

Toward Reference Architectures: A Cloud-Agnostic Data Analytics Platform Empowering Autonomous Systems

“…In the control architecture, u L (k) is a candidate control input, which is suggested by the reinforcement (RL)-learning-based controller. The value of ∆(k) is a result of an optimization process in the supervisor, which minimizes the difference between u(k) and u L (k) and guarantees collision avoidance between the automated and the other vehicles [59]. The constraint between the vehicles through a method of conflict points is formulated.…”