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.
Funder
Nemzeti Kutatási Fejlesztési és Innovációs Hivatal
Magyarország Kormánya
Magyar Tudományos Akadémia
Nemzeti Kutatási, Fejlesztési és Innovaciós Alap
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
10 articles.
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