Optimising situation-based behaviour of autonomous vehicles

Abstract

Reinforcement learning (RL) is a method which provides true learning capabilities regarding situation-based actions. RL-systems explore and self-optimise actions for situations in a defined environment. This paper describes the research of a driver (assistance) system based on pure reinforcement learning in the framework of an autonomous vehicle. The target of this research is to determine to what extent RL-based systems serve as an enhancement or even an alternative to classical concepts of autonomous intelligent vehicles such as modelling or neural nets.