@article{Guan2018, 
author = {Yang Guan and Shengbo Eben Li and Jingliang Duan and Wenjun Wang and Bo Cheng},
title = {Markov probabilistic decision making of self-driving cars in highway with random traffic flow: a simulation study},
year = {2018},
journal = {Journal of Intelligent and Connected Vehicles},
volume = {1},
number = {2},
pages = {77-84},
keywords = {Markov decision process, Decision-making, Dynamic programming, Self-driving cars},
url = {https://www.sciopen.com/article/10.1108/JICV-01-2018-0003},
doi = {10.1108/JICV-01-2018-0003},
abstract = {PurposeDecision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.Design/methodology/approachIn this research, a probabilistic decision-making method based on the Markov decision process (MDP) is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data. The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states, actions and basic models. Transition and reward models are defined by using a complete prediction model of the surrounding cars. An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.FindingsResults show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy.Originality/valueThis paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.}
}