Citation

Guan Y, Li SE, Duan J, Wang W, Cheng B. J. Intell. Connect. Veh. 2018; 1(2): 77-84.

Copyright

(Copyright © 2018, Emerald Group Publishing)

DOI

10.1108/JICV-01-2018-0003

PMID

unavailable

Abstract

PURPOSE Decision-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/APPROACH In 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.

FINDINGS Results show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy.

ORIGINALITY/VALUE This 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.

Language: en

Keywords

Decision-making; Dynamic programming; Markov decision process; Self-driving cars