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Available. Open Access. Open Access
Article
Publication date: 18 October 2018

Yang Guan, Shengbo Eben Li, Jingliang Duan, Wenjun Wang and Bo Cheng

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…

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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.

Details

Journal of Intelligent and Connected Vehicles, vol. 1 no. 2
Type: Research Article
ISSN: 2399-9802

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