Proactive MDP-based collision avoidance algorithm for autonomous cars

Denis Osipychev, Duy Tran, Weihua Sheng, Girish Chowdhary, Ruili Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper considers a decision making problem of an autonomous car driving through the intersection with the presence of human-driving cars. A proactive collision avoidance system based on a learning-based MDP model is proposed in contrast to a reactive system. This approach allows to pose the question as an optimization problem. The proposed learning algorithm explicitly describes the interaction with the environment through a probabilistic transition model. The effectiveness of this concept is supported by a variety of simulations which include driving behaviors with Gaussian-distributed velocity, random actions and real human driving.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages983-988
Number of pages6
ISBN (Electronic)9781479987290
DOIs
StatePublished - Oct 2 2015
Externally publishedYes
Event5th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015 - Shenyang, China
Duration: Jun 9 2015Jun 12 2015

Publication series

Name2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015

Other

Other5th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015
CountryChina
CityShenyang
Period6/9/156/12/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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