Belief state planning for autonomously navigating urban intersections

Maxime Bouton, Akansel Cosgun, Mykel J. Kochenderfer

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

27 Citations (Scopus)

Abstract

Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rapid changes in driver behavior. Providing an efficient strategy to navigate through urban intersections is a difficult task. This paper frames the problem of navigating unsignalized intersections as a partially observable Markov decision process (POMDP) and solves it using a Monte Carlo sampling method. Empirical results in simulation show that the resulting policy outperforms a threshold-based heuristic strategy on several relevant metrics that measure both safety and efficiency.

Original languageEnglish
Title of host publication2017 IEEE Intelligent Vehicles Symposium (IV 2017)
EditorsWei-Bin Zhang, Arnaud de La Fortelle, Tankut Acarman, Ming Yang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages825-830
Number of pages6
ISBN (Electronic)9781509048045, 9781509048038
ISBN (Print)9781509048052
DOIs
Publication statusPublished - 28 Jul 2017
Externally publishedYes
EventIntelligent Vehicles Symposium 2017 - Redondo Beach, United States of America
Duration: 11 Jun 201714 Jun 2017
Conference number: 28th
http://iv2017.org/
https://ieeexplore.ieee.org/xpl/conhome/7987634/proceeding (Proceedings)

Conference

ConferenceIntelligent Vehicles Symposium 2017
Abbreviated titleIEEE IV 2017
CountryUnited States of America
CityRedondo Beach
Period11/06/1714/06/17
Internet address

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