Abstract

This paper considers the problem of controlling an autonomous vehicle that must share the road with human-driven cars.We present proactive collision avoidance algorithms that take into account the expressed intent of human driven cars and minimize the need for sudden braking or other purely reactive sudden actions. The presented algorithm utilizes multi-stage Gaussian Processes (GPs) in order to learn the transition model for each vehicle given the intention of the vehicle's driver. It further updates the trajectory predictions on-line to provide an intention-based trajectory prediction and collision avoidance adapted to various driving manners and road/weather conditions. The effectiveness of this concept is demonstrated by a variety of simulations utilizing real human driving data in various scenarios including an intersection and a highway. The experiments are done in a specially developed driving simulation and a highly realistic third-party car simulator.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2974-2979
Number of pages6
ISBN (Electronic)9781509059928
DOIs
StatePublished - Jun 29 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2017 American Control Conference, ACC 2017
CountryUnited States
CitySeattle
Period5/24/175/26/17

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Osipychev, D., Tran, D., Sheng, W., & Chowdhary, G. (2017). Human intention-based collision avoidance for autonomous cars. In 2017 American Control Conference, ACC 2017 (pp. 2974-2979). [7963403] (Proceedings of the American Control Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2017.7963403