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Voluntary lane-change policy synthesis with control improvisation

Ge, Jin I. and Murray, Richard M. (2018) Voluntary lane-change policy synthesis with control improvisation. In: 2018 IEEE Conference on Decision and Control (CDC). IEEE , Piscataway, NJ, pp. 3640-3647. ISBN 9781538613955. https://resolver.caltech.edu/CaltechAUTHORS:20190201-143228935

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Abstract

In this paper, we use control improvisation to synthesize voluntary lane-change policy that meets human preferences under given traffic environments. We first train Markov models to describe traffic patterns and the motion of vehicles responding to such patterns using traffic data. The trained parameters are calibrated using control improvisation to ensure the traffic scenario assumptions are satisfied. Based on the traffic pattern, vehicle response models, and Bayesian switching rules, the lane-change environment for an automated vehicle is modeled as a Markov decision process. Based on human lane-change behaviors, we train a voluntary lane-change policy using explicit-duration Markov decision process. Parameters in the lane-change policy are calibrated through control improvisation to allow an automated car to pursue faster speed while maintaining desired frequency of lane-change maneuvers in various traffic environments.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/cdc.2018.8619616DOIArticle
ORCID:
AuthorORCID
Murray, Richard M.0000-0002-5785-7481
Additional Information:© 2018 IEEE. The authors would like to thank the support from NSF CPS Frontier project 1545126, and thank the insightful discussions with Prof. Sanjit Seshia and Prof. Gabor Orosz. This work is supported by NSF VeHiCal project 1545126.
Funders:
Funding AgencyGrant Number
NSFCNS-1545126
Record Number:CaltechAUTHORS:20190201-143228935
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190201-143228935
Official Citation:J. I. Ge and R. M. Murray, "Voluntary lane-change policy synthesis with control improvisation," 2018 IEEE Conference on Decision and Control (CDC), FL, USA, 2018, pp. 3640-3647. doi: 10.1109/CDC.2018.8619616
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92581
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:01 Feb 2019 23:37
Last Modified:03 Oct 2019 20:46

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