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Voluntary lane-change policy synthesis with reactive control improvisation
Abstract In this paper, we propose reactive control …
In this paper, we propose reactive control impro- visation 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 reactive control improvisation to allow an automated car to pursue faster speed while maintaining desired frequency of lane-change maneuvers in various traffic environments.
maneuvers in various traffic environments.  +
Authors Jin I. Ge and Richard M. Murray  +
Funding VeHICaL: Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems +
ID 2018b  +
Source To appear, 2018 Conference on Decision and Control (CDC)  +
Tag gm18-cdc  +
Title Voluntary lane-change policy synthesis with reactive control improvisation +
Type Conference paper  +
Categories Papers
Modification date
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19 June 2018 05:25:18  +
URL
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https://www.cds.caltech.edu/~murray/preprints/gm18-cdc_s.pdf  +
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Voluntary lane-change policy synthesis with reactive control improvisation + Title
 

 

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