Voluntary lane-change policy synthesis with reactive control improvisation

Title	Voluntary lane-change policy synthesis with reactive control improvisation
Authors	Jin I. Ge and Richard M. Murray
Source	To appear, 2018 Conference on Decision and Control (CDC)
Abstract	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.
Type	Conference paper
URL	https://www.cds.caltech.edu/~murray/preprints/gm18-cdc s.pdf
DOI
Tag	gm18-cdc
ID	2018b
Funding	NSF VeHICaL
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