Synthesizing voluntary lane-change policy using control improvisation

In this work, we propose control improvisation to synthesize voluntary lane-change policy that meets human preferences under given traffic environments. We first train Markov models to describe the lane-change environment for an automated vehicle under assumed traffic patterns. Parameters in the environment model are trained using traffic data and calibrated using control improvisation. Then, based on human lane-change behavior, we train a...