  Lane-change maneuver has always been a challenging task for both manual and
autonomous driving, especially in an urban setting. In particular, the
uncertainty in predicting the behavior of other vehicles on the road leads to
indecisive actions while changing lanes, which, might result in traffic
congestion and cause safety concerns. This paper analyzes the factors related
to uncertainty such as speed range change and lane change so as to design a
predictive Markov decision process for lane-change maneuver in the urban
setting. A hidden Markov model is developed for modeling uncertainties of
surrounding vehicles. The reward model uses the crash probabilities and the
feasibility/distance to the goal as primary parameters. Numerical simulation
and analysis of two traffic scenarios are completed to demonstrate the
effectiveness of the proposed approach.
