  Autonomous vehicles (AVs) need to interact with other traffic participants
who can be either cooperative or aggressive, attentive or inattentive. Such
different characteristics can lead to quite different interactive behaviors.
Hence, to achieve safe and efficient autonomous driving, AVs need to be aware
of such uncertainties when they plan their own behaviors. In this paper, we
formulate such a behavior planning problem as a partially observable Markov
Decision Process (POMDP) where the cooperativeness of other traffic
participants is treated as an unobservable state. Under different
cooperativeness levels, we learn the human behavior models from real traffic
data via the principle of maximum likelihood. Based on that, the POMDP problem
is solved by Monte-Carlo Tree Search. We verify the proposed algorithm in both
simulations and real traffic data on a lane change scenario, and the results
show that the proposed algorithm can successfully finish the lane changes
without collisions.
