  Autonomous vehicles have to navigate the surrounding environment with partial
observability of other objects sharing the road. Sources of uncertainty in
autonomous vehicle measurements include sensor fusion errors, limited sensor
range due to weather or object detection latency, occlusion, and hidden
parameters such as other human driver intentions. Behavior planning must
consider all sources of uncertainty in deciding future vehicle maneuvers. This
paper presents a scalable framework for risk-averse behavior planning under
uncertainty by incorporating QMDP, unscented transform, and Monte Carlo tree
search (MCTS). It is shown that upper confidence bound (UCB) for expanding the
tree results in noisy Q-value estimates by the MCTS and a degraded performance
of QMDP. A modification to action selection procedure in MCTS is proposed to
achieve robust performance.
