  Autonomous cars have to navigate in dynamic environment which can be full of
uncertainties. The uncertainties can come either from sensor limitations such
as occlusions and limited sensor range, or from probabilistic prediction of
other road participants, or from unknown social behavior in a new area. To
safely and efficiently drive in the presence of these uncertainties, the
decision-making and planning modules of autonomous cars should intelligently
utilize all available information and appropriately tackle the uncertainties so
that proper driving strategies can be generated. In this paper, we propose a
social perception scheme which treats all road participants as distributed
sensors in a sensor network. By observing the individual behaviors as well as
the group behaviors, uncertainties of the three types can be updated uniformly
in a belief space. The updated beliefs from the social perception are then
explicitly incorporated into a probabilistic planning framework based on Model
Predictive Control (MPC). The cost function of the MPC is learned via inverse
reinforcement learning (IRL). Such an integrated probabilistic planning module
with socially enhanced perception enables the autonomous vehicles to generate
behaviors which are defensive but not overly conservative, and socially
compatible. The effectiveness of the proposed framework is verified in
simulation on an representative scenario with sensor occlusions.
