Title: Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning,
Abstract: Autonomous vehicles (AVs) are on the road. To safely and efficiently interact
with other road participants, AVs have to accurately predict the behavior of
surrounding vehicles and plan accordingly. Such prediction should be
probabilistic, to address the uncertainties in human behavior. Such prediction
should also be interactive, since the distribution over all possible
trajectories of the predicted vehicle depends not only on historical
information, but also on future plans of other vehicles that interact with it.
To achieve such interaction-aware predictions, we propose a probabilistic
prediction approach based on hierarchical inverse reinforcement learning (IRL).
First, we explicitly consider the hierarchical trajectory-generation process of
human drivers involving both discrete and continuous driving decisions. Based
on this, the distribution over all future trajectories of the predicted vehicle
is formulated as a mixture of distributions partitioned by the discrete
decisions. Then we apply IRL hierarchically to learn the distributions from
real human demonstrations. A case study for the ramp-merging driving scenario
is provided. The quantitative results show that the proposed approach can
accurately predict both the discrete driving decisions such as yield or pass as
well as the continuous trajectories.