Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning

Understand

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.