Hierarchical Risk-Aware Planning for Modeling Human Driving in Virtual Environments | Nathan Ludlow
Hierarchical Learned Risk-Aware Planning for Modeling Human Driving Behavior in Virtual Environments
Nathan Ludlow, RISS 2023 Cohort
Carnegie Mellon University
Robotics Institute Summer Scholars: https://riss.ri.cmu.edu/
Accurately replicating human driver behavior in simulation is paramount for testing safety-critical systems of autonomous vehicles. Current methodologies predominantly use imitation learning or inverse reinforcement learning to simulate human driving behavior. However, these techniques replicate human driving behavior exclusively in the specific contexts in which the models have been trained and do not account for the differing styles of drivers. Recent work in risk-aware planning has shown effectiveness in replicating the human decision-making process. However, current implementations of risk-aware planners use naive risk predictions from surrounding vehicles, limiting the effective planning horizon time. This restriction confines risk-aware models to reactive maneuvers, and consequently, they cannot effectively model human drivers in simulation. This paper introduces a novel hierarchical risk-aware framework designed to model human drivers in simulation by integrating trajectory predictions of surrounding vehicles to produce accurate risk levels. We subsequently learn a risk-aware reward function from human agents that accounts for differing driving styles.
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