Risk-Constrained Interactive Safety under Behavior Uncertainty for
  Autonomous Driving
  Balancing safety and efficiency when planning in dense traffic is
challenging. Interactive behavior planners incorporate prediction uncertainty
and interactivity inherent to these traffic situations. Yet, their use of
single-objective optimality impedes interpretability of the resulting safety
goal. Safety envelopes which restrict the allowed planning region yield
interpretable safety under the presence of behavior uncertainty, yet, they
sacrifice efficiency in dense traffic due to conservative driving. Studies show
that humans balance safety and efficiency in dense traffic by accepting a
probabilistic risk of violating the safety envelope. In this work, we adopt
this safety objective for interactive planning. Specifically, we formalize this
safety objective, present the Risk-Constrained Robust Stochastic Bayesian Game
modeling interactive decisions satisfying a maximum risk of violating a safety
envelope under uncertainty of other traffic participants' behavior and solve it
using our variant of Multi-Agent Monte Carlo Tree Search. We demonstrate in
simulation that our approach outperforms baselines approaches, and by reaching
the specified violation risk level over driven simulation time, provides an
interpretable and tunable safety objective for interactive planning.
