  Dynamic game arises as a powerful paradigm for multi-robot planning, for
which safety constraint satisfaction is crucial. Constrained stochastic games
are of particular interest, as real-world robots need to operate and satisfy
constraints under uncertainty. Existing methods for solving stochastic games
handle chance constraints using exponential penalties with hand-tuned weights.
However, finding a suitable penalty weight is nontrivial and requires trial and
error. In this paper, we propose the chance-constrained iterative
linear-quadratic stochastic games (CCILQGames) algorithm. CCILQGames solves
chance-constrained stochastic games using the augmented Lagrangian method. We
evaluate our algorithm in three autonomous driving scenarios, including merge,
intersection, and roundabout. Experimental results and Monte Carlo tests show
that CCILQGames can generate safe and interactive strategies in stochastic
environments.
