  When a vehicle drives on the road, its behaviors will be affected by
surrounding vehicles. Prediction and decision should not be considered as two
separate stages because all vehicles make decisions interactively. This paper
constructs the multi-vehicle driving scenario as a non-zero-sum game and
proposes a novel game control framework, which consider prediction, decision
and control as a whole. The mutual influence of interactions between vehicles
is considered in this framework because decisions are made by Nash equilibrium
strategy. To efficiently obtain the strategy, ADP, a model-based reinforcement
learning method, is used to solve coupled Hamilton-Jacobi-Bellman equations.
Driving performance is evaluated by tracking, efficiency, safety and comfort
indices. Experiments show that our algorithm could drive perfectly by directly
controlling acceleration and steering angle. Vehicles could learn interactive
behaviors such as overtaking and pass. In summary, we propose a non-zero-sum
game framework for modeling multi-vehicle driving, provide an effective way to
solve the Nash equilibrium driving strategy, and validate at non-signalized
intersections.
