  This paper presents GAMMA, a general motion prediction model that enables
large-scale real-time simulation and planning for autonomous driving. GAMMA
models heterogeneous, interactive traffic agents. They operate under diverse
road conditions, with various geometric and kinematic constraints. GAMMA treats
the prediction task as constrained optimization in traffic agents' velocity
space. The objective is to optimize an agent's driving performance, while
obeying all the constraints resulting from the agent's kinematics, collision
avoidance with other agents, and the environmental context. Further, GAMMA
explicitly conditions the prediction on human behavioral states as parameters
of the optimization model, in order to account for versatile human behaviors.
We evaluated GAMMA on a set of real-world benchmark datasets. The results show
that GAMMA achieves high prediction accuracy on both homogeneous and
heterogeneous traffic datasets, with sub-millisecond execution time. Further,
the computational efficiency and the flexibility of GAMMA enable (i) simulation
of mixed urban traffic at many locations worldwide and (ii) planning for
autonomous driving in dense traffic with uncertain driver behaviors, both in
real-time. The open-source code of GAMMA is available online.
