  Autonomous driving in an unregulated urban crowd is an outstanding challenge,
especially, in the presence of many aggressive, high-speed traffic
participants. This paper presents SUMMIT, a high-fidelity simulator that
facilitates the development and testing of crowd-driving algorithms. By
leveraging the open-source OpenStreetMap map database and a heterogeneous
multi-agent motion prediction model developed in our earlier work, SUMMIT
simulates dense, unregulated urban traffic for heterogeneous agents at any
worldwide locations that OpenStreetMap supports. SUMMIT is built as an
extension of CARLA and inherits from it the physics and visual realism for
autonomous driving simulation. SUMMIT supports a wide range of applications,
including perception, vehicle control and planning, and end-to-end learning. We
provide a context-aware planner together with benchmark scenarios and show that
SUMMIT generates complex, realistic traffic behaviors in challenging
crowd-driving settings.
