  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. SUMMIT
simulates dense, unregulated urban traffic at any worldwide locations as
supported by the OpenStreetMap. The core of SUMMIT is a multi-agent motion
model, GAMMA, that models the behaviours of heterogeneous traffic agents, and a
real-time POMDP planner, Context-POMDP, that serves as a driving expert. SUMMIT
is built as an extension of CARLA and inherits from it the physical and visual
realism for autonomous driving simulation. SUMMIT supports a wide range of
applications, including perception, vehicle control or planning, and end-to-end
learning. We validate the realism of our motion model using its traffic motion
prediction accuracy on various real-world data sets. We also provide several
real-world benchmark scenarios to show that SUMMIT simulates complex, realistic
traffic behaviors, and Context-POMDP drives safely and efficiently in
challenging crowd-driving settings.
