SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic
This addresses the problem of simulating dense, unregulated urban traffic for autonomous driving researchers, though it is incremental as it builds on existing tools like CARLA and OpenStreetMap.
The paper tackles the challenge of autonomous driving in unregulated urban crowds with aggressive, high-speed traffic by presenting SUMMIT, a high-fidelity simulator that generates complex, realistic traffic behaviors for developing and testing crowd-driving algorithms.
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.