Simulating Autonomous Driving in Massive Mixed Urban Traffic
This addresses the problem of developing and testing autonomous driving algorithms in complex urban environments for researchers and engineers, though it is incremental as an extension of existing simulators.
The paper tackles the challenge of autonomous driving in dense, unregulated urban traffic by presenting SUMMIT, a high-fidelity simulator that facilitates algorithm development and testing, with validation showing realistic traffic behaviors and safe, efficient driving in benchmark scenarios.
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.