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SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic
arXiv - CS - Multiagent Systems Pub Date : 2019-11-11 , DOI: arxiv-1911.04074
Panpan Cai, Yiyuan Lee, Yuanfu Luo, David Hsu

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.

中文翻译:

SUMMIT:大规模混合交通中的城市驾驶模拟器

在不受管制的城市人群中自动驾驶是一项突出的挑战,尤其是在有许多激进的高速交通参与者的情况下。本文介绍了 SUMMIT,这是一种高保真模拟器,可促进人群驾驶算法的开发和测试。通过利用开源 OpenStreetMap 地图数据库和我们早期工作中开发的异构多智能体运动预测模型,SUMMIT 在 OpenStreetMap 支持的全球任何位置为异构智能体模拟密集、不受管制的城市交通。SUMMIT 是作为 CARLA 的扩展而构建的,并从中继承了自动驾驶模拟的物理和视觉真实感。SUMMIT 支持广泛的应用,包括感知、车辆控制和规划以及端到端学习。
更新日期:2020-03-16
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