论文标题
模拟大量混合城市交通中的自动驾驶
Simulating Autonomous Driving in Massive Mixed Urban Traffic
论文作者
论文摘要
在不受监管的城市人群中的自主驾驶是一个杰出的挑战，尤其是在许多积极进取的高速交通参与者面前。本文提出了峰会，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。 Summit模拟了OpenStreetMap支持的任何全球地点的密集，不受监管的城市交通。 Summit的核心是一种多代理运动模型Gamma，该模型对异质交通代理的行为进行了建模，以及实时POMDP计划者Context-POMDP，可作为驾驶专家。峰会是作为卡拉的扩展而建造的，并从中继承了自主驾驶模拟的物理和视觉现实主义。 Summit支持广泛的应用程序，包括感知，车辆控制或计划以及端到端学习。我们使用其流量运动预测的准确性在各种现实世界数据集上验证了我们的运动模型的现实主义。我们还提供了几种现实世界的基准方案，以表明峰会模拟复杂，现实的流量行为，并且上下文POMDP在挑战众驾驶的设置中安全有效地驱动器。
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.
