论文标题
Carla真实的交通情况 - 自动驾驶的新颖培训场和基准
CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous driving
论文作者
论文摘要
这项工作介绍了基于现实世界流量的Carla模拟器中的交互式流量方案。我们专注于持续几秒钟的战术任务，这对于当前的控制方法特别具有挑战性。 Carla真实的交通情况（CRT）旨在是自动驾驶系统的培训和测试场。为此，我们根据允许的许可开放代码，并介绍一组基线政策。 CRT结合了交通情况的现实主义和模拟的灵活性。我们使用它使用增强学习算法来训练代理。我们展示了如何获得竞争政策，并通过实验评估观察类型和奖励方案如何影响训练过程和结果代理的行为。
This work introduces interactive traffic scenarios in the CARLA simulator, which are based on real-world traffic. We concentrate on tactical tasks lasting several seconds, which are especially challenging for current control methods. The CARLA Real Traffic Scenarios (CRTS) is intended to be a training and testing ground for autonomous driving systems. To this end, we open-source the code under a permissive license and present a set of baseline policies. CRTS combines the realism of traffic scenarios and the flexibility of simulation. We use it to train agents using a reinforcement learning algorithm. We show how to obtain competitive polices and evaluate experimentally how observation types and reward schemes affect the training process and the resulting agent's behavior.
