论文标题
通过程序生成改善端到端驱动的概括
Improving the Generalization of End-to-End Driving through Procedural Generation
论文作者
论文摘要
在过去的几年中，人们对基于学习的自动驾驶系统越来越兴趣。为了确保安全，在部署在现实世界中之前，首先在模拟器中开发和验证此类系统。但是，大多数现有的驾驶模拟器仅包含固定的场景和有限数量的可配置设置。对于基于学习的驾驶系统而言，这可能很容易导致过度拟合的问题，以及缺乏看不见的场景的概括能力。为了更好地评估和改善端到端驾驶的概括，我们推出了一个名为PGDRIVE的开放式且高度可配置的驾驶模拟器，遵循程序生成的关键功能。拟议中的一代算法首先通过基本路障的采样来生成各种路线网络。然后，它们变成了互动培训环境，在这些环境中，附近的具有现实运动学的车辆的交通流量被渲染。我们验证了越来越多的程序生成场景的培训可显着改善代理在不同的交通密度和道路网络的场景中的概括。可以在模拟器上进一步构建许多应用程序，例如多代理交通模拟和安全驾驶基准。为了促进端到端驾驶的联合研究工作，我们在https://decisionforce.github.io/pgdrive上发布了模拟器和验证模型
Over the past few years there is a growing interest in the learning-based self driving system. To ensure safety, such systems are first developed and validated in simulators before being deployed in the real world. However, most of the existing driving simulators only contain a fixed set of scenes and a limited number of configurable settings. That might easily cause the overfitting issue for the learning-based driving systems as well as the lack of their generalization ability to unseen scenarios. To better evaluate and improve the generalization of end-to-end driving, we introduce an open-ended and highly configurable driving simulator called PGDrive, following a key feature of procedural generation. Diverse road networks are first generated by the proposed generation algorithm via sampling from elementary road blocks. Then they are turned into interactive training environments where traffic flows of nearby vehicles with realistic kinematics are rendered. We validate that training with the increasing number of procedurally generated scenes significantly improves the generalization of the agent across scenarios of different traffic densities and road networks. Many applications such as multi-agent traffic simulation and safe driving benchmark can be further built upon the simulator. To facilitate the joint research effort of end-to-end driving, we release the simulator and pretrained models at https://decisionforce.github.io/pgdrive
