Abstract
The training, testing, and deployment, of autonomous vehicles requires realistic and efficient simulators. Moreover, because of the high variability between different problems presented in different autonomous systems, these simulators need to be easy to use, and easy to modify. To address these problems we introduce TorchDriveSim and its benchmark extension TorchDriveEnv. TorchDriveEnv is a lightweight reinforcement learning benchmark programmed entirely in Python, which can be modified to test a number of different factors in learned vehicle behavior, including the effect of varying kinematic models, agent types, and traffic control patterns. Most importantly unlike many replay based simulation approaches, TorchDriveEnv is fully integrated with a state of the art behavioral simulation API. This allows users to train and evaluate driving models alongside data driven Non-Playable Characters (NPC) whose initializations and driving behavior are reactive, realistic, and diverse. We illustrate the efficiency and simplicity of TorchDriveEnv by evaluating common reinforcement learning baselines in both training and validation environments. Our experiments show that TorchDriveEnv is easy to use, but difficult to solve.
Abstract (translated)
自动驾驶车辆的训练、测试和部署需要真实的和高效的仿真器。此外，由于不同自治系统中呈现的问题之间的差异很大，这些仿真器需要易于使用且易于修改。为解决这些问题，我们引入了TorchDriveSim和其基准扩展TorchDriveEnv。TorchDriveEnv是一个完全用Python编写的轻量级强化学习基准，可以修改以测试学习到的车辆行为的多个不同因素，包括影响运动模型的不同，代理类型和交通控制模式。最重要的是，与许多基于回放的仿真方法不同，TorchDriveEnv完全集成了最先进的 behavioral simulation API。这使得用户可以在数据驱动的非玩家角色（NPC）的初始化和驾驶行为反应实时、真实和多样化的同时训练和评估驾驶模型。我们通过在训练和验证环境中评估常见的强化学习基准来说明TorchDriveEnv的易用性和简单性。我们的实验结果表明，TorchDriveEnv很容易使用，但很难解决。
URL
https://arxiv.org/abs/2405.04491
