Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous driving systems in simulation. This work introduces a data-driven method called TrafficGen for traffic scenario generation. It learns from the fragmented human driving data collected in the real world and then can generate realistic traffic scenarios. TrafficGen is an autoregressive generative model with an encoder-decoder architecture. In each autoregressive iteration, it first encodes the current traffic context with the attention mechanism and then decodes a vehicle's initial state followed by generating its long trajectory. We evaluate the trained model in terms of vehicle placement and trajectories and show substantial improvements over baselines. TrafficGen can be also used to augment existing traffic scenarios, by adding new vehicles and extending the fragmented trajectories. We further demonstrate that importing the generated scenarios into a simulator as interactive training environments improves the performance and the safety of driving policy learned from reinforcement learning. More project resource is available at https://metadriverse.github.io/trafficgen

翻译：多样且逼真的交通场景对于在仿真环境中评估自动驾驶系统的AI安全性至关重要。本文提出了一种名为TrafficGen的数据驱动方法，用于交通场景生成。该方法从真实世界收集的碎片化人类驾驶数据中学习，进而能够生成逼真的交通场景。TrafficGen是一个具有编码器-解码器架构的自回归生成模型。在每个自回归迭代步骤中，它首先通过注意力机制编码当前的交通上下文，然后解码生成车辆的初始状态，随后生成其长时程轨迹。我们从车辆放置和轨迹两方面对训练后的模型进行了评估，并展示了相较于基线方法的显著改进。TrafficGen还可用于增强现有交通场景，通过添加新车辆并延长碎片化轨迹来实现。我们进一步证明，将生成的场景作为交互式训练环境导入仿真器，可以提升基于强化学习训练的驾驶策略的性能和安全性。更多项目资源请访问https://metadriverse.github.io/trafficgen。