论文标题
交通：学会产生多样化和现实的交通情况
TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios
论文作者
论文摘要
多样化和现实的交通情况对于评估模拟中自动驾驶系统的AI安全至关重要。这项工作介绍了一种数据驱动的方法，称为流量场景的流动gen。它从现实世界中收集的零散的人类驾驶数据中学习，然后可以产生现实的交通情况。流动犬是具有编码器架构的自动回归生成模型。在每次自回旋迭代中，它首先使用注意机制编码当前的流量上下文，然后解码车辆的初始状态，然后再产生其长轨迹。我们根据车辆放置和轨迹评估训练有素的模型，并显示出对基准的实质性改进。通过添加新车辆并扩展了零散的轨迹，交通还可以用于增加现有的交通情况。我们进一步证明，随着互动培训环境可以改善从强化学习中学到的驾驶政策的性能和安全性，将生成的方案导入模拟器。可以在https://metadriverse.github.io/trafficgen上获得更多项目资源
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
