论文标题
使用Waymo打开数据集的基于LSTM的自主驾驶模型
An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset
论文作者
论文摘要
Waymo Open数据集最近发布了，为自动化车辆（AVS）（例如3D检测和跟踪）提供了一个众包的平台。 〜数据集提供了大量的高质量和多源驾驶信息，但学术界的人员对Waymo自动驾驶汽车进行编程的基础驾驶政策更感兴趣，这是由于AV制造商的专有保护而无法访问的。因此，学术研究人员必须做出各种假设来实现其模型或模拟中的AV组件，这可能不能代表现实世界流量中现实的相互作用。因此，本文介绍了一种学习长期记忆（LSTM）模型的方法，用于模仿Waymo自动驾驶模型的行为。已根据平均绝对误差（MAE）对所提出的模型进行了评估。实验结果表明，我们的模型在驱动动作预测中的表现优于几个基线模型。此外，提出了一个可视化工具，用于验证模型的性能。
The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking. While~the dataset provides a large amount of high-quality and multi-source driving information, people in academia are more interested in the underlying driving policy programmed in Waymo self-driving cars, which is inaccessible due to AV manufacturers' proprietary protection. Accordingly, academic researchers have to make various assumptions to implement AV components in their models or simulations, which may not represent the realistic interactions in real-world traffic. Thus, this paper introduces an approach to learn a long short-term memory (LSTM)-based model for imitating the behavior of Waymo's self-driving model. The proposed model has been evaluated based on Mean Absolute Error (MAE). The experimental results show that our model outperforms several baseline models in driving action prediction. In addition, a visualization tool is presented for verifying the performance of the model.
