论文标题
重新涂抹：一个基于深度学习的框架，用于自动驾驶应用中的多模式运动预测
ReCoAt: A Deep Learning-based Framework for Multi-Modal Motion Prediction in Autonomous Driving Application
论文作者
论文摘要
本文提出了一个新型的深度学习框架，用于多模式运动预测。该框架由三个部分组成：重复的神经网络，以处理目标代理的运动过程，卷积神经网络处理栅格化环境表示，以及基于距离的注意机制来处理不同代理之间的相互作用。我们在大规模的现实世界驾驶数据集，Waymo Open Motion数据集上验证了所提出的框架，并将其性能与标准测试基准中的其他方法进行比较。定性结果表明，我们的模型给出的预测轨迹是准确，多样的，并且根据道路结构。标准基准测试的定量结果表明，我们的模型在预测准确性和其他评估指标方面优于其他基线方法。提议的框架是2021 Waymo Open Datat Motion预测挑战的第二名。
This paper proposes a novel deep learning framework for multi-modal motion prediction. The framework consists of three parts: recurrent neural networks to process the target agent's motion process, convolutional neural networks to process the rasterized environment representation, and a distance-based attention mechanism to process the interactions among different agents. We validate the proposed framework on a large-scale real-world driving dataset, Waymo open motion dataset, and compare its performance against other methods on the standard testing benchmark. The qualitative results manifest that the predicted trajectories given by our model are accurate, diverse, and in accordance with the road structure. The quantitative results on the standard benchmark reveal that our model outperforms other baseline methods in terms of prediction accuracy and other evaluation metrics. The proposed framework is the second-place winner of the 2021 Waymo open dataset motion prediction challenge.
