论文标题
RES-GCNN：用于人类轨迹预测的轻量级残留图卷积神经网络
Res-GCNN: A Lightweight Residual Graph Convolutional Neural Networks for Human Trajectory Forecasting
论文作者
论文摘要
自动驾驶车辆（ADVS）寄希望于解决交通拥堵问题并减少交通事故的数量。对ADV的其他交通代理的准确预测对于实现安全有效的驾驶至关重要。尤其是行人，由于他们的复杂社会行为和随机移动的模式，对预测的预测更具挑战性。我们提出了一个残留的图形卷积神经网络（RES-GCNN），该神经网络（RES-GCNN）通过为当前场景使用构造图的相邻矩阵来对PEDES-Trians的交互作用进行建模。尽管所提出的RES-GCNN非常轻巧，只有6.4千公斤的参数就参数尺寸胜过所有其他方法，但我们的实验结果表明，最终流离失所误差（FDE）的最终置换状态的提高了13.3％，达到0.65米。至于平均分配误差（ADE），我们达到了次优的结果（值为0.37米），这也非常有竞争力。用NVIDIA GEFORCE RTX1080TI GPU在平台中评估RES-GCNN，其整个数据集的平均推理时间仅为2.2微秒。与其他方法相比，所提出的方法显示出强大的船上应用程序可能性，以预测准确性和时间效率。该代码将在GitHub上公开提供。
Autonomous driving vehicles (ADVs) hold great hopes to solve traffic congestion problems and reduce the number of traffic accidents. Accurate trajectories prediction of other traffic agents around ADVs is of key importance to achieve safe and efficient driving. Pedestrians, particularly, are more challenging to forecast due to their complex social in-teractions and randomly moving patterns. We propose a Residual Graph Convolutional Neural Network (Res-GCNN), which models the interactive behaviors of pedes-trians by using the adjacent matrix of the constructed graph for the current scene. Though the proposed Res-GCNN is quite lightweight with only about 6.4 kilo parameters which outperforms all other methods in terms of parameters size, our experimental results show an improvement over the state of art by 13.3% on the Final Displacement Error (FDE) which reaches 0.65 meter. As for the Average Dis-placement Error (ADE), we achieve a suboptimal result (the value is 0.37 meter), which is also very competitive. The Res-GCNN is evaluated in the platform with an NVIDIA GeForce RTX1080Ti GPU, and its mean inference time of the whole dataset is only about 2.2 microseconds. Compared with other methods, the proposed method shows strong potential for onboard application accounting for forecasting accuracy and time efficiency. The code will be made publicly available on GitHub.
