论文标题
S2TNET：自主驾驶中轨迹预测的时空变压器网络
S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving
论文作者
论文摘要
为了安全和合理地参与密集和异质的交通，自动驾驶汽车需要充分分析周围交通代理的运动模式，并准确地预测其未来的轨迹。这很具有挑战性，因为交通代理的轨迹不仅受交通代理本身的影响，而且还受到彼此的空间互动的影响。以前的方法通常依赖于长短期内存网络（LSTMS）的顺序逐步处理，而仅提取单一类型交通代理的空间邻居之间的相互作用。我们提出了时空变压器网络（S2TNET），该网络通过时空变压器对时空相互作用进行建模，并通过时间变压器处理临时序列。我们将其他类别，形状和标题信息输入到我们的网络中，以处理交通代理的异质性。在Apolloscape轨迹数据集上，所提出的方法优于最先进的方法，在平均值和最终位移误差的加权总和上都超过7％。我们的代码可在https://github.com/chenghuang66/s2tnet上找到。
To safely and rationally participate in dense and heterogeneous traffic, autonomous vehicles require to sufficiently analyze the motion patterns of surrounding traffic-agents and accurately predict their future trajectories. This is challenging because the trajectories of traffic-agents are not only influenced by the traffic-agents themselves but also by spatial interaction with each other. Previous methods usually rely on the sequential step-by-step processing of Long Short-Term Memory networks (LSTMs) and merely extract the interactions between spatial neighbors for single type traffic-agents. We propose the Spatio-Temporal Transformer Networks (S2TNet), which models the spatio-temporal interactions by spatio-temporal Transformer and deals with the temporel sequences by temporal Transformer. We input additional category, shape and heading information into our networks to handle the heterogeneity of traffic-agents. The proposed methods outperforms state-of-the-art methods on ApolloScape Trajectory dataset by more than 7\% on both the weighted sum of Average and Final Displacement Error. Our code is available at https://github.com/chenghuang66/s2tnet.
