Trajectory prediction has been a long-standing problem in intelligent systems such as autonomous driving and robot navigation. Recent state-of-the-art models trained on large-scale benchmarks have been pushing the limit of performance rapidly, mainly focusing on improving prediction accuracy. However, those models put less emphasis on efficiency, which is critical for real-time applications. This paper proposes an attention-based graph model named GATraj with a much higher prediction speed. Spatial-temporal dynamics of agents, e.g., pedestrians or vehicles, are modeled by attention mechanisms. Interactions among agents are modeled by a graph convolutional network. We also implement a Laplacian mixture decoder to mitigate mode collapse and generate diverse multimodal predictions for each agent. Our model achieves performance on par with the state-of-the-art models at a much higher prediction speed tested on multiple open datasets.
翻译:在诸如自主驾驶和机器人导航等智能系统中,轨迹预测是一个长期存在的问题。最近经过大规模基准培训的最先进的模型迅速推进了性能极限,主要侧重于提高预测准确性。然而,这些模型不太强调效率,而效率对于实时应用至关重要。本文件建议采用一个以关注为基础的图表模型,名为GATraj,预测速度要快得多。用关注机制模拟各种物剂(例如行人或车辆)的空间时空动态。各种物剂之间的相互作用以图示共变网络为模型。我们还采用了拉普拉西亚混合解密器,以缓解模式崩溃,并为每种物剂生成多种多式联运预测。我们的模型在多个开放数据集测试的预测速度上取得了与最新模型相当的业绩。