An ego vehicle following a virtual lead vehicle planned route is an essential
component when autonomous and non-autonomous vehicles interact. Yet, there is a
question about the driver's ability to follow the planned lead vehicle route.
Thus, predicting the trajectory of the ego vehicle route given a lead vehicle
route is of interest. We introduce a new dataset, the FollowMe dataset, which
offers a motion and behavior prediction problem by answering the latter
question of the driver's ability to follow a lead vehicle. We also introduce a
deep spatio-temporal graph model FollowMe-STGCNN as a baseline for the dataset.
In our experiments and analysis, we show the design benefits of FollowMe-STGCNN
in capturing the interactions that lie within the dataset. We contrast the
performance of FollowMe-STGCNN with prior motion prediction models showing the
need to have a different design mechanism to address the lead vehicle following
settings.

我们介绍了一个名为 FollowMe dataset 的新数据集，同时提出了用于数据集的基线模型 FollowMe-STGCNN，该模型拥有不同的设计机制以应对跟随先导车的情况；在实验中对比了 FollowMe-STGCNN 和先前的运动预测模型的表现。

FollowMe：自主驾驶汽车环境中的车辆行为预测

FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings

Video captioning is a challenging task that requires a deep understanding of
visual scenes. State-of-the-art methods generate captions using either
scene-level or object-level information but without explicitly modeling object
interactions. Thus, they often fail to make visually grounded predictions, and
are sensitive to spurious correlations. In this paper, we propose a novel
spatio-temporal graph model for video captioning that exploits object
interactions in space and time. Our model builds interpretable links and is
able to provide explicit visual grounding. To avoid unstable performance caused
by the variable number of objects, we further propose an object-aware knowledge
distillation mechanism, in which local object information is used to regularize
global scene features. We demonstrate the efficacy of our approach through
extensive experiments on two benchmarks, showing our approach yields
competitive performance with interpretable predictions.

本文提出一种基于时空图模型的视频字幕生成算法，利用物体间相互作用关系提供显式的视觉表征，通过一种对象感知的知识蒸馏机制，可以在不稳定的性能中实现对象的稳定预测。经过对两个基准测试的广泛实验，展示了我们方法的有效性和可解释性预测的竞争性能。