Trajectory prediction is crucial for autonomous vehicles. The planning system
not only needs to know the current state of the surrounding objects but also
their possible states in the future. As for vehicles, their trajectories are
significantly influenced by the lane geometry and how to effectively use the
lane information is of active interest. Most of the existing works use
rasterized maps to explore road information, which does not distinguish
different lanes. In this paper, we propose a novel instance-aware
representation for lane representation. By integrating the lane features and
trajectory features, a goal-oriented lane attention module is proposed to
predict the future locations of the vehicle. We show that the proposed lane
representation together with the lane attention module can be integrated into
the widely used encoder-decoder framework to generate diverse predictions. Most
importantly, each generated trajectory is associated with a probability to
handle the uncertainty. Our method does not suffer from collapsing to one
behavior modal and can cover diverse possibilities. Extensive experiments and
ablation studies on the benchmark datasets corroborate the effectiveness of our
proposed method. Notably, our proposed method ranks third place in the
Argoverse motion forecasting competition at NeurIPS 2019.

本文提出一种基于实例感知的车道表示法和目标導向车道注意力模型，结合车道特征和车辆轨迹特征进行车辆位置预测，并在 NeurIPS 2019 的 Argoverse 运动预测竞赛中获得第三名，实验证明了模型的有效性。