In autonomous driving, an accurate understanding of environment, e.g., the vehicle-to-vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks such as trajectory prediction and motion planning. Environment information comes from high-definition (HD) map and historical trajectories of vehicles. Due to the heterogeneity of the map data and trajectory data, many data-driven models for trajectory prediction and motion planning extract vehicle-to-vehicle and vehicle-to-lane interactions in a separate and sequential manner. However, such a manner may capture biased interpretation of interactions, causing lower prediction and planning accuracy. Moreover, separate extraction leads to a complicated model structure and hence the overall efficiency and scalability are sacrificed. To address the above issues, we propose an environment representation, Temporal Occupancy Flow Graph (TOFG). Specifically, the occupancy flow-based representation unifies the map information and vehicle trajectories into a homogeneous data format and enables a consistent prediction. The temporal dependencies among vehicles can help capture the change of occupancy flow timely to further promote model performance. To demonstrate that TOFG is capable of simplifying the model architecture, we incorporate TOFG with a simple graph attention (GAT) based neural network and propose TOFG-GAT, which can be used for both trajectory prediction and motion planning. Experiment results show that TOFG-GAT achieves better or competitive performance than all the SOTA baselines with less training time.

翻译：在自动驾驶中，对环境（如车辆间及车辆与车道间的交互）的精确理解对轨迹预测和运动规划等多项驾驶任务至关重要。环境信息来源于高精地图（HD map）和车辆历史轨迹。由于地图数据与轨迹数据的异构性，许多用于轨迹预测和运动规划的数据驱动模型采用分离且顺序的方式提取车辆间及车辆与车道间的交互。然而，这种方式可能会捕捉到有偏的交互解释，导致预测和规划精度下降。此外，分离式提取导致模型结构复杂，从而牺牲了整体效率和可扩展性。为解决上述问题，我们提出一种环境表征——时间占据流图（Temporal Occupancy Flow Graph, TOFG）。具体而言，基于占据流的表征将地图信息和车辆轨迹统一为同构数据格式，并实现一致预测。车辆间的时间依赖关系有助于及时捕捉占据流的变化，从而进一步提升模型性能。为证明TOFG能够简化模型架构，我们将TOFG与基于简单图注意力（GAT）的神经网络相结合，提出TOFG-GAT，该模型可同时用于轨迹预测和运动规划。实验结果表明，TOFG-GAT在训练时间更短的情况下，达到了优于或与所有最先进基线相当的性能。