TOFG: A Unified and Fine-Grained Environment Representation in
  Autonomous Driving
Zihao Wen Yifan Zhang Xinhong Chen Jianping Wang
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.