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In autonomous driving, an accurate understanding of environment, e.g., thevehicle-to-vehicle and vehicle-to-lane interactions, plays a critical role inmany driving tasks such as trajectory prediction and motion planning.Environment information comes from high-definition (HD) map and historicaltrajectories of vehicles. Due to the heterogeneity of the map data andtrajectory data, many data-driven models for trajectory prediction and motionplanning extract vehicle-to-vehicle and vehicle-to-lane interactions in aseparate and sequential manner. However, such a manner may capture biasedinterpretation of interactions, causing lower prediction and planning accuracy.Moreover, separate extraction leads to a complicated model structure and hencethe overall efficiency and scalability are sacrificed. To address the aboveissues, we propose an environment representation, Temporal Occupancy Flow Graph(TOFG). Specifically, the occupancy flow-based representation unifies the mapinformation and vehicle trajectories into a homogeneous data format and enablesa consistent prediction. The temporal dependencies among vehicles can helpcapture the change of occupancy flow timely to further promote modelperformance. To demonstrate that TOFG is capable of simplifying the modelarchitecture, we incorporate TOFG with a simple graph attention (GAT) basedneural network and propose TOFG-GAT, which can be used for both trajectoryprediction and motion planning. Experiment results show that TOFG-GAT achievesbetter or competitive performance than all the SOTA baselines with lesstraining time.

TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving