TOFG: Temporal Occupancy Flow Graph for Prediction and Planning in Autonomous Driving

In autonomous driving, an accurate understanding of the 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 maps and historical trajectories of vehicles. To interpret and utilize such information for the two aforementioned driving tasks, both learning-based models and mathematical methods have been proposed, while these existing approaches suffer from the following issues. Specifically, due to the heterogeneity of the map data and trajectory data, many learning-based models extract vehicle-to-vehicle and vehicle-to-lane interactions in a separate and sequential manner, which may capture biased interpretations of interactions, causing lower prediction and planning accuracy. As for the mathematical models, the environment information is mainly used to characterize the collision-free space, while the interactions are largely ignored. To address the above issues, we propose an environment representation, called Temporal Occupancy Flow Graph (TOFG). Specifically, TOFG unifies the map information and vehicle trajectories into a homogeneous data format and enables a consistent prediction. We incorporate TOFG with a graph attention (GAT) based neural network and propose TOFG-GAT to demonstrate the benefit of TOFG to learning-based trajectory prediction and motion planning. Moreover, we design and implement an interaction-aware sampling strategy based on TOFG to improve the mathematical sampling-based motion planning algorithms. Extensive experiment results show that our proposed TOFG can contribute to the trajectory prediction and motion planning tasks by improving the quality of the generated trajectory and computation efficiency for both the learning-based and mathematical models.