CG-Net: Urban Trajectory Forecasting with Bipartite Graphs for Agents, Scene Context and Candidate Centerlines

Abstract

Trajectory forecasting in urban environments is a critical task that needs to be addressed for the safety of autonomous vehicles, particularly in urban road intersection scenarios, where agents exhibit diverse behaviors mainly due to complex interactions between agents and the environment and the diversity of paths available to the agents. Current state-of-the-art methods do not perform well in urban road intersection scenarios. To address this issue, the proposed novel framework CandidateGraph-Net (CG-Net), improves trajectory prediction in urban road intersection scenarios by encoding the available candidate centerlines at the current location of the target agent. The proposed interaction encoder in CG-Net is inspired by human behavior. It is modeled utilizing a bipartite graph attention network to predict the trajectory of the target agent. It estimates the trajectory in the same way as humans anticipate the trajectory of other vehicles and pedestrians in dynamic environments. The agent embeddings in the interaction encoder at each time step pay attention to nearby agents and surrounding scene elements simultaneously. This enables the model to learn how to prioritize interactions between nearby agents and the environment map. Further, CG-Net’s performance is evaluated using the Argoverse 2 motion forecasting dataset. The results demonstrate its effectiveness in urban road intersection scenarios, with an overall improvement in key metrics such as minFDE and minADE compared to baseline methods. These improvements highlight CG-Net’s ability to perform better motion forecasting in urban road intersection scenarios.