2020
Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs
RA-L 2020
We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning. We predict both the low-level information (future trajectories) as well as the high-level information (road-agent behavior) from the extracted trajectory o