Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs

Understand

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 of each road-agent.
• Our formulation represents the proximity between the road agents using a weighted dynamic geometric graph (DGG).
• We use a two-stream graph-LSTM network to perform traffic forecasting using these weighted DGGs.