Scene Induced Multi-Modal Trajectory Forecasting via Planning
This addresses trajectory prediction for autonomous systems in dynamic environments, but it appears incremental as it combines existing techniques like goal prediction and inverse reinforcement learning.
The paper tackles multi-modal trajectory forecasting of agents in unknown scenes by framing it as a planning problem, achieving generalizability to novel scenes on the Stanford drone dataset.
We address multi-modal trajectory forecasting of agents in unknown scenes by formulating it as a planning problem. We present an approach consisting of three models; a goal prediction model to identify potential goals of the agent, an inverse reinforcement learning model to plan optimal paths to each goal, and a trajectory generator to obtain future trajectories along the planned paths. Analysis of predictions on the Stanford drone dataset, shows generalizability of our approach to novel scenes.