Motion Forecasting is a broader generalization of trajectory prediction — predicting the future state (position, velocity, pose, intention) of dynamic agents in an environment, critical for safety-critical autonomous decision making.

What Is Motion Forecasting?

• Scope: Includes Trajectory (where), Pose (body language), and Semantics (lane changes).
• Context: heavily relies on the static environment (HD Maps, road geometry).
• Uncertainty: A key requirement is outputting confidence intervals or multiple hypothesis modes.

Why It Matters

• Collision Avoidance: The primary safety layer for AV stacks (Waymo, Tesla FSD).
• Interactive Planning: "If I merge left, will the car behind me slow down?" (Game Theoretic planning).

Techniques

• VectorNet: Representing maps and agent paths as vectors.
• LaneGCN: Using Graph Convolutional Networks to model lane connectivity.
• Interaction Transformers: Attention over both time (history) and social space (other agents).

Motion Forecasting is predictive empathy for robots — anticipating what others will do so the robot can be a good citizen of the road.