Fast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction

Understand

Safety-critical applications such as autonomous vehicles and social robots require fast computation and accurate probability density estimation on trajectory prediction.

• To address both requirements, this paper presents a new normalizing flow-based trajectory prediction model named FlowChain.
• FlowChain is a stack of conditional continuously-indexed flows (CIFs) that are expressive and allow analytical probability density computation.
• This analytical computation is faster than the generative models that need additional approximations such as kernel density estimation.