Learning-Deep-Learning

TNT: Target-driveN Trajectory Prediction

February 2022

tl;dr: Predict diverse set of future targets and then use target to drive trajectory prediction.

Overall impression

The paper described the core drawbacks of previous methods, involving sampling latent states (VAE, GAN), or fixed anchors (coverNet, MultiPath).

TNT has the following advantages

• supervised training
• deterministic inference
• interpretable
• adaptive anchors
• likelihood estimation

The target, or final state capture most uncertainty of a trajectory. TNT decompose the distribution of futures by conditioning on targets, and then marginalizing over them.

The anchor-based method is improved by DenseTNT to be anchor-free, which also eliminated the NMS process by learning.

Key ideas

• Step 1: target prediction, based on manually chosen anchors
• Step 2: Motion estimation, conditioned on targets
• Step 3: Trajectory scoring/selection, with scoring and NMS

Technical details

• Vectorized (sparse) encoding with VectorNet.

Notes

• CoRL talk on Youtube