Paper Reading

This post provides a technical deep dive into the Wayformer paper [1], a key publication in the field of motion forecasting. Training Overview An overview of the deep learning training pipeline, illustrating the data flow and key components involved during model training. Model Overview of the One-Stage E2E model One staged E2E model. Overview of the Two-Stage E2E model Two staged E2E model. Details of the Two-Stage E2E Model Overview of the Wayformer model. Model Structure Overview (a) (b) The left figure shows the encoder and decoder of the Wayformer model. The right figure shows the details of the encoder [1]. Feature Embedding/Feature Projection $$\mathbf{f}\in \mathbb{R}^{T \times N\times D} \to \mathbf{x}_{input} \in \mathbb{R}^{(T \cdot N) \times d}$$Where $T$ is the number of time history, $N$ is the number of entities, $D$ is the number of features, and $d=256$. ...

In this document, we provide detailed derivations complementing those presented in [2]. Details of the Derivation Discrete Model Based on the $\boxplus$ operation defined above, we can discretize the continuous model in (1) at the IMU sampling period $\Delta t$ using a zero-order holder. The resultant discrete model is $$ \mathbf{x}_{i+1} = \mathbf{x}_i \boxplus (\Delta t f(\mathbf{x}_i, \mathbf{u}_i, \mathbf{w}_i))$$where $i$ is the index of IMU measurements, and the function $f$, state $\mathbf{x}$, input $\mathbf{u}$, and noise $\mathbf{w}$ are defined below: ...