軌道予測のための条件付きアンセンテッドオートエンコーダ
Conditional Unscented Autoencoders for Trajectory Prediction
自動運転の軌道予測において、CVAEのサンプリング手法をアンセンテッドサンプリングに置き換え、ガウス混合潜在空間や新しい推論法を導入することで、INTERACTIONデータセットで最先端性能を達成した。
著者: Faris Janjoš, Marcel Hallgarten, Anthony Knittel, Maxim Dolgov, Andreas Zell, J. Marius Zöllner
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
The CVAE is one of the most widely-used models in trajectory prediction for AD. It captures the interplay between a driving context and its ground-truth future into a probabilistic latent space and uses it to produce predictions. In this paper, we challenge key components of the CVAE. We leverage recent advances in the space of the VAE, the foundation of the CVAE, which show that a simple change in the sampling procedure can greatly benefit performance. We find that unscented sampling, which draws samples from any learned distribution in a deterministic manner, can naturally be better suited to trajectory prediction than potentially dangerous random sampling. We go further and offer additional improvements including a more structured Gaussian mixture latent space, as well as a novel, potentially more expressive way to do inference with CVAEs. We show wide applicability of our models by evaluating them on the INTERACTION prediction dataset, outperforming the state of the art, as well as at the task of image modeling on the CelebA dataset, outperforming the baseline vanilla CVAE. Code is available at https://github.com/boschresearch/cuae-prediction.
