車車間遭遇を模擬する新しいマルチ車両軌道生成器
A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters
限られたデータからマルチ車両の相互作用シナリオを解釈可能な表現に符号化し、サンプリングで新しい走行遭遇シーンを生成するMTGを提案した論文。
著者: Wenhao Ding, Wenshuo Wang, Ding Zhao
分類: cs.CV, cs.RO
原文アブストラクト
Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing. This paper introduces a multi-vehicle trajectory generator (MTG) that can encode multi-vehicle interaction scenarios (called driving encounters) into an interpretable representation from which new driving encounter scenarios are generated by sampling. The MTG consists of a bi-directional encoder and a multi-branch decoder. A new disentanglement metric is then developed for model analyses and comparisons in terms of model robustness and the independence of the latent codes. Comparison of our proposed MTG with $\beta$-VAE and InfoGAN demonstrates that the MTG has stronger capability to purposely generate rational vehicle-to-vehicle encounters through operating the disentangled latent codes. Thus the MTG could provide more data for engineers and researchers to develop testing and evaluation scenarios for autonomous vehicles.
