動画から多車両の相互作用パターンを学習する汎用フレームワーク
A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos
俯瞰視点の交通動画から、ガウス速度場と深層自己符号化器、階層ディリクレ過程付き隠れセミマルコフモデルを用いて多車両の相互作用パターン（交通プリミティブ）を抽出する枠組みを提案した。
著者: Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang, Ding Zhao
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions. This paper presents a general framework to gain insights into intricate multi-vehicle interaction patterns from bird's-eye view traffic videos. We adopt a Gaussian velocity field to describe the time-varying multi-vehicle interaction behaviors and then use deep autoencoders to learn associated latent representations for each temporal frame. Then, we utilize a hidden semi-Markov model with a hierarchical Dirichlet process as a prior to segment these sequential representations into granular components, also called traffic primitives, corresponding to interaction patterns. Experimental results demonstrate that our proposed framework can extract traffic primitives from videos, thus providing a semantic way to analyze multi-vehicle interaction patterns, even for cluttered driving scenarios that are far messier than human beings can cope with.
