コネクテッド車両の軌跡を用いた走行遭遇シナリオのクラスタリング
Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories
複数車両のGPS軌跡のみから、深層オートエンコーダと距離ベース特徴表現を組み合わせた2層の教師なし学習で走行遭遇シナリオをクラスタリングする枠組みを提案し、2,568件の実走行データで有効性を示した。
著者: Wenshuo Wang, Aditya Ramesh, Ding Zhao
分類: cs.RO
原文アブストラクト
Multi-vehicle interaction behavior classification and analysis offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising two layers: feature representation layer and clustering layer. In the layer of feature representation, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space that allows quantifying the spatiotemporal interaction characteristics of two vehicles. The clustering algorithm is then applied to the extracted representations to gather homogeneous driving encounters into groups. Our proposed generic framework is then evaluated using 2,568 naturalistic driving encounters. Experimental results demonstrate that our proposed generic framework incorporated with unsupervised learning can cluster multi-trajectory data into distinct groups. These clustering results could benefit decision-making policy analysis and design for autonomous vehicles.
