104 citations · 229 across the 15 of their papers we have counts for
4 papers · 2 filters
Understanding V2V Driving Scenarios through Traffic Primitives
Wenshuo Wang, Weiyang Zhang, Ding Zhao
Semantically understanding complex drivers' encountering behavior, wherein two or multiple vehicles are spatially close to each other, does potentially benefit autonomous car's dec…
Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning
Sisi Li, Wenshuo Wang, Zhaobin Mo +1
Learning knowledge from driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with nearby vehicles engaged. This paper develop…
Extraction of V2V Encountering Scenarios from Naturalistic Driving Database
Zhaobin Mo, Sisi Li, Diange Yang +1
It is necessary to thoroughly evaluate the effectiveness and safety of Connected Vehicles (CVs) algorithm before their release and deployment. Current evaluation approach mainly re…
Learning and Inferring a Driver's Braking Action in Car-Following Scenarios
Wenshuo Wang, Junqiang Xi, Ding Zhao
Accurately predicting and inferring a driver's decision to brake is critical for designing warning systems and avoiding collisions. In this paper we focus on predicting a driver's…