187 citations · 1.3k across the 43 of their papers we have counts for
8 papers · 1 filter
A Time Efficient Approach for Decision-Making Style Recognition in Lane-Change Behavior
Sen Yang, Wenshuo Wang, Chao Lu +2
Fast recognizing driver's decision-making style of changing lanes plays a pivotal role in safety-oriented and personalized vehicle control system design. This paper presents a time…
A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters
Wenhao Ding, Wenshuo Wang, Ding Zhao
Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing. This paper introduces a multi-vehicle tr…
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…
Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories
Wenshuo Wang, Aditya Ramesh, Ding Zhao
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 ran…
An Optimal LiDAR Configuration Approach for Self-Driving Cars
Shenyu Mou, Yan Chang, Wenshuo Wang +1
LiDARs plays an important role in self-driving cars and its configuration such as the location placement for each LiDAR can influence object detection performance. This paper aims…
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives
Jiacheng Zhu, Wenshuo Wang, Ding Zhao
A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV). However, the heterogeneities of databases in size, structure an…
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…
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…