187 citations · 1.3k across the 43 of their papers we have counts for
8 papers · 1 filter
Recurrent Attentive Neural Process for Sequential Data
Shenghao Qin, Jiacheng Zhu, Jimmy Qin +2
Neural processes (NPs) learn stochastic processes and predict the distribution of target output adaptively conditioned on a context set of observed input-output pairs. Furthermore,…
Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile
Qin Lin, Wenshuo Wang, Yihuan Zhang +1
Understanding multi-vehicle interactive behaviors with temporal sequential observations is crucial for autonomous vehicles to make appropriate decisions in an uncertain traffic env…
Active Learning for Risk-Sensitive Inverse Reinforcement Learning
Rui Chen, Wenshuo Wang, Zirui Zhao +1
One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assum…
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…
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…
How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior
Wenshuo Wang, Chang Liu, Ding Zhao
Big data has shown its uniquely powerful ability to reveal, model, and understand driver behaviors. The amount of data affects the experiment cost and conclusions in the analysis.…
A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model
Wenshuo Wang, Ding Zhao, Junqiang Xi +1
Misunderstanding of driver correction behaviors (DCB) is the primary reason for false warnings of lane-departure-prediction systems. We propose a learning-based approach to predict…