Wenshuo Wang

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Uncovering Interpretable Internal States of Merging Tasks at Highway On-Ramps for Autonomous Driving Decision-Making

Humans make daily-routine decisions based on their internal states in in...
02/15/2021 ∙ by Huanjie Wang, et al. ∙ 0 ∙ share

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Fuzzing Based on Function Importance by Attributed Call Graph

Fuzzing has become one of the important methods for vulnerability detect...
10/07/2020 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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A Power Analysis of the Conditional Randomization Test and Knockoffs

In many scientific problems, researchers try to relate a response variab...
10/05/2020 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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On Social Interactions of Merging Behaviors at Highway On-Ramps in Congested Traffic

Merging at highway on-ramps while interacting with other human-driven ve...
08/14/2020 ∙ by Huanjie Wang, et al. ∙ 0 ∙ share

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Stratification and Optimal Resampling for Sequential Monte Carlo

Sequential Monte Carlo (SMC), also known as particle filters, has been w...
04/04/2020 ∙ by Yichao Li, et al. ∙ 0 ∙ share

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Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

Interpretation of common-yet-challenging interaction scenarios can benef...
03/02/2020 ∙ by Chengyuan Zhang, et al. ∙ 0 ∙ share

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Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile

Understanding multi-vehicle interactive behaviors with temporal sequenti...
10/28/2019 ∙ by Qin Lin, et al. ∙ 0 ∙ share

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Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process

Predicting surrounding vehicle behaviors are critical to autonomous vehi...
10/17/2019 ∙ by Jiacheng Zhu, et al. ∙ 0 ∙ share

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Recurrent Attentive Neural Process for Sequential Data

Neural processes (NPs) learn stochastic processes and predict the distri...
10/17/2019 ∙ by Shenghao Qin, et al. ∙ 0 ∙ share

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Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression

Generating multi-vehicle interaction scenarios can benefit motion planni...
10/08/2019 ∙ by Weiyang Zhang, et al. ∙ 0 ∙ share

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Active Learning for Risk-Sensitive Inverse Reinforcement Learning

One typical assumption in inverse reinforcement learning (IRL) is that h...
09/14/2019 ∙ by Rui Chen, et al. ∙ 0 ∙ share

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A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos

Semantic learning and understanding of multi-vehicle interaction pattern...
07/17/2019 ∙ by Chengyuan Zhang, et al. ∙ 9 ∙ share

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Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field

Autonomous vehicles (AV) are expected to navigate in complex traffic sce...
06/25/2019 ∙ by Yaohui Guo, et al. ∙ 0 ∙ share

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Metropolized Knockoff Sampling

Model-X knockoffs is a wrapper that transforms essentially any feature i...
03/01/2019 ∙ by Stephen Bates, et al. ∙ 0 ∙ share

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Multi-Vehicle Trajectories Generation for Vehicle-to-Vehicle Encounters

Generating multi-vehicle trajectories analogous to these in real world c...
09/15/2018 ∙ by Wenhao Ding, et al. ∙ 0 ∙ share

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Understanding V2V Driving Scenarios through Traffic Primitives

Semantically understanding complex drivers' encountering behavior, where...
07/27/2018 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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Clustering of Driving Scenarios Using Connected Vehicle Datasets

Driving encounter classification and analysis can benefit autonomous veh...
07/23/2018 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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An Optimal LiDAR Configuration Approach for Self-Driving Cars

LiDARs plays an important role in self-driving cars and its configuratio...
05/20/2018 ∙ by Shenyu Mou, et al. ∙ 0 ∙ share

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A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

A multitude of publicly-available driving datasets and data platforms ha...
05/13/2018 ∙ by Jiacheng Zhu, et al. ∙ 0 ∙ share

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Clustering of Naturalistic Driving Encounters Using Unsupervised Learning

Deep understanding of driving encounters could help self-driving cars ma...
02/28/2018 ∙ by Sisi Li, et al. ∙ 0 ∙ share

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Learning and Inferring a Driver's Braking Action in Car-Following Scenarios

Accurately predicting and inferring a driver's decision to brake is crit...
01/11/2018 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications

Developing an automated vehicle, that can handle the complicated driving...
09/11/2017 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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Feature Analysis and Selection for Training an End-to-End Autonomous Vehicle Controller Using the Deep Learning Approach

Deep learning-based approaches have been widely used for training contro...
03/28/2017 ∙ by Shun Yang, et al. ∙ 0 ∙ share

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Statistical Pattern Recognition for Driving Styles Based on Bayesian Probability and Kernel Density Estimation

Driving styles have a great influence on vehicle fuel economy, active sa...
06/03/2016 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machines

A rapid pattern-recognition approach to characterize driver's curve-nego...
05/22/2016 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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Wenshuo Wang

Featured Co-authors

Chang Liu

Yang Zhang

Ding Zhao

Rui Chen

Jun S. Liu

Chengyuan Zhang

Huei Peng

Lucas Janson

Wenhao Ding

Lijun Sun

Jiacheng Zhu

Uncovering Interpretable Internal States of Merging Tasks at Highway On-Ramps for Autonomous Driving Decision-Making

Fuzzing Based on Function Importance by Attributed Call Graph

A Power Analysis of the Conditional Randomization Test and Knockoffs

On Social Interactions of Merging Behaviors at Highway On-Ramps in Congested Traffic

Stratification and Optimal Resampling for Sequential Monte Carlo

Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile

Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process

Recurrent Attentive Neural Process for Sequential Data

Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression

Active Learning for Risk-Sensitive Inverse Reinforcement Learning

A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos

Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field

Metropolized Knockoff Sampling

Multi-Vehicle Trajectories Generation for Vehicle-to-Vehicle Encounters

Understanding V2V Driving Scenarios through Traffic Primitives

Clustering of Driving Scenarios Using Connected Vehicle Datasets

An Optimal LiDAR Configuration Approach for Self-Driving Cars

A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

Clustering of Naturalistic Driving Encounters Using Unsupervised Learning

Learning and Inferring a Driver's Braking Action in Car-Following Scenarios

Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications

Feature Analysis and Selection for Training an End-to-End Autonomous Vehicle Controller Using the Deep Learning Approach

Statistical Pattern Recognition for Driving Styles Based on Bayesian Probability and Kernel Density Estimation

A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machines