b'Wenshuo Wang'

research

∙ 09/07/2023

Total Variation Floodgate for Variable Importance Inference in Classification

Inferring variable importance is the key problem of many scientific stud...

0 Wenshuo Wang, et al. ∙

research

∙ 07/30/2023

Interactive Car-Following: Matters but NOT Always

Following a leading vehicle is a daily but challenging task because it r...

0 Chengyuan Zhang, et al. ∙

research

∙ 07/07/2023

Calibrating Car-Following Models via Bayesian Dynamic Regression

Car-following behavior modeling is critical for understanding traffic fl...

0 Chengyuan Zhang, et al. ∙

research

∙ 07/05/2023

Understanding Resolution of Multi-Language Bugs: An Empirical Study on Apache Projects

Background: In modern software systems, more and more systems are writte...

0 Zengyang Li, et al. ∙

research

∙ 10/15/2022

On Trustworthy Decision-Making Process of Human Drivers from the View of Perceptual Uncertainty Reduction

Humans are experts in making decisions for challenging driving tasks wit...

0 Huanjie Wang, et al. ∙

research

∙ 09/24/2022

Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills

Efficient and effective exploration in continuous space is a central pro...

0 Tong Zhou, et al. ∙

research

∙ 08/16/2022

Social Interactions for Autonomous Driving: A Review and Perspective

No human drives a car in a vacuum; she/he must negotiate with other road...

0 Wenshuo Wang, et al. ∙

research

∙ 06/07/2022

Learning Symbolic Operators: A Neurosymbolic Solution for Autonomous Disassembly of Electric Vehicle Battery

The booming of electric vehicles demands efficient battery disassembly f...

0 Yidong Du, et al. ∙

research

∙ 04/28/2022

Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms

Computer vision algorithms have been prevalently utilized for 3-D road i...

0 Nachuan Ma, et al. ∙

research

∙ 07/17/2021

Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix

The advance in machine learning (ML)-driven natural language process (NL...

14 Wenshuo Wang, et al. ∙

research

∙ 02/15/2021

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...

0 Huanjie Wang, et al. ∙

research

∙ 10/07/2020

Fuzzing Based on Function Importance by Attributed Call Graph

Fuzzing has become one of the important methods for vulnerability detect...

0 Wenshuo Wang, et al. ∙

research

∙ 10/05/2020

A Power Analysis of the Conditional Randomization Test and Knockoffs

In many scientific problems, researchers try to relate a response variab...

0 Wenshuo Wang, et al. ∙

research

∙ 08/14/2020

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...

0 Huanjie Wang, et al. ∙

research

∙ 04/04/2020

Stratification and Optimal Resampling for Sequential Monte Carlo

Sequential Monte Carlo (SMC), also known as particle filters, has been w...

0 Yichao Li, et al. ∙

research

∙ 03/02/2020

Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

Interpretation of common-yet-challenging interaction scenarios can benef...

0 Chengyuan Zhang, et al. ∙

research

∙ 10/28/2019

Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile

Understanding multi-vehicle interactive behaviors with temporal sequenti...

0 Qin Lin, et al. ∙

research

∙ 10/17/2019

Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process

Predicting surrounding vehicle behaviors are critical to autonomous vehi...

0 Jiacheng Zhu, et al. ∙

research

∙ 10/17/2019

Recurrent Attentive Neural Process for Sequential Data

Neural processes (NPs) learn stochastic processes and predict the distri...

0 Shenghao Qin, et al. ∙

research

∙ 10/08/2019

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

Generating multi-vehicle interaction scenarios can benefit motion planni...

0 Weiyang Zhang, et al. ∙

research

∙ 09/14/2019

Active Learning for Risk-Sensitive Inverse Reinforcement Learning

One typical assumption in inverse reinforcement learning (IRL) is that h...

0 Rui Chen, et al. ∙

research

∙ 07/17/2019

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

Semantic learning and understanding of multi-vehicle interaction pattern...

9 Chengyuan Zhang, et al. ∙

research

∙ 06/25/2019

Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field

Autonomous vehicles (AV) are expected to navigate in complex traffic sce...

0 Yaohui Guo, et al. ∙

research

∙ 03/01/2019

Metropolized Knockoff Sampling

Model-X knockoffs is a wrapper that transforms essentially any feature i...

0 Stephen Bates, et al. ∙

research

∙ 09/15/2018

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

Generating multi-vehicle trajectories analogous to these in real world c...

0 Wenhao Ding, et al. ∙

research

∙ 07/27/2018

Understanding V2V Driving Scenarios through Traffic Primitives

Semantically understanding complex drivers' encountering behavior, where...

0 Wenshuo Wang, et al. ∙

research

∙ 07/23/2018

Clustering of Driving Scenarios Using Connected Vehicle Datasets

Driving encounter classification and analysis can benefit autonomous veh...

0 Wenshuo Wang, et al. ∙

research

∙ 05/20/2018

An Optimal LiDAR Configuration Approach for Self-Driving Cars

LiDARs plays an important role in self-driving cars and its configuratio...

0 Shenyu Mou, et al. ∙

research

∙ 05/13/2018

A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

A multitude of publicly-available driving datasets and data platforms ha...

0 Jiacheng Zhu, et al. ∙

research

∙ 02/28/2018

Clustering of Naturalistic Driving Encounters Using Unsupervised Learning

Deep understanding of driving encounters could help self-driving cars ma...

0 Sisi Li, et al. ∙

research

∙ 01/11/2018

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

Accurately predicting and inferring a driver's decision to brake is crit...

0 Wenshuo Wang, et al. ∙

research

∙ 09/11/2017

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

Developing an automated vehicle, that can handle the complicated driving...

0 Wenshuo Wang, et al. ∙

research

∙ 03/28/2017

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...

0 Shun Yang, et al. ∙

research

∙ 06/03/2016

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...

0 Wenshuo Wang, et al. ∙

research

∙ 05/22/2016

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...

0 Wenshuo Wang, et al. ∙

Wenshuo Wang

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