Wenshuo Wang

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Wenshuo Wang

Featured Co-authors

Chang Liu

Ding Zhao

Rui Chen

Chengyuan Zhang

Jun S. Liu

Huei Peng

Wenhao Ding

Mansur Arief

Lucas Janson

Jiacheng Zhu

Emmanuel Candes

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

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

Clustering of Naturalistic Driving Encounters Using Unsupervised Learning

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

A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

An Optimal LiDAR Configuration Approach for Self-Driving Cars

Understanding V2V Driving Scenarios through Traffic Primitives

Clustering of Driving Scenarios Using Connected Vehicle Datasets

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

Metropolized Knockoff Sampling

Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field

Active Learning for Risk-Sensitive Inverse Reinforcement Learning

Recurrent Attentive Neural Process for Sequential Data

Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile

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

Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process

Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

Stratification and Optimal Resampling for Sequential Monte Carlo