187 citations · 1.3k across the 43 of their papers we have counts for
8 papers · 1 filter
Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications
Wenshuo Wang, Ding Zhao
Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and unde…
Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches
Wenshuo Wang, Junqiang Xi, Ding Zhao
Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framew…
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.…
Feature Analysis and Selection for Training an End-to-End Autonomous Vehicle Controller Using the Deep Learning Approach
Shun Yang, Wenshuo Wang, Chang Liu +2
Deep learning-based approaches have been widely used for training controllers for autonomous vehicles due to their powerful ability to approximate nonlinear functions or policies.…
Development and Evaluation of Two Learning-Based Personalized Driver Models for Car-Following Behaviors
Wenshuo Wang, Ding Zhao, Junqiang Xi +2
Personalized driver models play a key role in the development of advanced driver assistance systems and automated driving systems. Traditionally, physical-based driver models with…
Evaluation of A Semi-Autonomous Lane Departure Correction System Using Naturalistic Driving Data
Ding Zhao, Wenshuo Wang, David J. LeBlanc
Evaluating the effectiveness and benefits of driver assistance systems is essential for improving the system performance. In this paper, we propose an efficient evaluation method f…
Evaluation of Lane Departure Correction Systems Using a Stochastic Driver Model
Wenshuo Wang, Ding Zhao
Evaluating the effectiveness and benefits of driver assistance systems is crucial for improving the system performance. In this paper, we propose a novel framework for testing and…
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…