Wenshuo Wang
Wenshuo Wang
University of California at Berkeley, PostDoc Researcher
在 andrew.cmu.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing
M Zhou, H Jin, W Wang
Transportation Research Part D: Transport and Environment 49, 203–218, 2016
942016
Modeling and recognizing driver behavior based on driving data: A survey
W Wang, J Xi, H Chen
Mathematical Problems in Engineering 2014, 2014
932014
Driving Style Classification Using a Semisupervised Support Vector Machine
W Wang, J Xi, A Chong, L Li
IEEE Transactions on Human-Machine Systems, 2017
662017
How much data are enough? A statistical approach with case study on longitudinal driving behavior
W Wang, C Liu, D Zhao
IEEE Transactions on Intelligent Vehicles 2 (2), 85-98, 2017
482017
A learning-based approach for lane departure warning systems with a personalized driver model
W Wang, D Zhao, J Xi, W Han
IEEE Transactions on Vehicular Technology, DOI:10.1109/TVT.2018.2854406, 2017
402017
Learning and Inferring a Driver's Braking Action in Car-Following Scenarios
W Wang, J Xi, D Zhao
IEEE Transactions on Vehicular Technology 67 (5), 3887-3899, 2018
382018
Extracting traffic primitives directly from naturalistically logged data for self-driving applications
W Wang, D Zhao
IEEE Robotics and Automation Letters 3 (2), 1223-1229, 2018
362018
Driving style analysis using primitive driving patterns with bayesian nonparametric approaches
W Wang, J Xi, D Zhao
IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS …, 2017
362017
Human-Centered Feed-Forward Control of a Vehicle Steering System Based on a Driver's Path-Following Characteristics
W Wang, J Xi, L Chang, X Li
IEEE Transactions on Intelligent Transportation Systems 18 (6), 1440-1453, 2017
352017
A Rapid Pattern-Recognition Method for Driving Styles Using Clustering-Based Support Vector Machines
W Wang, J Xi
IEEE 2016 American Control Conference, pp. 5270-5275, 2016
352016
Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation
W Han, W Wang, X Li, J Xi
IET Intelligent Transportation Systems, DOI:10.1049/iet-its.2017.0379., 2018
26*2018
Feature analysis and selection for training an end-to-end autonomous vehicle controller using deep learning approach
S Yang, W Wang, C Liu, W Deng, JK Hedrick
IEEE 2017 Intelligent Vehicles Symposium (IV), 1033-1038, 2017
262017
Evaluation of lane departure correction systems using a regenerative stochastic driver model
W Wang, D Zhao
IEEE Transactions on Intelligent Vehicles 2 (3), 221-232, 2017
202017
Scene Understanding in Deep Learning-Based End-to-End Controllers for Autonomous Vehicles
S Yang, W Wang, C Liu, W Deng
IEEE Transactions on Systems, Man, and Cybernetics: Systems 49 (1), 53-63, 2018
192018
Estimating driver’s lane-change intent considering driving style and contextual traffic
X Li, W Wang, M Roetting
IEEE Transactions on Intelligent Transportation Systems 20 (9), 3258-3271, 2018
172018
Development and Evaluation of Two Learning-Based Personalized Driver Models for Car-Following Behaviors
W Wang, D Zhao, J Xi, DJ LeBlanc, JK Hedrick
IEEE American Control Conference, May 24–26, 2017, Seattle, USA, 1133-1138, 2017
152017
Driving Style-Oriented Adaptive Equivalent Consumption Minimization Strategies for HEVs
S Yang, W Wang, F Zhang, Y Hu, J Xi
IEEE Transactions on Vehicular Technology, DOI: 10.1109/TVT.2018.2855146, 2018
142018
Human-Centered Feed-forward Control of a Vehicle Steering System Based on a Driver’s Steering Model
W Wang, J Xi, J Wang
IEEE 2015 American Control Conference,July 1-3, Chicago, IL, USA, 3361-3366, 2015
122015
Learning V2V Interactive Driving Patterns at Signalized Intersections
W Zhang, W Wang
Transportation Research Part C: Emerging Technologies, DOI: 10.1016/j.trc …, 2019
102019
Understanding v2v driving scenarios through traffic primitives
W Wang, W Zhang, D Zhao
arXiv preprint arXiv:1807.10422, 2018
92018
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