フォロー
Wenshuo Wang
Wenshuo Wang
Professor, Beijing Institute of Technology (BIT) | Research Fellow, UC Berkeley, CMU, McGill
確認したメール アドレス: bit.edu.cn - ホームページ
タイトル
引用先
引用先
年
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
3642016
Driving Style Classification Using a Semisupervised Support Vector Machine
W Wang, J Xi, A Chong, L Li
IEEE Transactions on Human-Machine Systems 47 (5), 650-660, 2017
2762017
Modeling and recognizing driver behavior based on driving data: A survey
W Wang, J Xi, H Chen
Mathematical Problems in Engineering 2014 (1), 245641, 2014
1882014
Driving style analysis using primitive driving patterns with bayesian nonparametric approaches
W Wang, J Xi, D Zhao
IEEE Transactions on Intelligent Transportation Systems 20 (8), 2986 - 2998, 2019
1772019
Decision-making in driver-automation shared control: A review and perspectives
W Wang, X Na, D Cao, J Gong, J Xi, Y Xing, FY Wang
IEEE/CAA Journal of Automatica Sinica 7 (5), 1289-1307, 2020
1652020
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 67 (10), 9145 - 9157, 2018
1542018
Social Interactions for Autonomous Driving: A Review and Perspectives
W Wang, L Wang, C Zhang, C Liu, L Sun
Foundations and Trends in Robotics 10 (3-4), 198-376, 2022
1452022
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
1182018
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, 2019
1122019
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
1102017
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, 2019
1072019
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
972017
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
942018
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
932016
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 13 (1), 22-30, 2019
88*2019
Computer vision for road imaging and pothole detection: a state-of-the-art review of systems and algorithms
N Ma, J Fan, W Wang, J Wu, Y Jiang, L Xie, R Fan
Transportation safety and Environment 4 (4), tdac026, 2022
772022
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 67 (10), 9249--9261, 2018
702018
TriPField: A 3D potential field model and its applications to local path planning of autonomous vehicles
Y Ji, L Ni, C Zhao, C Lei, Y Du, W Wang
IEEE Transactions on Intelligent Transportation Systems 24 (3), 3541-3554, 2023
652023
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
582017
Clustering Driving Encounter Scenarios Using Connected Vehicle Trajectories
W Wang, A Ramesh, J Zhu, J Li, D Zhao
IEEE Transactions on Intelligent Vehicles, 2020
542020
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