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Shi Xiupeng
Shi Xiupeng
Alibaba | Nanyang Technological University | Agency for Science, Technology and Research (A*STAR)
在 ntu.edu.sg 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
A feature learning approach based on XGBoost for driving assessment and risk prediction
X Shi, YD Wong, MZF Li, C Palanisamy, C Chai
Accident Analysis & Prevention 129, 170-179, 2019
2152019
Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data
T Chen, X Shi, YD Wong
Accident Analysis & Prevention 129, 156-169, 2019
1152019
Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory
X Shi, YD Wong, MZF Li, C Chai
Accident Analysis & Prevention 117, 346-356, 2018
902018
Fuzzy logic-based observation and evaluation of pedestrians’ behavioral patterns by age and gender
C Chai, X Shi, YD Wong, MJ Er, ETM Gwee
Transportation research part F: traffic psychology and behaviour 40, 104-118, 2016
80*2016
An automated machine learning (AutoML) method of risk prediction for decision-making of autonomous vehicles
X Shi, YD Wong, C Chai, MZF Li
IEEE Transactions on Intelligent Transportation Systems 22 (11), 7145-7154, 2020
672020
Predicting lane-changing risk level based on vehicles’ space-series features: A pre-emptive learning approach
T Chen, X Shi, YD Wong, X Yu
Transportation research part C: emerging technologies 116, 102646, 2020
482020
A machine learning based study on pedestrian movement dynamics under emergency evacuation
K Wang, X Shi, APX Goh, S Qian
Fire safety journal 106, 163-176, 2019
482019
Fuzzy cellular automata model for signalized intersections
C Chai, YD Wong
ComputerAided Civil and Infrastructure Engineering 30 (12), 951-964, 2015
472015
A lane-changing risk profile analysis method based on time-series clustering
T Chen, X Shi, YD Wong
Physica A: Statistical Mechanics and its Applications, 2020
392020
A xgboost-based lane change prediction on time series data using feature engineering for autopilot vehicles
Y Zhang, X Shi, S Zhang, A Abraham
IEEE Transactions on Intelligent Transportation Systems 23 (10), 19187-19200, 2022
382022
A data-driven feature learning approach based on Copula-Bayesian Network and its application in comparative investigation on risky lane-changing and car-following maneuvers
T Chen, YD Wong, X Shi, Y Yang
Accident Analysis & Prevention 154, 106061, 2021
262021
A study of short term forecasting of the railway freight volume in China using ARIMA and Holt-Winters models
Y Guo, X Shi, X Zhang
2010 8th International Conference on Supply Chain Management and Information …, 2010
262010
Optimized structure learning of Bayesian Network for investigating causation of vehicles’ on-road crashes
T Chen, YD Wong, X Shi, X Wang
Reliability Engineering & System Safety 224, 108527, 2022
162022
Automatic clustering for unsupervised risk diagnosis of vehicle driving for smart road
X Shi, YD Wong, C Chai, MZF Li, T Chen, Z Zeng
IEEE Transactions on Intelligent Transportation Systems 23 (10), 17451-17465, 2022
112022
A novel cooperative resource provisioning strategy for multi-cloud load balancing
B Zhang, Z Zeng, X Shi, J Yang, B Veeravalli, K Li
Journal of Parallel and Distributed Computing 152, 98-107, 2021
112021
An automated machine learning (AutoML) method for driving distraction detection based on lane-keeping performance
C Chai, J Lu, X Jiang, X Shi, Z Zeng
arXiv preprint arXiv:2103.08311, 2021
102021
Accident risk prediction based on driving behavior feature learning using CART and XGBoost
X Shi, YD Wong, MZF Li, C Chai
Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018
82018
Method study on forecast for body comfort index
XP Shi, CN Wang, XR Chen, WQ Zhai, XQ Cheng
Scientia Meteorologica Sinica 21 (3), 363-368, 2001
72001
Effects of trust in human-automation shared control: A human-in-the-loop driving simulation study
W Yin, C Chai, Z Zhou, C Li, Y Lu, X Shi
2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021
42021
Developing and evaluating an human-automation shared control takeover strategy based on Human-in-the-loop driving simulation
Z Zhou, C Chai, W Yin, X Shi
arXiv preprint arXiv:2103.06700, 2021
32021
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