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A machine learning study for predicting driver goals in contingencies with leading and lagging features during goal determination
Hsueh Yi Lai
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工業工程與管理學系
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引文 斯高帕斯（Scopus）
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Keyphrases
Machine Learning Experiments
100%
Defensive Driving
100%
Goal Determination
100%
Decision Support System
33%
Predictive Models
33%
Result Prediction
33%
Machine Learning Algorithms
33%
Machine Learning
33%
Decision Support
33%
Early Detection
33%
Reflex
33%
Driving Task
33%
Simulated Driving
33%
Cognitive State
33%
Random Forest
33%
Time Pressure
33%
Cognitive Outcome
33%
Drowsiness
33%
Pressure Condition
33%
Model Performance
33%
Data Learning
33%
Learned Features
33%
Fatigue Index
33%
Nave Bayes Classifier
33%
Extreme Gradient Boosting
33%
Early Prediction
33%
Immediate Reactions
33%
Psychological Data
33%
Reflex Control
33%
Scenario Assessment
33%
Active Choice
33%
Emerging Scenarios
33%
Computer Science
Decision-Making
100%
Related Feature
100%
Machine Learning
100%
Learning System
100%
Decision Support System
33%
Machine Learning Algorithm
33%
Predictive Model
33%
Performance Model
33%
Bayes Classifier
33%
Early Detection
33%
Random Decision Forest
33%
Extreme Gradient Boosting
33%
Nave Bayes
33%
Cognitive Outcome
33%
Engineering
Learning System
100%
Support System
50%
Early Detection
50%
Bayes Classifier
50%
Machine Learning Algorithm
50%
Random Forest
50%
Psychology
Decision Making
100%
Cognitive State
33%
Learning Algorithm
33%
Psychology
33%
Neuroscience
Decision-Making
100%
Cognitive State
33%
Machine Learning Algorithm
33%
Biochemistry, Genetics and Molecular Biology
Decision Making
100%
Random Forest
33%
Decision Support System
33%