Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Driver fatigue detection based on saccadic eye movements
Driver Fatigue Detection Based on Eye Tracking
Real Time Eye State Monitoring System for Driver Drowsiness Detection
Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions
Yawning detection for monitoring driver fatigue based on two cameras
Measurement ofDriver's Consciousness byImageProcessing -AMethodforPresuming Driver's Drowsiness byEye-Blinks coping withIndividual Differences -
Driver Sleepiness Detection System Based on Eye Movements Variables
Drowsiness Detection Based on Eyelid Movement
Fully automated real time fatigue detection of drivers through Fuzzy Expert Systems
Fatigue Driving Detection System Design Based on Driving Behavior
Robust eyelid tracking for fatigue detection
Driving fatigue detection with fusion of EEG and forehead EOG
Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals
Yawning Detection for Monitoring Driver Fatigue
Intelligent driver drowsiness detection through fusion of yawning and eye closure
Real-time driver's eye state detection
Eye Blink Based Fatigue Detection for Prevention of Computer Vision Syndrome
Head movement-based driver drowsiness detection: A review of state-of-art techniques
Monitoring head/eye motion for driver alertness with one camera
Driver Fatigue Detection: A Survey
Efficient driver drowsiness detection at moderate levels of drowsiness.
Augmented Reality Interface for Teleoperation via Internet ) , one of the characteristics Design and Manipulation in Augmented Reality of Fanuc 200 iC Robot
CONDITIONAL RANDOM FIELDS FOR LIDAR POINT CLOUD CLASSIFICATION IN COMPLEX URBAN AREAS
Modeling drivers' visual attention allocation while interacting with in-vehicle technologies
A simple low voltage, high output impedance resistor based current mirror with extremely low input and output voltage requirements
Diagnosing and predicting wind turbine faults from SCADA data using support vector machines