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EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome.
Efrat Levy, Ben Nassi, Raz Swissa,
Yuval Elovici
Department of Software and Information Systems Engineering
Research output
:
Working paper/Preprint
›
Preprint
Overview
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Dive into the research topics of 'EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome.'. Together they form a unique fingerprint.
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Keyphrases
Autonomous Cars
100%
3D Objects
100%
Autonomous Driving
100%
Stereoblindness
100%
Classification Accuracy
66%
2D Objects
66%
Pedestrian
33%
Life-threatening
33%
Detection Method
33%
Real-time Decision Making
33%
Video Recording
33%
Learning-based
33%
Real-time Performance
33%
Detection Error
33%
Object Detector
33%
YouTube Videos
33%
Driver's Seat
33%
Street View
33%
Few-shot Learning
33%
Dashcam
33%
Liveness Detection
33%
Engineering
Misclassification Rate
100%
Error Detection
50%
Road
50%
Video Recording
50%
Decision Time
50%
Social Sciences
Autonomous Driving
100%
Decision Making
33%
Video Recordings
33%
YouTube
33%
Pedestrian
33%
Psychology
Decision Making
100%
YouTube
100%
Medicine and Dentistry
Decision Making
100%
Neuroscience
Decision-Making
100%