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Lex Fridman
lex.mit.edu
January
2018
MIT 6.S094: Deep Learning for Self-Driving Cars
https://selfdrivingcars.mit.edu
For the full updated list of references visit:
https://selfdrivingcars.mit.edu/references
Lecture 5:
Deep Learning for Human Sensing
Lex Fridman
lex.mit.edu
January
2018
MIT 6.S094: Deep Learning for Self-Driving Cars
https://selfdrivingcars.mit.edu
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Lex Fridman
lex.mit.edu
January
2018
MIT 6.S094: Deep Learning for Self-Driving Cars
https://selfdrivingcars.mit.edu
Deep Learning for Human Sensing
• Requirements for success (from more to less critical)
• Data: A lot of real-world data (and algorithms that learn from data)
• Semi-supervised: Human annotations of representative subsets of data
• Efficient annotation: Specialized annotation tooling
• Hardware: Large-scale distributed compute and storage
• Robustness: Algorithms that don’t need calibration (learn the calibration)
• Temporal dynamics: Algorithms that consider time
• Current importance relation for successful application of deep learning:
>
Real-World Data
Good Algorithms*
* As long as they learn from data
5rjs.cn 专注无人驾驶
Lex Fridman
lex.mit.edu
January
2018
MIT 6.S094: Deep Learning for Self-Driving Cars
https://selfdrivingcars.mit.edu
For the full updated list of references visit:
https://selfdrivingcars.mit.edu/references
Overview
• Human Imperfections
• Pedestrian Detection
• Body Pose Estimation
• Glance Classification
• Emotion Recognition
• Cognitive Load Estimation
• Human-Centered Vision for Autonomous Vehicles
5rjs.cn 专注无人驾驶
Lex Fridman
lex.mit.edu
January
2018
MIT 6.S094: Deep Learning for Self-Driving Cars
https://selfdrivingcars.mit.edu
For the full updated list of references visit:
https://selfdrivingcars.mit.edu/references
Humans Are Amazing
• 3.22 trillion miles (US, 2016)
• 40,200 fatalities (US, 2016)
• 1 fatality per 80 million miles
• 1 in 625 chance of dying in car crash
(in your lifetime)
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