autonomous driving: the good the bad and the ugly · 2019-03-29 · autonomous driving: the good...
TRANSCRIPT
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Who’s TuSimple?
Autonomous Driving: The Good The Bad and The Ugly
Xiaodi Hou
TuSimple
What’s level 4Why trucks?Camera or LiDAR?When do you launch the product?
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Building an autonomous truck
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4 pillars of autonomous driving
Algorithms
Infrastructure
Process Product
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Algorithms
Algorithms
Infrastructure
Process Product
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Algorithms
• “Typical” challenges
• Detection, tracking, localization, pose estimation, planning, control…
• More for trucks!
• Wider, and longer (430% of a Camry), slow accelerate/decelerate
• Fuel matters
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Perception for trucks
Absence of “superior” controllability
Long horizon motion planning
Long term behavior prediction of others
Long range perception
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A superior pilot uses his superior judgement to
avoid situations which require the use of his
superior skill.
-- Frank Borman
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The more boundless your vision, the
more real you are.
--Deepak Chopra
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650m away
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From algorithm to product
• Effective algorithms always have an impact on products
• Why most academic papers are not applicable
• False positive/false negative cost
• Indirect implications/narrow application
• Computational/implementation cost
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Infrastructure and process
Algorithms
Infrastructure
Process Product
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What do we need
• Infrastructure
• Big data, deep learning
• Simulation, real-time systems
• Process
• Data annotation, vehicle testing
• Continuous integration, benchmarking
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What do we REALLY need
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On big data
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Trucks can generate big data cheaply
• Mileage accumulation:
• 45 miles/hr * 20hr/day * 25day/mo = 22,500 miles/mo
• Cost-per-mile
• $1.8/mile operating cost - $1.6/mile revenue = $0.2/mile
• Sampling density
• Fixed routes (1D structure)
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On data digestion
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“Divide and conquer”?
• Software engineering methodology
• Easy to scale-up the dev team
• 3x resources = 3 problems to be solved (patched) simultaneously
• Steady progress
• Regression test
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“Divide and conquer” won’t work
• AI systems are not typical software systems
• Every node contributes to the noise, without making an error
• Team division precludes architectural evolution
• How many cases must a system fix, before you call it level-4?
• Every “fix” is a technical debt, making future fixes harder
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TuSimple’s design philosophies
• AI engineering is missing
coding : software engineering
algorithm : AI engineering
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TuSimple’s design philosophies
• All about generalization
• Each corner case is a reminder
• Regression tests
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The evolution of autonomous driving systems
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The Kardashev scale
Type I civilization: 1016 W Type II civilization: 1026 W Type III civilization: 1036 W
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Comparable infrastructure & process
Single vehicle 5 vehicle fleet 50 vehicle fleet
Raw data transfer Flash diskCommand-line +
networkingFully automated
pipeline
Algorithm deployment In vehicle deploy/debugManual deployment of
packagesFully automated
pipeline
Road testing Superior driverSuperior driver +
protocolProtocol-based test +
conservative AI
Data digestion Naked eye HashtagStatistical learning
based development
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No matter how much funding, or how many algorithm
geniuses you have, you can’t build a level 4 product with shaky
infrastructure/process.
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The missing evaluation metrics
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How about Miles-Per-Intervention (MPI)
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Interpretations
How far are we to achieve driverless automation?
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Two types of the fleet
• Validation
• Stable release of hardware + software
• Sufficient coverage of the operational design domain
• Significant sampling density
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Two types of the fleet
• Development
• Expected to fail
• Rapid iterations
• Specific domains and scenarios to check
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Understanding interventions
• Inefficient maneuvers: benign
• waiting too long, detour, slowing down, stopped at the roadside
• Traffic rule violations: costly
• Stopped in the lane, failed to yield
• Accidents: critical
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We need a better MPI metric
• Why do we care?
• Regulators, insurance companies, investors, and AI companies
Critical intervention (Cal. DMV)
Costly intervention Benign intervention
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Thanks