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Smart Mobility, Empowering Cities
www.itsworldcongress2019.com | #ITSWC19
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Jacobo Antona-Makoshi (PhD)
Japan Automobile Research Institute
A Safety Assurance Process for AD Systems
N UchidaS KitajimaH NakamuraH Imanaga
S TaniguchiK OzawaE Kitahara
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Aim of this presentation
To report on the ongoing development of an AD system safety assurance
methodology in Japan
(SAE Level 3+ in motorways)
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Global trend for AV social acceptance
Safety requirements (pg.4)
When in the automated driving mode, the vehicle shall not cause any traffic accidents that are rationally foreseeable and preventable
Safety vision (pg.1)
Automated vehicles shall not cause any non-tolerable risk, meaning that, under their operational domain, shall not cause any traffic accidents resulting in injury or death that are reasonably foreseeable and preventable
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Comprehensive approach to safety
Fo
reseeab
leU
nfo
reseeab
le
Preventable Unpreventable
Prevention
Resilience Support
Mitigation
Learning
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Safety assurance engineering approach
Search Based Test
Testscenarioapproach
Proving ground tests Virtual testsSafety
evaluationReal-traffic tests
Process
Data Driven Approach
Trajectory
Distribution(Statistics)
Process
Trafficdata
Parameter Search Engine
Process
Functional
scenario
Logical
scenario
Concrete
scenario
Convert
Convert
Parameterrange
Socially acceptable top safety goals defined by authorities
Safety Criteria
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Scenario based approach based on AD system physics
Scenario Structure
Traffic DisturbancePerception disturbance Vehicle Disturbance
Judgement ControlPerception
Traffic disturbance functional scenarios out from a proposed
structure that incorporates road geometry, ego-vehicle behavior,
and surrounding vehicles location and motion
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Traffic data collection in Japan
Data
Source
TUAT Driving
Recorder
(~2018~)
JAMA Driving
Recorder
(2008)
Driving Database
(2017)
On road
Recognition
Database (2017)
Instrumented
Vehicles (2018~)
Fixed Camera
(2018~)
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
Ego
EgoEgo
Ego
Ego
Ego
Ego
Ego
Ego
Parameter available
Video only
visible
Not recorded
/
Ongoing
Camera LiDAR
Fixed camerasInstrumented vehicles
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① ②
③ ④
Data acquisition with instrumented vehicles & automatic detection of scenarios
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Data acquisition with fixed cameras & automatic detection of scenarios
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Derivation process of functional scenarios parameter spaces
Measured data
Fitted distribution
③ Parameter extrapolation
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6Lateral cut-in speed (m/s)
① Real-world data accumulation ② Parameter correlation analysis
④ Parameter spaces
Account for:
• Real-world data
• Parameter correlation
• Theoretically foreseeable
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Process automation from functional to concrete scenarios
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Summary
• Complete AD safety assurance methodology under development in Japan
• Applicable to continue developing the systems and to evaluate their safety
Need for international consensus:
1) Definitions of 'foreseeable' and 'preventable' in the context of social acceptance
2) Qualification of real-world traffic data
3) Applicability to different countries/regions
4) Harmonized automated processes towards common database
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