More Related Content Similar to ASSESSING ADAS/AD FOR SCENARIO-DRIVEN DESIGN VALIDATION AND OPTIMIZATION (20) 1. Assessing ADS real-life performance:
Uniting real data, AI and optimization
for scenario-driven design validation
and optimization
Alexandre Mugnai
Business Development Manager
4. © 2021 ESTECO SpA
ESTECO is an independent
software company, highly
specialized in numerical
optimization and simulation
process and data management.
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Our products
The leading software solution for
simulation process automation and
design optimization
The innovative enterprise platform for
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Accelerate the engineering process
and run multiple simulations.
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simulation across teams.
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Fast deliver the best product by applying
intelligent algorithms to the simulation
process.
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engineering solvers and IT resources.
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Leonardo
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Autonomous Vehicles: The verification challenge
”Autonomous vehicle shall safely manage every possible situation on the road”
Safety proof by driving Identify all possible situations
Test AV in these situations
1-10 billion km
Accidents may happen
Scenario types
Parameter ranges
Edge cases
Realistic interactions between actors
Performance evaluation
Assuring test coverage
Finding & solving performance gaps
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• Data, AI & Optimization to Aid Scenario-Based Validation
REAL
DATA
AI analysis
and
modelling
Test
Coverage
& Opt.
Evaluation
Test coverage
Find & solve issues
Scenario types
Parameter ranges
Edge cases
Realistic interactions
Understand complex scenarios
Test only relevant conditions
Sample complexity of real world
Data-driven scenario-based
development and validation
process
REAL
DATA
Data Collection
Test vehicles
High cost & time
Complex infrastructure
Driver bias
Customer vehicles
Low accuracy
Sensor bias
Huge investment
Complex infrastructure
Infrastructure sensors
Anywhere
Anytime
Cost-efficient
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Vehicle data
Sensor data
#
RAW DATA
STORAGE
- vehicles
- sensors, GPS
- annotations
Scenario category
definition
Scenario Mining
Run identification
and classification algorithms
SCENARIO DATABASE
incl. all characteristic
parameter distributions
Specify Operational
Design Domain
Statistical based
sampling
Simulations
Residual Risk
&
Uncertainty
Vehicle
Validation
Behavioural model
(Inv. Reinforc. Learning)
Critical scenarios
Algorithms
Sensors
Actuators
DB Completeness
Edge cases
Severity
Exposure
Optimisation
Propose a methodology for the assessment of ADAS/AD systems
based on scenario mining and scenario based testing (Streetwise – )
The offering
OEM / Tier
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• Scenario: ACC
• First impression
• Very basic, yet relatively complicated case when considering function parameters and various vehicle conditions (i.e. ego
vehicle & target vehicle)
• Simple enough to confirm method is functioning properly
• Partners
• TNO providing scenario database, measurables (i.e. severity – exposure – risk)
• ESTECO developing process automation and optimization tools (i.e. specialized algorithms)
• Scope: provide customers a methodology to optimize and validate a given function operating in a specific ODD
The Use Case: ACC
16. © 2021 ESTECO SpA
Lead vehicle decelerating scenario
The Use Case: ACC (Cont.)
ACC parameters
• k1: distance gain
• k2: speed gain ve,0
Scenario parameters
• ve,0
• vl,0
Identify the ACC parameters such that:
• The driving style is similar to human drive style
• The risk of a collision is minimized
Optimisation
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The Use Case: ACC (Cont.)
ACC parameter optimisation
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The Use Case: ACC (Cont.)
k1 = 1.0
k2 = 0.4
Pareto Front
ACC parameter optimisation
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The Use Case: ACC (Cont.)
Edge case identification
20. © 2021 ESTECO SpA
The Use Case: ACC (Cont.)
Edge case identification
Pareto Front
Safe and Probable
Scenario
Edge cases
Collisions
22. © 2021 ESTECO SpA
• By using cutting-edge AI to understand and model the real-world data, and by bringing these
AI models into simulation and optimization, the approach optimizes and validates the function
and its operational design domain (ODD)
• Gather real-life unbiased scenario data in relevant locations and time
• Produce a catalogue of parametrized scenarios with parameter distributions
• Identify most critical cases for the validation of a function
• Objectively quantify a system function and allow studies on:
• Sensor positions
• Sensor selection
• Algorithm parameter tuning for function optimization
Conclusion
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• The proposed approach has proven to work on an ACC study and can be extended to
various ADS functions that need to be developed, optimized and validated.
• The method can be applied for any domain in which vehicle requires best performance to
be identified in any real-life condition
• Automate process to simplify system assessment and optimization
• Algorithms play a significant role
• Building statistical understanding on the environment in which system needs to operate
• Identifying edge cases which challenge system performance
• Optimizing system performance
• Identify possible challenges ground truth system is facing
• Design a system that is not over spec-ed (i.e. excessive sensors) understanding its
strengths and weaknesses
• Provide safety argumentation, based on right real-life data
Take Away Ideas
