driving simulation and scenario factory for · 2019-03-07 · index 1. introduction of autonomous...
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Driving simulation and Scenario Factory for Automated Vehicle validationPr. Andras KemenyScientific Director, A. V. SimulationExpert Leader, Renault
INDEX
1. Introduction of autonomous driving
2. Validation of autonomous vehicle (AV)
3. Scenario factory for AV testing
4. Simulation, proving ground and field testing
5. Collaborative and public investments
SECTION 1
Introduction of autonomous driving on the automotive market
Motivations
Human vs. autonomous driving
How safe is safe enough
Level of introduction on the
market
Autonomous vehicle - definition
1. Unconnected or cooperative
2. Driverless or w/ driver
3. Self-driving or under control
4. Automated vs. human operation
Target of automated vehicles
1. Reduction of accidents
2. Reduction of gasoil consumption
3. Fluid traffic and Higher user rate of vehicles
4. Releasing of driver time and business opportunities
5. More space and less congestion in the cities in fine
6. New potential market opportunities
Motivations
1939 – General Motors “Futurama” exhibit
1964 – GM Firebird IV “Futurama II” exhibit
1964 – Research by Fenton at OSU
1986 – ROMETHEUS and PATH programs
1994 – PROMETHEUS demonstration in Paris
1997 - NAHS Consortium Demonstration in San Diego
2003 – PATH automated bus and truck demonstrations
2007 - DARPA Urban Challenge
Motivation (suite): History of Automated Driving
Curtesy: Steve Shladover, ITS, Berkeley
Human errors as a source of 90 % of automotive crashes
but
The safest drivers drive 10 x better than average (age, experience, fatigue, alcohol, external causes)
Human vs. automated driving fatalities
Perception while exposition to involuntary risk: bias of risk perception (ex. flying vs. driving)
Users requiring 1000 x smaller acceptable risk level
The media role in risk perception (ex. Hits in Tesla accident vs. ordinary road fatalities)
How safe is safe enough
Regulatory mandate
ex. Seat belt in the US – from 40 to 90 % of adoption
- New car fitting – 6 years
- Fitted all occupants – 22 years
- All occupants wearing – not yet
Perception of control
Technological maturity
with complex multiple systems with combined effects and robustness requirements
Level of introduction on the market
SECTION 2
Validation of autonomous vehicles
Acceptation and deployment of autonomous vehicles depends on
the extensive validation of user interface and safety level.
PICTURE
Critical ADAS and AD validation challenges
ADAS Features
AD Simulation (Km/21days)
AD Level
Storage & compute
NEW SKILLS
Model architectsData scientists
AI
PROCESS
New skills
Model architectsData scientists
AI
PROCESS
0,25 PB
1 TB/day
Transfer1400cores
50 PB
1,2 PB/day
Transfer79kcores
Based on virtual simulation and
physical validation
ECOSYSTEM& PARTNERSHIP
Suppliers +
Contributors
2016
30 +40
2018 2022
ON ON OFF OFF
3 M Km 500 M Km
From an assisted to an autonomous driving
5
Different levels of autonomous driving
Increase of the complexity of the vehicle
Front camera
Radar
Lidar
Around view camera
HD Map
Cut in scenario EXAMPLE
Following vehicle:• Speed = EGO
Speed
Preceding vehicle :• Various speed
conditions
EGO initial parameters:• Speed = preceding vehicle speed
Ground creation• Number of lanes• Width of the lanes• TiltWeather conditionsLuminosity:• Day time
Cut-in vehicle :• Speed > EGO and various
speed conditions
Trigger• Distance between
ego and cut-in vehicle according theirs speeds
SECTION 3
Scenario factory for AV testing
Massive scenario generation and corresponding data analysis are
necessary for thorough AV/AD testing and validation.
SCANeR Studio ©
AVS Validation processes
PICTURE
Scenario generation with the SCANeRStudio driving simulation software package.
PICTURE
Scenario replay with the SCANeRStudio driving simulation software package.
Post-processing data framework
Datalake
Simulation Plan Manager
Data Analyst
Data Scientist
Level 1• Main Insights of the SP• Dashboards
Level 2• Data mining on Reduced Information • Regular Data Mining
Level 3• Deep mining on Raw Data• Data Mining
Dev Team
Indicators Library
Warning Library
Dev Team
Add requested indicators to library
Add new warnings to library
Indicators
Warnings
SimulationsInputs
SECTION 4
Simulation, proving ground and field testing
The stake of AD/AV validation requires an extensive mixed,
simulation and physical testing procedure in order to cover all
known and rare critical road traffic scenarios.
Mixed, simulation and physical testing and validation procedures
Results analysis(Machine learning for Clusterisation and scenes recognition)
On Field & Tracks data collectionAccident DB / OEM DBDysfunction collection
Massive SIMULATION
Use Case catalog
Capitalization & car SW Upgrade
NG
NG
Massive Simulation platform ADIH (HPC (*))
Driving SimulatorDigital vehicle
+ Human model
+ Driver in the loop
Vehicle Models
Scenario Models (road, trafic, weather, …)
Scenario Factory
(*) HPC: High Performance ComputingNG : failed cases
1 2 3
Customer failed cases
A complete AD validation chain to address growing technology complexity & Uses Case diversity
AD DIL Dr iv ing s imulator requirements for AD occupant percept ion
Renault Optimized AD Simulateur (ROADS)- 9 DOF, 1G acceleration using 2 axes- 360° Screen, 3D & UHD
AD Functional safety, Driver acceptanceLimit conditions & dangerous Use Cases
Example : Animal crossing the lanes
Example : from other direction of the highway crashes into Ego Vehicle direction
New simulation building ~2 000 m² in 2019
Renault Investment
+
Example : Motorbike cuts in front Ego Vehicle (close or far cut-in)
PICTURE
High performance driving simulator for ADAS validation in various driving
simulation. Validation of delegation (from manual to autonomous driving and vice
and versa) as well as identification of rare worst case scenarios necessities the use
of driving simulators.
Scenario identification (MOOVE Project)
1. Data collect0. Use case Definition & Targeted scenarios
2. Data transformation at common format
3. Calculation of high level parameter (Sensors independent)
4. Scenario searching and clustering
Relative_Velocity_X
Relative_Velocity_Y
Absolute_Velocity_X
Absolute_Velocity_Y
Relative_Accel_longi
Accel_longi
…..
Time_To_Collision
Time_Between_Vehicles
Status_Mobile
Pos_X
Pos_Y
1. Real world driving safety critical scenarios (SCS)
2. SCS occurrence statistics
3. New SCS
Digital scenario library & test case generation(SVA Project)
1. Simulation platform
2. Digital Scenarios library
MooveDriving data recordings
Accidentology databases
Type approval
Library buildingScenarios & Environments• Description formats• Implementation
Network
Digital scenario library implementation
SVA PlatformProcess and tools validation by MIL
simulation
Partners
Driving data recordings
Retex
Cooperation with otherconsortiums (PEGASUS,SIP-ADUS, ENABLE…).
Driving simulation for autonomous vehicle validation
THANK YOU