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