SAFETY : the biggest challenge for autonomous vehicle SIMULATION - - PowerPoint PPT Presentation

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SAFETY : the biggest challenge for autonomous vehicle SIMULATION - - PowerPoint PPT Presentation

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SAFETY : the biggest challenge for autonomous vehicle SIMULATION will complete actual data. Rmi BASTIEN VP prospective B RMI BASTIEN 2018, JUNE 1 Confidential C AUTONOMOUS VEHICLES , WHAT STAKES? The he ke key is in n affordable

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2018, JUNE

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RÉMI BASTIEN

Confidential C

SAFETY : the biggest challenge for autonomous vehicle SIMULATION will complete actual data. Rémi BASTIEN VP prospective

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2018, JUNE

B

RÉMI BASTIEN

Confidential C

The he ke key is in n affordable transp sport modes for

  • r us

users, and nd with th hi high throughputs to

  • sta

tay economical in n m² of

  • f infr

nfrastructure

AUTONOMOUS VEHICLES , WHAT STAKES?

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MORE AND MORE, INFRASTRUCTURE BECOMES A KEY ENABLER

Michigan initiative CAVnue

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CLASSIFICATION BY LEVEL (SAE) IS UNCOMPLETE…

Driver continuously performs the longitudinal and lateral dynamic driving task Driver continuously performs the longitudinal or lateral dynamic driving task Driver must monitor the dynamic driving task and the driving environment at all times No intervening vehicle system active The other driving task is performed by the system System performs longitudinal and lateral driving task in a defined use case

System performs longitudinal and lateral driving task in a defined use case. Recognizes its performance limits and requests driver to resume the dynamic driving task with sufficient time margin.

Automation ➔ Driver Level 0 Level 1 Level 2 Level 3 Level 4 Level 5

Driver Only Assisted Partial Automation Conditional Automation High Automation Full Automation

Driver is not required during defined use case System performs the lateral and longitudinal dynamic driving task in all situations in a defined use case. System performs the lateral and longitudinal dynamic driving task in all situations encountered during the entire

  • journey. No

driver required.

Driver does not need to monitor the dynamic driving task nor the driving environment at all times; however he must be attentive to and follow system’s requests / warnings to resume the dynamic driving task.

*terms acc. to SAE J3016

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… AS ODD* STATUS ARE NECESSARY : EXAMPLE OF L4

visibility = 𝑔

𝐺𝑃𝑊 𝑊 Arterial / sub-arterial driveways Dedicated bus lanes Non highway intercity driveways Intercity highways (A6, A13) Chaotic traffic (Mumbai, place de l’Etoile, etc.)

Traffic density = 𝑔 𝛼 Ԧ 𝑟

Narrow FOV Limited FOV Open FOV

No life zone Low Average High

Mars surface Warehouses War zones

← City slow ← City fast ← Expressways

Dedicated highway lanes Residential pathways Complex urban traffic (Paris intramuros, Rome, Madrid, etc.) Dense intra-urban arteries (Paris, Rome, Madrid, etc.)

*ODD : Operational design domain

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TECHNOLOGY IS MANDATORY BUT … IS NOT ENOUGH

▪ Platform enhancement ▪ Core technologies

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KEY SUCCESS CONDITIONS : SOCIAL ACCEPTANCE

Social acceptance Experimentation ▪ Proof by FOT on certified roads ▪ Regulations ▪ Product Liability ▪ Infrastructure ▪ Insurance ▪ Consumer awareness ▪ Driver Education

➔ THE BIGGEST STAKE IS SAFETY

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AD IS FAR BEYOND ADAS, WITH AN UNCERTAIN MARKET

Only active under driver’s request Active only in limited conditions and vehicle proposal ABS ESP AEB Continuously active Without driver intervention

EMERGENCY / SAFETY ASSISTANCE

Park assist ACC LKA

STRESS FREE / FREE TIME : AUTONOMOUS DRIVING

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ADAS

(L1, L2, L3)

AD

(L3+(1), L4, L5) Driver is the last resort System is the last resort Driver reliability proof System reliability proof

Driver training + experience Massive mile accumulation + resimulation

AD IS A MAJOR DISRUPTION

(1) Emerging German L3 standard (Audi, BMW, Daimler) (2) Emerging consensus among European OEM

SAFETY IS A MUST

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ISO 26262 defines how to assess a risk and the necessary activities to perform for each step:

❖ System

❖ Software ❖ Hardware ❖ Production... Redundancy for Autonomous Driving: ❖ Redundant Sensors & Actuators ❖ Redundant Communication Networks ❖ Redundant Power supply Networks

SAFETY NEEDS STANDARDS FOR DEVELOPMENT AND VALIDATION

▪ Additional Safety Stakes: ❖ For Autonomous Driving, Automotive EE

Architecture has to switch from Fail Safe design to Fail Operational.

❖ Safety has also to consider SOTIF (Safety of

the Intended Functionality)

A

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ISO 26262 Standard is necessary but not sufficient !

Does an ultrasound sensor can detect a child with a wool sweater? Does a radar will be accurate on a metallic bridge ? Does a camera can identify a target in a very large roundabout without lane ?

E

SOTIF IS MANDATORY AND COMPLETE ISO 26262

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evolutive proven

FIRST CONDITION : E/E ARCHITECTURE ABLE TO ENSURE ASILD

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SECOND CONDITION : VALIDATION STRATEGY TO PROVE ASIL D

I Statistical safety threshold II Reduction : Experience plan + Simulation III Road sections criticity inductors IV Map of road sections with criticity V Clustered road tests VI Final proof of reliability

Reliable and efficient validation <<< 20 Billions km

Order of magnitude for validation : 20 Billions of kms Non affordable by physical test drive

Numerical simulation Targetted, iterative physical test drive For each road section, calculate the « criticity cube » : Nb incoming lanes x Nb exits x Strong Curvature …

Each road section is ranked by its criticity ratio = criticity cube volume / average criticity Distribution of clusters is proportional to the criticity ratio of the road sections

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EXAMPLE OF RISKY SITUATIONS ON HIGHWAY

1 3 2 4

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COMBINATION OF THE 4 SITUATIONS : VERY UNLIKELY… BUT POSSIBLE

ONLY SIMULATION CAN COVER SUCH CASES WHEN MILEAGE ACCUMULATION CANNOT

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KEY TAKE AWAY

  • Safety is a must and is extremely demanding. This will lead to

first applications on limited Operational Design Domain.

  • ADAS potential market will stay strong before AD applications
  • Simulation is essential for the development and validation
  • Validation time and cost will be reduced by a factor > 1000 with

the support of advanced simulation

  • To model precisely the performances of the sensors
  • To combine compatible scenarios to cover exhaustive risky situations
  • To allow safer&quicker time to market applications for ADAS and AD

Autonomous Driving Autonomous Driving and Simulation AD Next challenges for simulation