10th AUTOSAR Open Conference

AUTOSAR Nov-2017

Virtualization Solutions for the AUTOSAR Classic and Adaptive Platforms

Pierre-Antoine BernardOpenSynergy GmbH

Cornel IzbasaOpenSynergy GmbH

AUTOSAR Nov-2017 2

OpenSynergy GmbH

• Headquarters in Berlin (Germany), further locations in Munich, San Diego and Salt Lake City

• Platform virtualization software (COQOS, COQOS micro) as part of the product portfolio

• Focus on cockpit controller, connectivity (smart antenna) and ADAS systems

Global provider of software and services for automotive embedded systems to Tier-1 suppliers and OEMs

Development Member

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– Hardware Consolidation– Software Consolidation– AI for Autonomous Driving and Virtualization

• Semantic Abstraction and Virtualization• Informational ADAS and Virtualization

– Hypervisor– Inter-VM Communication

Outline

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Hardware Consolidation

System-on-Chip (SoC)

Ethe

rnet

AVB

CAN

FD

Flex

ray

SPI

USB

Audi

o IF

Dis

play

IF

Video Decoder

…

GPU

DSPIPU

…

Cam

era

IF

Hardware ConsolidationSilicon manufacturers tend to intensify the integration of IP blocks inside SoCs:

• Clusters of powerful homogeneous or heterogeneous CPU cores• I/O interfaces (Ethernet AVB, CAN FD…)• Acceleration blocks (video decoder, DSP…)

CPU Cores CPU Cores

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Software Consolidation

System-on-Chip

Virtualization LayerIn

stru

men

t Clu

ster

SW

Hea

d U

nit S

W

ADAS

SW

Con

nect

ivity

SW

…

Benefits• Safety (real-time, freedom from interference)• Enhanced security• Use of automotive standards• Low impact on performance

Why virtualization?• Heterogeneous SW platforms onto a single SoC• Efficient use of highly integrated SoC• Extra SW layer between HW and OSs• Reduced attack surface (limited API)• Overall cost reduction• Modular software update

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Artificial Intelligence for Autonomous Driving

Symbolic• Putative characteristics

of cognition• Information processing

metaphor

Subsymbolic• Inspired by neurobiology• How cognition emerges

from neurobiology

Based on “The Mind-Body Problem” from [8], using [1] for IP reasons.

• Expert systems

• Artificial Neural Networks

• Deep Neural Networks (DNN)

I’m a CartesianDualist!

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Deep Learning Architectures for Autonomous Driving

• End-to-End

• Semantic Abstraction

Semantic Abstraction is superior to End-to-End architecture, for a given amount of training data. [4] demonstrates cases in which End-to-End requires exponentially greater number of examples.

InputSensorData

DNN

PD

LD

VDControlDNN /algo

OutputVehicleControl

OutputVehicleControl

InputSensorData

Diagrams based on [5]

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Semantic Abstraction enables running the different neural network and algorithmic components in different VMs, ensuring freedom from interference.

Semantic Abstraction enables Virtualization

System-on-Chip

Virtualization Layer

Inst

rum

ent C

lust

er S

W

Hea

d U

nit S

W

Con

nect

ivity

SW

…

ADAS/AD SW

VD 1 ControlLogic

Adaptive AR

Adaptive AR …

PD

Adaptive AR

LD

Adaptive AR

VD2

Adaptive AR

ASIL B ASIL D

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Informational ADAS, helpful for societal, individual and even legal acceptance of AD [7], is easier and more efficiently realized on a system that integrates ADAS, IC and IVI functionality in separate VMs with mixed criticality.

– Decision explanation challenging for Subsymbolic AI, but see [6] for related work.

Efficient Informational ADAS through ADAS/IC/IVI Consolidation

Examples of salient objects, inspired by [6],based on [3] for IP reasons.

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Hypervisor

VM

interrupt handling

memory management

memory management

Guest OS

I/O resource management exception

handling

Application

exception handling

INTC

Hypervisor

1st level MMU2nd level MMU vINTC

interrupt handling

Multi-Core HWHyper Privileged

Privileged

Non-Privileged

System Call

• Type-1 hypervisor• CPU abstraction (multi-core capable)• Containment and scheduling of VMs• Highest CPU operation mode• Non-monolithic architecture• Access control to I/O• Low footprint

HyperCall

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Inter-VM Communication

Hypervisor

VM VM

MonolithicKernel

Application

Driver

Kernel API

Inter-VM Communication

Application

Driver

A Hypervisor provides the low level communication mechanisms (multi-core capable) to set the foundation for high level communication frameworks (virtual Ethernet for example):• Shared memory• Hypercall

The communication properties may be similar to the AUTOSAR OS IOC (Inter-OS-Application Communicator):• Event vs Last-is-Best semantic• Triggering on data reception

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Virtual Ethernet

Hypervisor

VM

Inter-VM Communication

Virtual Ethernet Driver

Service VM

Virtual Ethernet Switch

VM

Kernel

Application

Virtual Ethernet Driver

Kernel

Application

Virtual Ethernet Switch

Virtual Ethernet enables the use (or reuse) of an IP communication stack. Applications are able to perform high level inter-VM communication (BSD socket as kernel interface).

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AdaptiveAUTOSAR

ClassicAUTOSAR

ClassicAUTOSAR

Use Case: Multi-AUTOSAR

Hypervisor

ClassicAUTOSAR

vECU

vECU

vECU

ServiceVM

• Time and space partitioning• Cluster of VMs to implement multiple vECUs• VM SW update granularity• Different SW suppliers• Easier development across multiple SW suppliers

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Classic AUTOSAR: Inter-VM Communication

Hypervisor

ClassicAUTOSAR

RTE

Inter-VM Communication

CDD

Inter-VM Communication

SWCSWC

DescriptionCDD

Description

ClassicAUTOSAR

RTE

Inter-VM Communication

CDD

SWC

ECU ConfigurationCDD Code

Gen

Metadata

Gen

RTE Code Gen

• Data proxying overhead (CDD)• Dedicated tooling required• Not standardized

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Classic AUTOSAR: Standardized Inter-VM Communication

Hypervisor

ClassicAUTOSAR

RTE

Inter-VM Communication

SWC

VirtCom

ClassicAUTOSAR

RTE

SWC

OS

IOC

SWC

Standardized Module (Proposal):• Signal Interface to BSW and RTE• “Last-is-Best” and “Event” semantics• Polling or (virtual) interrupt driven• Similar to OS IOC• May be an extension of the IOC sub-module instead of a separate module• Could pave the road for a distributed BSW across VMs

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Classic AUTOSAR

Classic AUTOSAR: Service-Oriented Inter-VM Communication

Hypervisor

Classic AUTOSAR

LdCom

EthIfEth (virtual)

PduR

SoAdTcpIp

BswMSD

(SOME/IP SD)

RTE

SWC

LdCom

VirtCom

PduR

BswMSD

(Inter-VM SD)

RTE

SWC

Inter-VM Communication

Possible Solution:• PDU interface towards PduR and SD• Inter-VM SD specific protocol• No overhead of TCP, IP and Ethernet (OSI Layer 2)

Adaptive AUTOSAR

Xf Xf

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Adaptive AR: Service-Oriented Inter-VM Communication

System-on-Chip

Virtualization Layer

ara::com

Adaptive Application …

Adaptive Application

ara::cominter-VM binding

ara::comInter-ECU binding

SOME/IP, DDS, etc.

Inter-VM Communication

Adap

tive

AR P

latfo

rm In

stan

ce

Adap

tive

AR P

latfo

rm In

stan

ce

Ethernet

Cla

ssic

AR

Pla

tform

Inst

ance

Adap

tive

AR P

latfo

rm In

stan

ce

Cla

ssic

AR

Pla

tform

Inst

ance

…

Con

solid

atio

n

ara::rest

Object Graph

HTTP/JSON

vETH vETH

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Standard interface for ara::com bindings on Adaptive AUTOSAR?

ara::com

Adaptive Application …

Adaptive Application

ara::com inter-VM binding

ara::com Inter-ECU binding(s)

SOME/IP, DDS, etc.

Adap

tive

AUTO

SAR

Pla

tform

Inst

ance

Standard interface? Standard interface? Standard interface?

ara::com local IPC binding

– Allows integration of binaries from Adaptive Application providers onto a project’s specific communication binding(s).

– Ease testing of application code using mock bindings.

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– OpenSynergy aims to bring the hypervisor concept into AUTOSAR through the creation of a CONC document and related artifacts such as:• Methodology and metamodel• Software modules and APIs

– OpenSynergy welcomes the cooperation with other AUTOSAR members on this topic.

Hypervisor Concept for AUTOSAR

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1. Brain image: https://commons.wikimedia.org/wiki/File:PSM_V46_D167_Outer_surface_of_the_human_brain.jpg.2. “Data is the New Oil in the Future of Automated Driving”, Brian Krzanich, Intel, https://newsroom.intel.com/editorials/krzanich-the-future-of-

automated-driving/.3. Road image: https://commons.wikimedia.org/wiki/File:California_I-280_Mill_Road.jpg.4. “On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training”, S. Shalev-Shwartz, A. Shashua, Mobileye,

https://arxiv.org/abs/1604.06915.5. “Autonomous Car - what goes into sensing for autonomous driving?”, presentation by A. Shashua, Mobileye, https://youtu.be/GCMXXXmxG-

I?t=271.6. “Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car”, M. Bojarski et al., NVIDIA Corporation,

https://arxiv.org/abs/1704.07911.7. “European Union regulations on algorithmic decision-making and a right to explanation”, B. Goodman, S. Flaxman,

https://arxiv.org/abs/1606.08813.8. “The Symbolic vs Subsymbolic Debate”, H. Bowman, CCNS, Kent, https://www.cs.kent.ac.uk/people/staff/hb5/SymbSubSymb.ppt.

References

10th AUTOSAR Open Conference

Thank you for your attention!

- AUTOSAR - page 21

OpenSynergy GmbHRotherstraße 2010245 Berlin / Germanyt +49 30 60 98 540 32pierre-antoine.bernard@opensynergy.com

Pierre-Antoine Bernard

OpenSynergy GmbHRotherstraße 2010245 Berlin / Germanyt +49 30 60 98 540 18cornel.izbasa@opensynergy.com

Cornel Izbasa