model-based development for vehicular embedded systems

Model-based Development for Vehicular Embedded SystemsAlessio Bucaioni

13-10-2016

STEW 2016

Arcticus Systems

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OUTLINE

• MESS RESEARCH GROUP• BACKGROUND• PROBLEM FORMULATION• PROPOSED SOLUTION• UNIQUENESS• RUNNIN EXAMPLE• ACCADEMIA-INDUSTRY TRANSFER

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MODEL-BASED ENGINEERING OF EMBEDDED SYSTEMS RESEARCH GROUP

16 research projects

15 members

Born in 2011 as a spin-off from the ”Real-Time System Design” group

2 main research areas

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15

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2011 2012 2013 2014 2015 2016

Nu

mbe

r of

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Years

Conference Paper

Doctoral Thesis

Licentiate Thesis

Book Chapter

Journal Article


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Arcticus Systems


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BACKGROUND – VEHICULAR EMBEDDED SYSTEMS

PARENTALCONTROL

WINDSHIELDWIPER

CONTROL

ENGINECONTROL

AIRBAGDEPLOYMENT

ADAPTIVE FRONTLIGHTING

ADAPTIVE CRUISECONTROL

AUTOMATICBRAKING

ELECTRIC POWER STEERING

ELECTRONICTHROTTLECONTROL

ELECTRONIC VALVE TIMING

IDLE STOP/START

CYLINDERDE-ACTIVATION

ACTIVEVIBRATIONCONTROL

OBDII

REMOTE KEYLESSENTRY

BLINDSPOTDETECTION

LANEDEPARTUREWARNING

TRANSMISSION CONTROL

SEAT POSITIONCONTROL

ACTIVE YAWCONTROL

PARKINGSYSTEM

ELECTRONICSTABILITY

CONTROL

ANTILOCKBREAKING

TIRE PRESSUREMONITORING

NIGHTVISION

HEAD-UPDISPLAY

DRIVER ALERTNESSMONITORING

INSTRUMENTCLUSTER

ACCIDENTRECORDER

EVENT DATARECORDER

AUTO-DIMMINGMIRROR

INTERIORLIGHTING

ACTIVE CABIN NOISESUPPRESSION

VOICE/DATACOMMUNICATION

CABIN ENVIRONMENTCONTROLS

DSRC

ENTERTAINMENT SYSTEMS

BATTERY MANAGEMENT

LANE CORRECTION

ELECTRONICTOLL CORRECTION

DIGITAL TURNSIGNALS

NAVIGATION SYSTEM

SECURITY SYSTEM

ACTIVE EXHAUSTNOISE SUPPRESION

RIGENERATIVEBREAKING

ACTIVE SUSPENSION

HILL HOLDCONTROL

Courtesy of www.volvo.com

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BACKGROUND - VEHICULAR EMBEDDED SYSTEMS

“More than 80 percent ofvehicle innovation comes from embedded systems”

- MANFRED BROYProfessor of informatics at Technical University, Munich

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100000000

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Late 1970s Nowadays

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Size of vehicular embedded software


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Courtesy of www.bmw.com

4,5 times more expensive

Multi-core platforms

25% longer schedules

3 times as many software engineers

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* S. Balacco, C.Rommel. Next Generation Embedded Hardware Architectures:Driving Onset of ProjectDelays, Costs Overruns and Software Development Challenges. Klockwork Inc. 2010.

BACKGROUND - VEHICULAR EMBEDDED SYSTEMS ON MULTICORE

BACKGROUND - MODEL-DRIVEN ENGINEERING

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- BRAN SELICFather of Real-Time UML

“As our systems grow in complexity traditional code-

centric development methods are becoming intractable”

BACKGROUND - MODEL-DRIVEN ENGINEERING

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Abstraction Automation

+ =

Model-driven Engineering

BACKGROUND – EAST-ADL

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Vehicle Level

Analysis Level

Design Level

Implementation Level

Activities Abstraction levels Format

Capture requirements onE2E vehicle functionality

Consistency analysis of requirements. Functional verification

Prototyping, system properties,timing and resource analysis.

Complete SW architecture

Modelling of features.SW architecture, HW architecture, SW

to HW allocation,

Often informal. Textual. Solution-independent

Formal, model-based.Allocation independent

Formal, model-based.Implementation-independent.

Formal, model-based. Implementation details.

BACKGROUND – EAST-ADL

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PROBLEM FPRMULATION

- PONTUS DE LAVALCTO at Saab AB

“It is so much cheaper to find defects at design time”

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PROPOSED SOLUTION - MY RESEARCH IN A NUTSHELL

Model-based software development

methodology which supports early timing analysis for vehicular embedded systems.

Design Level


Timing analysis

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PROPOSED SOLUTION -METHODOLOGY

Analysis results

M2M transformation

Timing analysis & filter

Analysis results

M2M in-place transformation

Des

ign

lev

elIm

ple

men

tati

on

lev

el

EAST-ADLdesign model

u-Rubusmodel

u-Rubus modelwith

analysis results

Negativefeedback

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UNIQUENESSES – WHAT DO YOU GAIN ?

• Reduce accidental complexity• Early timing verification• Support uncertainty• Support for multi-core

RUNNING EXAMPLE: INTELLIGENT PARKING ASSIST

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Proximity_Sensor_DFP Input_Process_DFP Path_Calculator_DFP CAN_Send_DFP CAN_Receive_DFP Control_DFP Brake_Actuator_DFP

IPAssistant_DFP Actuator_DFP

15 ms

20 ms

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(1)

(2)

(3)

(4)

Software Circuit Clock

Connector data

Connector trigger

Data ports

Trigger ports

Timing constraintsTiming constraints


Reduce complexitySupport uncertainty

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Timing analysis has filtered the solution space.However there are still 14 RCM models to inspect.

(1)

(2)

(3)

(4)

Software Circuit Clock

Connector data

Connector trigger

Data ports

Trigger ports

Timing constraintsTiming constraints


Early timing verification

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Support uncertainty


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METHODOLOGY FOR MULTICORESTART

Functional ModelRubusMM_SW

Platform ModelRubusMM_HW

M2M TransformationJTL

Execution Modelsμ-RubusMM_SW + Timing

Model-based TimingAnalysis

Modify theAllocation Models

Modify theFunctional Model

Code Generation

END

Are the TimingRequirements Met?

Is It a Single-corePlatform?

Are all the AllocationsModel checked?

YES

NO

NO

YES

YES

NO

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MEES CONTRIBUTIONS

Vehicle Level

Analysis Level

Design Level


Abstraction levels Contribution of the MEES research groupfinished contribution ongoing contribution

Rubus Component Model (RCM)RCM metamodel definition (RubusMM)Exact RTARTA for CAN and high level protocol, e.g., HCAN, CANopenE2E response timeE2E delays, e.g., age and delayShared stack analysisSwitched ethernetSWEET benchmarkExtensions for multi-core platforms

RubusMM extensions for multi-core platformsModel-based methodology for early predictabilityPredictability enabled on design assumptions

Predictability enabled for legacy nodes

Ru

busE

AST

and

tran

slat

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of

TAD

L2 c

onst

rain

ts

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ACADEMIA-INDUSTRY TRANSFERMDH

BASEMENT

SaveComp

ProSave

EMDEF

FEMMVA

SynthSoft

RCM 1&2

RCM 3

RCM 4Extension of timing analysis, modelling

support

Multicore

Arcticus

19941996

20022005

2005

20122009

20122014

20142018

RCM 4 +

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ACADEMIA-INDUSTRY TRANSFER

Arcticus Systems

Requirements,Existing tools,Certified RTOS

Methods, Technbiques,Prototypes

Thank you for the attention!Questions?