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Control as a Service (CaaS) Cloud-based Software Architecture for Automotive Control Applications

Hasan Esen*, Hideaki Tanaka+, Akihito Iwai#

DENSO (*Europe, +Japan, #Silicon Valley) Masakazu Adachi TOYOTA CENTRAL R&D LABS., INC. Jens Knodel, Dominik Rost, Christian Peper Fraunhofer IESE, Germany Alberto Bemporad, Daniele Bernardini ODYS Srl, Italy 13.04.2015, SEC Workshop, Seattle, US

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2 CONTENTS

I. Automotive Trends

II. CaaS: Moving the Controllers to the Cloud?

III. Case Study

IV. Conclusion

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3 CONTENTS

I. Automotive Trends

II. CaaS: Moving the Controllers to the Cloud?

III. Case Study

IV. Conclusion

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4 1. Automotive Trends

Stand Alone Control

Integrated Vehicle System (IVS)

Vehicle System Trend

IVS + Infrastructure

Networked Control

~60’s 90’s 2010’s 2030’s

Increasing • number of Electronic Control Units (ECUs) • ECU program size • SW complexity

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5

Diversified Mobility

Propulsion Technology in 2030

1. Automotive Trends

(1)

Adapted ICE

Full Electric (inc. Fuel Cel)

Plug-in hybrid range ext.

Spark ignition hybrid

Advanced spark ignition

Advanced flexible combustion techniques

Diesel hybrid

Advanced Diesel

0%

100%

Smart Grid in 2030

Mega Competition

Resource: (1)ERTRAC/EUCAR)

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6 1. Automotive Trends

Automated Driving (AD) and Communication Technologies in 2030

System of Systems Increased Complexity & Scale: Safety, Security, Reliability Problems

AD

AD

Remote Control Center

V2I

V2V

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7 CONTENTS

I. Automotive Trends

II. CaaS: Moving the Controllers to the Cloud?

III. Case Study

IV. Conclusion

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8 CaaS: Moving the Controllers to the Cloud?

data domain

Brake

Steering

Cruise

Pre-crash

control domain Engine Transmission

Cyber world

Physical world

Sensor/Actuator

TODAY… data domain is moving to the cyber space

Telematics

Microcontroller

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9 What can be more?

data domain

Safety

Cyber world

Physical world

POTENTIAL FUTURE… controllers will also move to the cyber space?

Telematics

Sensor/Actuator

Microcontroller

Steering Brake

Motor

Cruise

Pre-crash Engine

control domain

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10 Potential Future: Moving the Controllers to the Cloud

Idea: „Control as a Service“ (CaaS)

Steering Brake

Motor

Cruise

Pre-crash Engine

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11 Selected Scenarios

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12 Concept Platform and Software Architecture

Partner: Dr. Jens Knodel

Development of a concept prototype

Architecture-Centric Engineering Solutions (ACES)

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13 First Tests

Setting Throttle Control Variants (Virt. ECU)

Round-Trip Times

Cloud Server Location: Kaiserslautern, Germany

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14 Challenges

Too Many!

SOFTWARE

COMMUNICATION

CONTROL

hard real-time

control-aware (wireless)

communication

Security, safety, privacy

… Efficient computing

Real-time cloud technology

Reliable/fault-tolerant SW-

architecture

… Sensor fusion

Communication-aware control

Role distribution: Cloud

vECUs/In-vehicle ECUs

…

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15 CONTENTS

I. Automotive Trends

II. CaaS: Moving the Controllers to the Cloud?

III. Case Study

IV. Conclusion

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16 4. Case Study

CONTROL Sensor fusion

Communication-aware control

Role distribution: Cloud vECUs/In-

vehicle ECUs

…

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17 Case Study – Scenario

Assumptions:

The vehicle has CAN-Bus and a WLAN gateway for cloud communication.

Obstacles are detected by a distance sensor

Objective: Achieve torque control from cloud coupled with active steering for obstacle avoidance taking WLAN latencies into account

Proposed Solution Methodology: Stochastic Model Predictive Control

If possible, brake and adjust to leader velocity... ... otherwise, steer.

Partner: Prof. Alberto Bemporad

Adaptive Cruise Control with Active Steering in a Cloud-Controlled Vehicle

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18

I. Nominal Deterministic MPC

Standard MPC; no delay compensation

Case Study – Model Predictive Control (MPC) Design

S

i

iitot JpJ1

Find optimal torque rate (DT) minimizing the cost J s.t. C1

subject to (s.t.) constraints C1

pi: probability of scenario i

Ji: cost function for scenario i

S: # of scenarios (2)

subject to constraints, where i = 1,2

Find optimal torque rate (DT) minimizing the cost Jtot s.t. Ci

II. Network-aware Stochastic MPC

Markov transitions + delay compensation

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19

Obstacle detection sensor range: 30m in front of the car

Time-varying reference velocity: 30km/h to 50km/h

Two obstacles: the first can be avoided by braking, the second requires steering

1 delay step in “idle”, 3 delay steps in “busy”

Probability to switch Markov state is 10% (both idle to busy and busy to idle)

Case Study – Results

time

Reference velocity

30 km/h

50 km/h

1st Obstacle

2nd Obstacle

Simulation conditions

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20 Case Study – Results

Performance assessment

I. Nominal Deterministic MPC

Standard MPC; no delay compensation

II. Network-aware Stochastic MPC

Markov transitions + delay compensation

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21 CONTENTS

I. Automotive Trends

II. CaaS: Moving the Controllers to the Cloud?

III. Case Study

IV. Conclusion

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22

Emerging mobility trends

remote traffic observation and vehicle control

diverse inter & intra vehicle communication networks

automated driving

platooning

vehicle to grid integration

powertrain electrification

alternative fuels

…

Automotive systems: more complex, more connected, larger in scale

Safety, security, reliability and privacy problems: very challenging to solve

Interdisciplinary, advanced solutions; new methodologies are required

Conclusion

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23 Conclusion

On-going work related to “CaaS” Platform

SOFTWARE

COMMUNICATION

CONTROL

Cooperative CaaS Architecture

Security threat analysis

Optimal use of communication medium

Event-Based Control (EBC)