FUTURE MOBILITY WITH AUTONOMOUS AND CONNECTED …italy.vehiclemeetings.com/images/presentations/Stefan Pischinger presentation FEV.pdfSAE Automation Level Something Somewhere Sometimes

© by FEV – all rights reserved. Confidential – no passing on to third parties

Plenary Speech

Prof. Dr.-Ing. S. Pischinger

FUTURE MOBILITY

WITH AUTONOMOUS AND CONNECTED VEHICLES

Torino (Italy), November 27-28, 2018

© by FEV – all rights reserved. Confidential – no passing on to third parties |

Agenda

Introduction

Market Forecast

Customer Benefits

Core Technologies

Challenge Cyber Security

Summary & Outlook

2Prof. Dr.-Ing. S. Pischinger | VTM Torino | November 2018


Autonomous and Connected Vehicles are Expected to Strongly Impact the

Automotive Industry as well as Public Life

3

Key Sectors

Individual Transportation

Public Transportation

Car-Sharing / E-Hailing

Commercial Vehicles

Emergency Vehicles

Farming

When will it happen?

Prof. Dr.-Ing. S. Pischinger | VTM Torino | November 2018


Agenda

Introduction

Market Forecast

Customer Benefits

Core Technologies


Summary & Outlook



Automation of Vehicles Has Already Been Started and Will Be Extended

Gradually According to SAE Automation Levels

5

0

Driver

Automation1 2 3 4 5

No

AutomationDriver

Assistance

Partial

Automation

Conditional

Automation

High

Automation

Full

Automation

SAE Automation Level

Something

Somewhere

Sometimes

Assist

only

Everything

Somewhere

Sometimes

Everything

Everywhere

Every time

Sales Forecast


86%

61%

38%

14%

25%

31%

14%31%

0%

20%

40%

60%

80%

100%

20352030

Sh

are

of sa

les o

f n

ew

ve

hic

les

2025

Source: FEV

High level automation (level 3+) is projected to push into market starting from 2025

onwards

In 2035, the sales shares of new vehicles with an automation level lower or equal to 2,

vehicles with automation level 3 as well as level 4 will be almost evenly distributed

Re

sp

on

sib

ility


New Mobility Concepts Will Arise from Current Technological as wells as

Social Trends and Will Push Highly Automated Vehicles into the Market

6

CURRENT TRENDS & NEW MOBILITY CONCEPTS

Trends

Technology

Society Motivation

Technology Society

Automation

Electrification

Connectivity

Demography

Sharing Economy

Urbanisation






Technology

Society

Car-Sharing

E-HailingMotivation

Car-Sharing E-Hailing*

7

Mobility ConceptsTrends


* E-Hailing: Ordering peer-to-peer ridesharing services via mobile apps





Technology

Society

Car-Sharing

E-HailingMotivation

8

Mobility ConceptsTrends

Consequences

Consequences

Changed

buyer/user relation

Changed customer requirements

High influence on technology

configuration of future vehicles



Highly Automated Vehicles Will Reach a Significant Share of New Mobility

Concepts Starting in 2030

9

Source: FEV


72%Share of e-hailing vehicles with

automation level 4 in 2030

2%

2025

6%

Sh

are

of sa

les o

f n

ew

ve

hic

les

26,6

85%

2%

96%

4%

3%

56,8

94%

2030

10%

2035

43,0

11%

21,2

4%

2%

87%

2025

87%

3%

93%

2030 2035

9%18,04%

9,5

2025 2030

62%

38%

60%

40%

2035

9,7

21,9

Market share of vehicles with automation level 4 in private car purchases

amount to about around 24 % in 2035

n.a.

Private Vehicle Car-Sharing E-Hailing

Automation Level 0-2 Automation Level 3 Automation Level 4


Agenda

Introduction

Market Forecast

Customer Benefits

Core Technologies


Summary & Outlook



Customers Will Benefit from More Comfort, Increased Safety and a Better

System Efficiency

11

Safety≥ Level 3

Traffic Jam Pilot

High Way Pilot

Use of Multimedia

Services

≥ Level 4

Self Parking

Vehicle as Office /

Living Room

< Level 3

Driver Warnings

Emergency Brake

Assistant

Active Distance &

Speed Control

Lane Keeping

≥ Level 2 / 3

Smart Powertrain Control /

Predictive Energy Management

≥ Level 4

Platooning

Traffic Flow Optimization

Comfort Safety

Economy

Autonomous

&

Connected

Vehicles



Information by Vehicle-to-X Communication Allows the Development of

New Smart Powertrain Functionalities with Big Saving Potentials

12

Prediction

Predictive Engine Control


Predictive Hybrid Strategy

Predictive Speed Control

Vehicle-to-

Infrastructure

Cloud Services

30

Vehicle-to-Vehicle

V2X

Plug-In HEV 11%*

Conv. Vehicle 3%*

15%*

Conv. Vehicle 23%*

Plug-In HEV 28%*

BEV 24%*

Surrounding Traffic

Short Horizon

Mid / Long Horizon

Ego Vehicle

*FEV simulation results of real life cycles assuming a perfect prediction


Agenda

Introduction

Market Forecast

Customer Benefits

Core Technologies


Summary & Outlook



From Sensing to Autonomous Driving Functionality:

Data Handling Is One of the Key Competences of Autonomous Driving


Data Handling

Autonomous

Driving

Onboard

Sensors

(Lidar etc.)

HD

MapsV2X

Comm.

Data

Collection

Data

Aggregation

Data

Interpretation

Data

Prediction

Decision

& Action


Turning the Real World into Virtuality Using HD Lidar Mapping


Source: FEV





Data Handling

Autonomous

Driving

Onboard

Sensors

(Lidar etc.)

HD

MapsV2X

Comm.

Data

Fusion

Object

Detection

Data

Collection

Data

Aggregation

Data

Interpretation

Data

Prediction

Decision

& Action

# FEV EXAMPLE


Object Detection ”Tagging” Algorithms Based on Artificial Intelligence






Data Handling

Autonomous

Driving

Onboard

Sensors

(Lidar etc.)

HD

MapsV2X

Comm.

Data

Fusion

Object

Detection

Dynamic

Situation

Analysis

Data

Collection

Data

Aggregation

Data

Interpretation

Data

Prediction

Decision

& Action

Artificial

Intelligence

# FEV EXAMPLE

# FEV EXAMPLE


Autonomous Driving with Dynamic Situation Analysis Based on Artificial

Intelligence

19

FEV DEVELOPMENT PLATFORM AND TECHNOLOGY DEMONSTRATOR





Data Handling

Autonomous

Driving

Onboard

Sensors

(Lidar etc.)

HD

MapsV2X

Comm.

Data

Fusion

Object

Detection

Dynamic

Situation

Analysis

Artificial

Intelligence

Prediction

Models

Data

Collection

Data

Aggregation

Data

Interpretation

Data

Prediction

Decision

& Action

Artificial

Intelligence

# FEV EXAMPLE # FEV EXAMPLE

# FEV EXAMPLE


Predicting Preceding Vehicles Based on Actual Sensor and V2X

Information Using a Driver Model

21

A COMPARISON OF SPEED PREDICTION OUTPUT TO VEHICLE MEASUREMENTS


Source: FEV

Driver model based algorithm predicts speed and acceleration profile of preceding vehicle

Predicting the behavior of surrounding traffic is one key feature for autonomous & predictive driving





Data Handling

Autonomous

Driving

Onboard

Sensors

(Lidar etc.)

HD

MapsV2X

Comm.

Data

Fusion

Object

Detection

Dynamic

Situation

Analysis

Artificial

Intelligence

Prediction

Models

Route

Planing

Moral

Machine

Trajectory

calculation

Data

Collection

Data

Aggregation

Data

Interpretation

Data

Prediction

Decision

& Action

Artificial

Intelligence

Artificial

Intelligence

# FEV EXAMPLE # FEV EXAMPLE

# FEV EXAMPLE

# FEV EXAMPLE


OTOMOD – An Autonomous Electric Campus Shuttle

L4 Use Case: Connected – Automated – Shared - Electric

23

Supported by:

Seat Reservation

and payment by

App

Autonomous

drive without

driver

Closed Campus

area

4 seater

Semi open body

Automatic doors

Interaction screen

Fully electric

drivetrain2

3

1

4

5

Source: Kodeco, FEV


The FEV Smart Mobility R&D Landscape:

Different Use Cases, different missions, different partner

24

MISSION: Extended EV

Range (L2, Predictive

Cruise Control)

MISSION:

Autonomous

Campus Shuttle

(L4)

MISSION: Individual

Transport Features

(L3/4)

MISSION:

Shared Mobility

(L3/4)

SMART MOBILITYINDIVIDUAL MOBILITY


CERMcity Facilitates Research and Development on Autonomous and

Connected Driving on the Ground of the Aldenhoven Testing Center

Prof. Dr.-Ing. S. Pischinger | VTM Torino | November 2018 25

https://www.atc-aldenhoven.de/de/

Grand Opening on October 1st, 2018


KoMo:D - First Test Field in Germany for Testing Automated and

Connected Driving Under Real Traffic Conditions


https://www.komod-testfeld.org/index.html

Test Environment

Complex main routes connecting the city center of Düsseldorf

with the adjacent motorway network

Targets:

Testing of data provision and data communication based on

existing V2X standards

Consideration of the functional safety of cooperative systems

Analysis of the effects of automated driving on safety,

environment (noise and air pollutants) and efficiency as well

as social aspects

Test track in Düsseldorf, Germany


To Overcome the Variety of Test Cases for Autonomous Driving, Virtual

Testing Is Indispensable


http://www.hy-nets.de/en/

Traffic Flow SimulationDetailed Physical SimulationElectric Motor Test Bench

Source: Tectos

Coupling of component test benches, a detailed vehicle simulation and a traffic flow simulation including V2X communication results in an unique testing environment for automated vehicles

OUTCOME FROM RESEARCH PROJECT ‘HY-NETS’ CARRIED OUT BY FEV’S COOPERATION PARTNER VKA

Supported and founded by

dSPACE Automotive Simulation Models Veins


Agenda

Introduction

Market Forecast

Customer Benefits

Core Technologies


Summary & Outlook



Why We Need to Talk about Cybersecurity:

A Brief Introduction into the Differences between Safety and Security

29

HOW SECURITY COMPARES TO FUNCTIONAL SAFETY AND WHY IT IS VASTLY DIFFERENT

http://www.supercoloring.com/fr/silhouettes/famille-de-5-personnes

Protection of humans from machines

Countermeasures against expected malfunction

Your product fails to work in the way it was

designed

Protection of machines from humans

Countermeasures against unexpected misuse

Your product will work in a way it was never

designed

Security: What is it?Safety: What is it?

SA

FE

TY

SE

CU

RIT

Y


Electronic components in a vehicle had been an isolated network for decades

Now, multiple interfaces, mostly wireless, managing dozens of connections are introduced within few

development cycles


Hacking Is No New Phenomenon:

Track Record of Successful Security Attacks

30

Source: Esacrypt, Checkpoint, FEV

1985 20001990 1995 20152005 2010

HummingBad

AS/1 Card Cracking

Morris Worm

SQL Slammer

CCC BTX Hack

Tordow

Tribe flood DDOS

Cohen

Brain Leandro

Sasser Conficker

Heartbleed

Wannacry

DaCaMo

Jeep hack

IMSI Catcher

Code Red

CAESS

Prism

GSM Interface exploit

Tesla hacks

Iloveyou

Hypothetical vulnerability identified Limited attack no known damage Attack with severe damage

Ve

hic

leM

ob

ileP

CS

erv

er

2018

Meltdown/

Spectre

Stuxnet Mirai

Meltdown/

Spectre

Meltdown/

Spectre

S-Gold 2



An Automotive Example that Shows the Importance of Cyber Security:

The Jeep Hack (2015) by Charlie Miller and Chris Valasek

Getting access: Using Wi-Fi to enter the system

Default password could be brute-forced within an hour

Inter-process communication interface (IPC) accessible via car Wi-Fi on TCP port;

authentication was not enabled

A service could be connected to IPC which allows to directly execute commands on the system

Going beyond: Sending CAN messages

Firmware on CAN controller (V850) was not signed and there was no validation at all,

so easily replaceable by a modified version

The impact:


The result: A recall of 1.4 million cars

Source: https://www.youtube.com/watch?v=MK0SrxBC1xs

Controlling … infotainment system steering & shift throttle & brakes air conditioning


FEV’s Four-Layer Approach for Cyber Security:

A Simple Example of an ADAS System Architecture

Braking Driving

Driver Interaction

Camera

ECU

Radar

ECUSonic

ECU

Steering

X-by-wire

32

LiDar

Higher data speed

High safety demand

Preprocessed data

Vehicle Interface

Connectivity

Smart sensors

Advanced Sensors


Telematics

Secure Interface (Layer 1)

Secure Gateway (Layer 2)

Gateway

Secure Network (Layer 3)

Secure Processing (Layer 4)

EPS ESC ECU

ADAS ECUHD

Localization

HMI

OBD # FEV EXAMPLE


FEV Has Developed a Gateway Platform For Connected Vehicles

33

OBDII Dongles Connected Vehicle GatewaySoM Development Platform

Connected Vehicle Gateways engineered & produced by FEV


Agenda

Introduction

Market Forecast

Customer Benefits

Core Technologies


Summary & Outlook



Summary & Outlook


Outlook

Summary

Autonomous and connected vehicles are expected to strongly

impact the automotive industry as well as public life

Autonomous driving according to SAE level 4 will reach a

noticeable market share in 2030

Customers will benefit from more comfort, increased safety and a

better system efficiency

Challenges for an imminent market entry of autonomous vehicles:

Setting up legal conditions

Ensuring functional safety on the one and cyber security on

the other hand

Reducing elec. power consumption for autonomous driving

functionalities with its calculation and control units as well as

sensors (currently up to 3 - 4 kW)

© by FEV – all rights reserved. Confidential – no passing on to third parties

Plenary Speech

Prof. Dr.-Ing. S. Pischinger

FUTURE MOBILITY

WITH AUTONOMOUS AND CONNECTED VEHICLES

Torino (Italy), November 27-28, 2018

Thank you for your

attention!

Documents

FUTURE MOBILITY WITH AUTONOMOUS AND CONNECTED …italy.vehiclemeetings.com/images/presentations/Stefan Pischinger presentation FEV.pdfSAE Automation Level Something Somewhere Sometimes