Technology of Autonomous Vehicles basic concepts, types ...

developments:

• vehicle technology

• infocommunication

• energetics

smart and connected vehicle (V2X)

V2I: traffic sign, emergency situation, etc.

V2V: location sharing, emergency situation, etc.

alternative fuels - electromobility

Technology of Autonomous Vehicles –basic concepts, types, operational characteristics

Automated functions

• programmed rules

• predetermined, step-by-step, clearly described

• manage known situations

Autonomous functions

• data collection: perception/from other sources

• cognitive capabilities, individual decision making

• manage not known situations

cognitive capability: recognition and persistent learning capabilitycreate new, reliable, value-added information

use experience, knowledge, secondary information sources

Automated vehiclesseparated track, closed from the traffic

Autonomous vehiclesunseparated track in the traffic

available new vehicles today: SAE 1-2

developments in test phases:

• Tesla autopilot system (SAE 2)

• driverless pods, Google/Waymo, UBER (SAE 4)

SAE levels

BASt levels

NHTSA levels

execution of steering and acceleration/deceleration

monitoring of driving

environment

fallback performace of

dynamic driving task

system capability

(driving modes)

0 no automation driver only 0 human driver human driver human driver -

1driver

assistanceassisted 1

human driverand system

human driver human driversome drivng

modes

2partial

automationpartially

automated2 system human driver human driver

some drivng modes

3conditional automation

highly automated

3 system system human driversome driving

modes

4high

automationfully

automated3/4

system system systemsome drivng

modes

5 full automation - system system systemall driving

modes

SAE | Society of Automotive (USA)

BASt | Bundesanstalt für Straßenwesen (Germany)

NHTSA | National Highway Traffic Safety Administration (USA)

Automation levels – vehicle control

source: SAE International

Available driver assistance functions

• warning e.g. lane keeping assistance, blind spot detection, distance warning system,

• emergency assistance e.g. ASR - Anti Slip Regulation, ESP - Electronic Stability Program, adaptive brake assistance,

• improving capacity and efficiency e.g. tempomat, traffic light assistance,

• improving comfort e.g. parking assistance

Devices for self-driving

Hardware devices:

• GPS – localization (+network map!)

• LIDAR – distance measure -> point cloud (3D)

• camera – traffic sign, traffic light, lane recognition

• radar – distance keeping

• sensor – environment detection – e.g. LGPR

Software devices – decision making:

automated image recognition, artificial intelligence -persistent learning

LIDAR can be replaced: cameras (360° angel of view, monocamera + AI)

Autonomous vehicleis a rolling IT device

Intelligent road infrastructure

where is the intelligence? – vehicle vs. infrastructure

• sensors (e.g. weather, road condition, traffic situation)

• V2I: messages between vehicle and infrastructure

• V2V: messages between vehicles – without road signs/markers

BUT! road signs/markers are necessary for soft mobility modes

developers:

conventional vehicle manufacture/

supplying industry/IT company/start-up

development goal:

service oriented (UBER)/product oriented (Tesla)

TEST PHASE – accompanying staff (wheels/pedals and emergency stop button)

goal: experience collection – machine learning

passenger reaction analyzing

Who do you trust better?

What will be the transportation in the future?

sub-system development vs. entirely vehicle development

vehicle conversion vs. new vehicle

test environment: closed test track vs. existing (urban/motorway area)

International practice

types of development:

• car: Tesla, BMW, Audi, UBER, Google/Waymo’s

• small bus – pod: Easymile, Navya Arma, Local Motors

• bus: Mercedes

• truck: Volvo (Otto)

pod-like services in test:

• Berlin: university campus (DB)

• Berlin: hospital buildings (BVG)

• Vienna: smart city quarter (WienerLinien)

• Wageningen–Ede, Netherland: between cities in public roads

• Civaux, France: nuclear power station

• Citymobile2 EU project (Trikala, Vantaa, La Rochelle)

• isolated areas

• fix timetable or route

1-4

Hungary:

• university researches

• RECAR – research center

• education - Autonomous Vehicle Control Engineer - MSc (BME-ELTE) – expected launch: 2018 autumn

• ZalaZone Zalaegerszeg - Autóipari Próbapálya Zala Kft. – closed test track

• Budapest, Szépvölgyi út - test environment in public roads - AImotive Kft.

Model of Transportation System and Mobility Services based on

Autonomous Vehicles

complex system

generate alterations

ENVIRONMENT

MOBILITY

MOBILITY SERVICE

PlanningOperat ional managment

TRAFFIC

Modelling

Mobility management

Environmental impacts

Economic impacts

INFRASTRUCTURE

Transportat ion infrastructure management

VEHICLE

Vehicle technology and

control

Info-communicat ion infrastructure management

Energy infrastructure management

Traffic control and management

SOCIETY

Acceptance

Travel habits (model share)

Human abilit ies

Land use

Modelling

Legal aspects

Qualityassesment

Model of transportation system

System architecture

Telecommunicat ion services

5. Integrated mobility

management centre

6. Operators

Mobilit y

system4.

Autonomous

vehicle

Intelligent infrastructure

Passenger informationSensing and control

Sensors, cameras, radars,

LIDAR, etc.

High capacity

computers

Braking,steering,

propulsion subsystems

database

Display

Emergency control units

Cameras

Speakers

Interact ive terminals

V2V

6.d.

Fleet operators

6.a.

Transportation network

6.c.

Charging infrastructure

6.b.

Road network

Infrastructure operators

2. Smart stop

Display

Speakers

Cameras

Interact ive

terminals

Emergency

cont rol units

3.

Charging

station and

Depot

5.B.

Traffic control center

5.A. Mobility service

management centre

Public

transport

companies

Other

transport

companies

Smartphone

Passen-

gers

1. Passenger

Passenger

transport

operators

Other

organisation

Weather

information

provider

Financel

service

provider

Authorit ies

the (passenger) transportation transfer into a special information system

System components

1. Passenger – with smart device

2. Smart stop

3. Charging station and Depot

4. Autonomous vehicle

5. Integrated mobility management centre

A. Mobility service management centre

B. Traffic control centre

6. Operators

a. Transportation network

b. Road network

c. Charging infrastructure

d. Fleetautonomy is a relative concept

Integrated mobility management centre

Traffic control centre

• control and prediction of traffic parameters

• provision of real-time traffic information

• data exchange with road network operators

• statistical analysis of traffic parameters

supplying data for the decision-making

Mobility management centre

• planning

• organizing

• control of mobility process

Smart stop

• equipped with devices → improve physical and mental comfort

• automated/autonomous functions

• mobility related and not-mobility related services

• renewable energy sources

• high comfort level

• intermodal facilities

mobility related

services

informat ion provision

ticketingintermodal

services

e-vehicle charging stat ion

bike sharing stat ion

bike parking

not-mobility

related services

heated seats

entertainment

phonecharging

WiFi

comfort

air-conditioning

food/drink machine

supplementary

services

recycle waste

pick-pack point

ATMinformat ion provision

(POI, weather)

services

cont rol

purchase

valiadation

warning

general informat ion

real-time informat ion

informat ion services physical servicesphysical

services

informat ion

services

5

Operational model

system dynamism – management of real-time data

Dat

a ac

cura

cyH

igh

Low

Dat

a ac

cura

cyH

igh

Low

Service

environment

Preliminary

operational plans

STATIC ATTRIBUTES

Real-time

operational plans

Pla

nned o

pera

tion

Real

opear

atio

n

Planning funct ions

Operat ional funct ions

(real-t ime management and control)

5.Integrated mobil ity

management centre

Current

situation

Databases

DYNAMIC ATTRIBUTES

Infrast ructure and t ransport network

2. Smart stop

3. charging

station and

Depot

1.

Passenger4.

Autonomous

Vehicle (AV)

Infrast ructure and t ransport network

2. Smart stop

3. charging

station and

Depot

1.

Passenger4.

Autonomous

Vehicle (AV)

Current automated (autonomous) transportaiton modes

(aviation – robotpilot)

underground

people mover

GRT (Group Rapid Transit)

PRT (Personal Rapid Transit)

ride-sourcing (ride-hailing) - taxi

• UBER, Grab, Google/Waymo

• test phase

6-7

Alteration in transportation modes

service-oriented approach instead of vehicle-oriented approach

individual AVonly for the most flexible travel purposes

smaller –> spatial utilization, drive into buildings

public transportation

arterial network, high volume of passenger

STA (Shared Transportaiton based on

Autonomous vehicle)

merging existing ’transitional’ modes

bicycle, bike-sharingunaltered volume

pedelec

Hagyományos közforgalmú közlekedés

flexibiltiyunflexible(fix route/fix timetable)

flexible(no fix route/no timetable)

number of passengers per vehicle

GRT

DRT ride-sharing car-

sharing PRT

taxichauffeur

service

ride-sourcingindividual

AV use

STA

Legend: no altering altering merging

under-ground

autonomous tram/bus

people mover

1000

100

1 bike-sharing

high capacities modes

biking

public transportation modes

new

STA service types

• small-capacity

• demand driven

(demand-responsive)

• door-to-door/feeder

• advance ordering

• customized

• mobil application based

O1

S1 taxi D1

O1 Dn

F1 F2 Fn

S2 shared taxi

S3 feeder pod

S4 fix route

pod

O1

On D1

legend: On origin Dn destination Fn fix stop

O1 On F1 Dn

high capacity linecar podwalkingmovements:

Fn

DnFn

S1

taxi

S2

shared taxi

S3

feeder pod

S4

fix route pod

Vehicle type Car Car Pod Pod

Sharing No Yes Yes YesModality Door-to-door Door-to-door Feeder Feeder

Capacity Demand-driven Demand-driven Rather demand-driven Demand-responsive

Scheduling Flexible Flexible Semi-fix (guaranteed transfer) Fix (additional departures)Boarding point Flexible Flexible Semi-fix (only in the zone) Fix stop

Alighting point Flexible Flexible Fix stop Fix stopRoute Flexible Flexible Semi-fix (fix arrival stop) FixAccess walking No No No Yes

Egress walking No No Yes Yes

transportation is more personalized but

planned in advance

STA

motorized urban transportation

automated autonomous

elevator,

escalator,

moving

walkway

t ram buspeople

mover

automated rather

autonomous

mostly

autonomous

under-

ground

indivi-

dual car

autonomousauto

mat

ion

path

unseparatedrather

unseparated

rather

separatedseparated

schedule

demand-drivenrather

demand-

driven

mostly

fixfixrather

fix

Legend: classification in: typical cases special cases

demand-

responsive

feeder

pod

fix route pod

shared

taxitaxi

Categorization of motorized urban transportation means

Future categories of passenger transportation:

• Individual transportation

o non-motorized: walking, biking

o motorized: individual AV use (motorcycle use),

• Public transportation

o small capacity

▪ non-motorized: bike-sharing,

▪ motorized: STA,

o high capacity (mass transit) based on AVs (e.g. bus, tram)

or highly automated vehicle (e.g. underground).

Future public transportation:

Accessible by anyone who is willing to pay a fare; the vehicle is operated by any

type of companies (or private person); however, the services are regulated,

managed (and controlled) by the mobility management centre.

Mobility-as-a-Service (MaaS) with autonomous vehicles

• vehicles managed by mobility management centres

• concept of MaaS – more efficient

Conventional MaaS MaaS based on AVs

Drivers Necessary in most

transportation modes

Elimination

Involved services Public/semi-public/ private

services

Public service

Passenger handling Human-based and based-

on application

Automated, based-on

only application

Task coordination Complicated

(several operators)

More reliable, efficient

coordination

Dispatching Drivers need to respond Automatic

Comparison of conventional and AV-based MaaS

Delivery

last mile collection/distribution (and long distance delivery)

– safety/security?

– parcel handling? – (un)loading, receipt (identification, payment)

– on ground/in the air (e.g. drone)

delivery-sourcing: e.g. UBEReats, Pickitapp

Combined ride-sourcing

order vehicle for traveling or sending package/purchase a product and home delivery