Highly accurate navigation in the age of automated driving

19th September 2017
Stefan Pruisken Sr. Product Manager
Webinar: Highly accurate navigation
in the age of automated driving

2© Elektrobit (EB) 2017 | Confidential
Outline
Highly accurate navigation in the age of automated driving
Electronic horizon – Key technology for
using navigation assets
Evolution of in-vehicle system architecture
From navigation to automated driving

Outline

Evolution of a vehicle navigation system
Complexity/Functionality
Time
Navigation Driver Assistance Automated Driving
 Standard Navigation Maps
 Map attributes used for driver’s
navigation only  no ADAS
 TMC traffic data
 Annual map update
 GNSS positioning
 Standard Navigation Maps + ADAS
attributes
 Reliability of few ADAS attributes as
needed for ADAS functions
 Online traffic, 3rd party content
 Quarterly to semi-annual map update
 GNSS & Dead reckoning positioning
 HAD Maps / Live Data
 High reliability of dedicated ADAS
attributes
 Online traffic, 3rd party content
 On demand daily map update
 Lane accurate positioning/localization
How to deal with increased complexity?
HAD functionality | online services | accurate localizationArchitecture is the key to managing complexity
past today future

Evolution of E/E architectures
past today future
Domain controller architecture
 Dependability and security supporting
architecture
 Performance and connectivity orientated
 Service oriented communication
Centralized functional architecture
 Fully virtualized high
performance computers
 Ring architecture for redundancy
Central gateway architecture
 Up to 100 ECUs in one car
 One ECU for one function
 Signal based communication
Classic ECU Performance/Safety ECU “small ECU”/Sensor/Actuator Virtualized Performance Cluster
Architecture using dedicated
computing cluster

Today’s in-vehicle navigation architecture
Route | Static & Dynamic Data
HAD
Map
Nav
Map
Map
matcher
UI Navigation
EHP
Camera
RADAR
LiDAR
GNSS
Odometer
Gyroscope
Accelerometer
Lane
Accurate
Position
Sensor Data
Environmental
Sensor Data
HD Positioning
Dead
Reckoning
Ego Motion
Estimation
Calibration
Environm.
Sensor Fusion
ADAS function (e.g. Active lane assist)
Speed
Control
Steering
Control
Lane change
Lane keeping
EHR
Environment perception
Lane fusion …
Ego Motion
UI computing cluster HAD computing cluster
UI Infotainment

7© Elektrobit (EB) 2017 | Confidential 7© Elektrobit (EB) 2017 | Confidential
 Number of integrated functionality increases and so does complexity
 Demand of high computation power
 Introduction of centralized HAD and UI computing cluster
 The asset of a scalable architecture is shown by…
 Reliability
 Controllability
 Testability
Summary – Evolution of in-vehicle system architecture
UI computing cluster HAD computing cluster
Modern navigation
?
Is a modern navigation really an “UI application”?

Outline

Electronic horizon
Navigation
Electronic horizon provider
ADASISv2 | ADASISv3
Dynamic
data
Maps
Map and navigation data become
additional predictive sensors for ADAS
The standard electronic horizon transmission protocol for the communication
between provider and reconstructor is developed by the ADASIS consortium
It integrates:
 Map matched positioning
 Most probable path (MPP)
 Static map attributes
Curvature, slopes, speed limits, road
class, etc.
 Dynamic data
Route, traffic data, hazard warnings,
road construction data, weather, etc.
Automated driving
Fuel efficient driving
Predictive curve light
Curve speed warning
Horizonreconstructor
The electronic horizon provides
other ECUs a continuous forecast of
the upcoming road network by using
optimized transmission protocols.
ADAS applications:
i

Electronic horizon – Provider (EHP)
On board maps &
connected services
On demand map
download
On board maps
Basic maps Highly automated driving (HAD) maps
Static HAD
electronic horizon
Electronic horizon
1
Connected
electronic horizon
2
HAD
electronic horizon
3
Connectivity
Content
Addressing…
 Entry ADAS applications
 High end ADAS applications
 SAE Level >3 applications
1
2
3
dynamic data:
traffic, parking, weather, etc.
manual map updates
HAD maps,
lane level resolution,
live layer
road topology, speed limits,
slopes, curvature, etc.
lane boundaries & groups, lane
connectivity, polygonal lane description
v2
v2
v3
EHP
ADASIS

Reassembles and stores efficiently relevant electronic horizon data
Electronic horizon – Reconstructor (EHR)
ADAS ECU - A -
EHR
ADAS
Function
A.1
EHP protocol
Electronic
horizon
data
API
protocol
handler
ADAS ECU - B -
EHR ADAS
Function
B.1
Electronic
horizon
data
API
protocol
handler
ADAS
Function
B.2
Protocol compatibility
 A reconstructor ensures total compatibility to connected provider
 Protocol compliance is being observed and issues are reported
Configurability
 Each reconstructor is able to be configured to store only electronic
horizon data which is relevant for the ADAS ECU
Usage of generic interface
 Vehicles communication buses like CAN, Flexray, Ethernet are
encapsulated
 ADAS functions access electronic horizon data via a convenient and
stable interface
Functional Safety
 A reconstructor ensures electronic horizon protocol integrity as it
may run on ECUs which are subject for ASIL allocations
1
2
3
4

ADAS applications benefitting from electronic horizon
Passive driver assistant systems
Curve Speed Warning
Traffic Sign AssistantIntersection Warning
Predictive Curve Light Night Vision
Diesel Particulate Filter Control Power Train Control
Economic/ecologic assistant systems
Eco Drive Assist Range Control Assist
future
Active driver assistant systems
Adaptive Cruise Control
Forward Collision Warning Lane Keep Assist
…
Full automation
past today
Partial, conditional and high automation
Highway PilotTraffic Jam Assist
Automated Valet Parking Urban Driving

ADASIS protocol versions compared
ADASISv2 ADASISv3
Purpose
Standard and ADAS map data for advanced driver assistance
applications
HAD map data for highly automated driving
Vehicle bus Designed for CAN bus Broadband connection (Ethernet, TCP/IP)
Communication scheme
Broadcast communication:
 1 provider, n clients
Bi-directional communication mechanisms supported:
 Broadcast for most probable path (MPP)
 Publish-Subscribe (P2P) for additional attribute information
 Multiple sub-provider
Electronic horizon road
network representation
Single tree support Support for multiple independent trees
Most probable path (MPP) and
tree length
Up to 8190m (13 bit) ~Up to 43.000 km (32 bit)
Num. of possible profile types 31 attribute profiles Room for up to 232 bit possible profiles, 45 types currently specified
Profile attribute value range
Profile short: 10 bit
Profile long: 32 bit
64 bit for all profile attributes
Attribute resolution Meter [m] Centimeter [cm]
Content
 Standard map attribute profiles on link level
 Traffic data
 Map attribute profiles on lane level
 Extended road lane model | Detailed intersection model
 Road boundaries / furniture / land marks

ADASISv3: Intelligent data update mechanisms
Publishers
Sub provider
Camera
Sensors
Fill or update electronic
horizon data
Send electronic
horizon skeleton
Listen to
electronic horizon
ADAS
Function
Reconstructor
ADAS
Function
Reconstructor
ADAS
Application
Reconstructor
Sub provider
Reconstructor
Reconstructor
Reconstructor
Electronic horizon data update support
Change attribute value and/or offset position without update of the whole path
Multiple provider support
Multiple provider can fill in the electronic horizon in
order to support dynamic information and sensor fusion
Electronic
horizon provider A D A S I S v 3

 ADASIS consortium standardizes the next generation electronic horizon protocol addressing
HAD applications: ADASISv3
 Positioning, static map (e.g. road/lane topology) and dynamic data (e.g. traffic) is transmitted
in an effective and safe way
 All transmitted information is geo-referenced and utilizable for all ADAS applications
 With ADASISv3 electronic horizon applications gain possibility to transmit updated data
Summary – Electronic horizon
Electronic horizon connects both worlds: UI & HAD

Outline

Generic levels of path planning and navigation contribution
Guided Route
Exit ramps
Intersection structure
Lane divider
Turn lanes
Safe-State support
Traffic signs
Pedestrian
in car‘s
path
high
low
high
low
Strategic
60 sec - ∞
Where do I need
to get off the highway?
Tactical
3-60 sec
Which lane to turn right
in this intersection?
Reactive
0-3 sec
How do I not hit
that pedestrian?

Combining strategic, tactical and reactive planning for highly automated driving
Automated Driving – Closing the Gap
Sensor/object data (camera, LiDAR, RADAR, etc.)Extended navigation map
HAD map with detailed lane information
Lane accurate positioning
Strategic path planning
on link level (~60 - ∞ sec.)
Tactical path planning
on lane level (~10 – 60 sec.)
 Provides the route to a selected
destination for an AD drive
 Suitability of road segments to be
considered for route calculation
 Generates lane change advices according
to driven lane and lane situation for
upcoming maneuvers along the route
 Computes AD vehicle trajectories based
on lane change advices
 Consideration of surrounding traffic and
sensed road characteristics (i.e. lanes)
Reactive path planning
on lane/geometry level (~10 sec.)
Route
Lane change advice

Advanced in-vehicle navigation control architecture
Route
HAD
Map
Nav
Map
Map
Matcher
UI Navigation Core
Camera
RADAR
LiDAR
GNSS
Odometer
Gyroscope
Accelerometer
Lane
Accurate
Position
HAD Navigation Core EHP
HAD
Map
Nav
Map
HAD Map
Matcher
Av3
Sensor Data
Environmental
Sensor Data
HD Positioning
Dead
Reckoning
Ego Motion
Estimation
Calibration
Environm.
Sensor Fusion
Active lane assist
Speed
Control
Steering
Control
Lane change
Lane keeping
EHR
Environment Model
Lane fusion …
Ego Motion
Strategic path planning Tactical path planning Reactive path planning
A D A S I S v 3
Av3
UI Infotainment
Lane data
Lane advice
Route
Route
Subject to ISO26262
Subject to ISO26262Subject to ISO26262
UIComputingCluster
HADComputingCluster

Navigation assets support highly automated driving
Geo fencing
 Is vehicle located on a HAD permitted road?
 Provide HAD transition locations and prepare driver in time
Calculate HAD routes
 Prefer HAD permitted roads
 Route with a minimum needed lane changes
 Prefer clearly arranged intersections for performing maneuvers
 Avoid complex and dangerous intersections
Generate HAD maneuver advices
 Generate optimal lane change prompts
 Provide lane change ranges
 Provide “points of no return”
Provide smart safe state locations
 Take local road traffic regulations into account
 Medical emergency break after highway entry point
Keep navigation UI and HAD behavior in sync to each other
1
2
3
4
5
Navigation Core
Real Time Traffic
Incidents & Flow
Destination Input
Online Parking
Information
Local Weather
Information
Route Calculation
Maneuver
Generation
Voice Guidance
Positioning /
Map Matching
Route
Import / Export
Map
Rendering
Route

Summary – From navigation to automated driving
Classical in-vehicle navigation will experience ground breaking disruption!
Navigation to support vehicle’s path planning capability
 Navigation based strategical and tactical path planning will increase
overall HAD availability
 Provide smart safe state locations
 Gain driver’s trust and positive experience
ADASISv3 enables navigation based HAD functions
 Industry’s standard to transmit geo referenced information
 Transmit static and dynamic data which is able to be updated
Keep driver’s UI and vehicle’s HAD behavior in sync
 ADASISv3 connects the UI and HAD world
 Generate optimal lane change prompts
today
future

www.elektrobit.com
stefan.pruisken@elektrobit.com
Get in touch!

Highly accurate navigation in the age of automated driving

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Highly accurate navigation in the age of automated driving

Similar to Highly accurate navigation in the age of automated driving (20)

Recently uploaded

Recently uploaded (20)

Highly accurate navigation in the age of automated driving