#817

Localization & Map-based ADASAUTTABLE OF CONTENTS
Introduction
Executive summary

Autonomous
VehiclesIn aiming to enhance the accessibility and overall user experience of their ADAS features and solutions, OEMs indifferent regions are overlaying map and localization information onto other on-board sensors. Among the benefitsof this process, for the end user and OEM alike, are the ability to optimize existing systems as well as theopportunity to deliver a more refined system functionality.

Localization and maps overview
Challenges
Goals

However, as localization solutions begin to gain popularity, it is essential for any automaker investigating them tohave a thorough, secure, understanding of their potential. This is especially important when considering the speed
at which such solutions can develop, often starting with a small area before scaling rapidly to city- or even statewide operations.

Techniques
OEM and Tier 1 case studies

The Localization & Map-based ADAS report outlines the key technologies already being used by OEMs, alongside
the various use cases they enable. In addition to analyzing the ecosystem for these technologies today, the report
also looks ahead, offering a five and ten-year outlook for the adoption of map and localization systems for ADAS.
Reflecting the broad scope of these solutions are illustrations of how they are implemented in a variety of use cases
to address OEM challenges and innovation goals.

Analysis
Future Outlook
Map Provider Profiles

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#817/Localization & Map-based ADAS

Key questions answered> Who is implementing
localization & mapbased ADAS, and withwhich partners?

> What are the technologicalenablers for localization
systems?

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Contents
817 – Localization & Map-Based ADAS 2023
Introduction ≫Executive summary ≫46Techniques ≫46• Localization techniques48

 Vehicle – to – infrastructure Global navigation satellite system

Localization and maps overview ≫11

 What inputs and methods can beused to localize a vehicle?

 Sensor based positioning19

 Crowd sourcing or survey
vehicles?

57 Survey vehicles

 Regional and legislative challenges

 AI processing Use of infrastructure in GNSS /GPS denied areas

OEM and tier 1 case studies ≫Goals ≫

27

 Autonomous driving goals

29

 Safety goals33

 Driver convenience goals40

 Multiple map suppliers Map-based augmented reality
guidance ADAS without HD maps

 Mercedes-Benz Automatic Valet
Parking Map usage outlook

 Relationships with map providers
 Localization outlook

 Tesla Full Self Driving
 Honda Legend SAE level 3
65

Map Provider Profiles ≫97

Next Steps ≫104

Glossary of Terms ≫109

 XPENG City Navigation Guided
Pilot Volkswagen I.D Buzz autonomous
taxis

Analysis ≫ BMW HD maps for hands-free
driving Managing safety burden without
maps

89

 Data use Waymo HD map matching Medium-definition map
 High-definition map

Future outlook ≫ Toyota Safety Sense Standard-definition map

 Processing and registering map
data Data labelling and change
verification

 Mercedes-Benz use of HD maps
for autonomy

 Crowdsourcing
Challenges ≫ Infrastructure implementations

 CARIAD smart parking structure Map matching• Map techniques Artificial intelligence and 5G

 Volvo and Polestar with Google HD
map
 Audi Predictive Efficiency Assist

 5G

 Why does localization and maps
matter?

 Energy and computing efficiency

 NIO battery swap station

 Dead reckoning and inertial
measurement units What are maps?

 Nissan HD maps for ProPilot Assist
2.082

Total number of pages - 112

2023

817

Localization & Map-based ADAS

Introduction

Introduction

Introduction
Though a clear path to automation has been set out with SAE levels of
autonomy, the actual path for OEMs, and teams within OEMs is not clear.
Increasing automation requires more hardware, which increases
cost. Increasing automation also requires hardware redundancy. At low
levels of automation, the OEM can consider the driver to be a form of
redundancy, but when automation increases to ‘hands off, eyes off’, this will
not be available.
High-Definition maps and map-based ADAS (advanced driver assistancesystems) have attraction as a critical technology by some to enhanceautonomous driving. HD maps contain information about environments in the
3D space. This could be information about lanes, traffic furniture and relative
elevation of the road. OEMs are already using maps in multiple ways toimprove and supplement ADAS.
One of the ways a map can improve ADAS is to supplement the on-board
sensing systems. A sensing system can be a camera and radar providing
information to a central computer, which then determines the location of thevehicle on the road.
Sensing systems can be noisy. They may have clutter, blur, weather
variations and time of day variations. Furthermore, developing a robust
system that can detector specific objects (traffic signs, road markings) is stillchallenging. Object detection in urban areas is more complicated.
In this report, we present use cases and recommendations on how to deploy
localization and map-based ADAS.

SectionContent

Executive summary

From the point of view of a vehicle manufacturer, this chapter
evaluates the motivations and concerns that need to be balanced
when developing localization systems and map based ADAS.

Localization and maps
overview

This chapter introduces the basics of localization and map based
ADAS.

Challenges associatedwith localization andmaps

A more in depth look at the challenges that OEMs and map
providers are facing.

Goals

A list of the goals that OEMs and Tier 1s may have whendeveloping localization systems.

Localization and map
techniques

Potential techniques for the goals outlined in the previous chapter.

OEM and Tier 1 casestudies

A closer look at different examples of how localization is being
used by OEMs and Tier 1s.

Analysis

SBD insight into the nuisances of localization and map based
ADAS, focusing on sensor fitment and the sourcing of maps. The
use of artificial intelligence and 5G is discussed.

Outlook

Appendix

A forward look and outline of the potential relationships between
map providers and OEMs and examples of companies not using
maps for ADAS.
Glossary of terms, list of OEMs and their predominant map data
suppliers. Profile pages for major map data suppliers.

66

Example slides
from the report

Executive Summary

A quick background to maps and localization
What are maps?
Map data shows the fire hydrant is
5 meters in front of the stop light.
5 meters

How are maps made?
Survey vehicle
Survey vehicles are equipped with advanced sensors
to detect and record changes in the environment withhigh accuracy• Highly accurate detection up to centimeter level.
• High costs and slower map refresh time.
Advanced, survey
vehicle, sensor suite

1. GPS
provides thelocation of thevehicle3. With this information,
the vehicle can localize
itself on the map. This
helps to prepare for the
traffic light.

Lidar

6 meters
1 meters

2. Sensor suite
detects fire hydrant

5 meters

Crowd source
Consumer vehicle sensor suite detects and records
changes in the environment• Can be less accurate than survey vehicle
equipment.
• Typically costs less and faster map updates thansurveying.
Lidar

Maps and Localization• Localization and maps are key for autonomous
goals. Localization is needed to support the vehicle
placing itself within an environment. Maps can add
safety benefits because they can provide thevehicle with information outside of the range ofthe sensors.

• OEMs can have localization without the use ofmaps, but not maps without the use of
localization. AI can support localization in a
dynamically generated model via object detection,
allowing for autonomous functions without a prerecorded map. Some OEMs have opted for thisapproach. It allows for the vehicles to be operated
on any unmapped road but at the cost of losing
some safety benefits of maps.

• Maps require localization as part of their platform.
Localization techniques are used to find where thevehicle is in the environment and place it in thecorresponding area in the map. Upkeep of thesemaps can be difficult depending on the map
resolution. HD (High-definition) maps are being
used by some OEMs for autonomous systems.
These maps will require frequent updates, higher
data transfer sizes, and higher costs to maintain.
SD (Standard-definition) and MD (Mediumdefinition) maps will be better for use cases that
require SAE level 2 capabilities.

88Localization and Maps

What is localization

What is localization?
Relative position

Absolute position
Latitude and Longitude and GPS Coordinates
Decimal: (52.015707, -0.793956)
Degrees, Minutes, Seconds:
52° 0' 56.5452'' N0° 47' 38.2416'' W
Determine
position relative
to road furniture

Localization can be the obtaining of the position of the vehicle on theearth.

•

Matching the absolute position on the globe is challenge because of
the size of the area being covered.

•

•
•

•
9

Lidar (Bumper or
top of windshield)
CameraSmall errors in sensing or map add up over large scales.

Long strings of latitude and longitude could result in floating point
errors, large storage needs, or efficiency tricks. Map formats andcompilation address these issues.
Navigation uses absolute positioning to match to mapped real-world
locations.

Radar

•

Using the relative position of a vehicle instead of the absolute
position reduces impact of global map errors.

• Relative positioning can be performed using road furniture and lane
features. These can be matched with a map to determine the vehicles
position relative to all items on the map.
• Relative positioning is used many ADAS such as Mobileye based
systems.
9Delays

Localization and Maps

Challenge No. 1: Delays to updates can occur
Data collection for a map can be by survey vehicle or crowdsourcing or both. Survey vehicles must be driven frequently to provide accurate data which is costly(and unrealistic). Crowdsourcing is reliant on private vehicles to collect data to map out an area. Lower grade sensors on consumer vehicles can lead to lessaccurate data collection than survey vehicles. Survey vehicles have expensive dedicated hardware for map data collection. Consumer privacy can be a concern withcrowdsourcing.

Map Provider

VehicleRealitychangesFirst
vehicleperceives
realitychanged‘x’number ofvehiclesperceivechangeMap
providerverifies
realitychangedMap
providerupdatesmap

Delays can happen here
Delays can happen here
• In urban areas, there is good chance a
sufficient number of vehicles will pass in ashort space of time. Around 15 minutes.
• In rural areas, it might take weeks forenough vehicles to pass the reality change
to consider it a true change.

• Depends on confidence level, weighting andlikelihood of change.
• Verification process can take time.
• Map providers are looking to optimize thisprocess. Delivering changes to SD maps
can be quick and aren’t safety critical, more
complex features should take longer.

Map
Map
provideruploads
map tocloud

Vehicledownloads
new map

Delays can happen here
• Map update interruption because of
infrastructure problems, or software
problems.
• If wireless signal is not strong enough, theupdate to the vehicle may fail.

• HD maps can quickly remove and change
information because systems prioritize
stopping distribution of wrong safety critical
data. New data is not always added straightaway, however. HD maps implement newer
expansion most slowly because it is critical
not to add bad data.

1010GoalsSafety compliance

Compliance with intelligent speed assistance regulation
Overview

Summary Table

Some regions have made intelligent speed assistance (ISA) mandatory, which canincrease driver awareness of the current speed limit (passively implemented, provide
warnings only) or prevent speeding by automatically decelerating (actively
implemented).
For example, European legislation has made ISA mandatory for all new vehicles
starting in 2022 and all vehicle types sold will be equipped with ISA, where 90%
accuracy is required. It means that digital map data needs to be in place to satisfy
this requirement, which is also pushed by Euro NCAP. Other regions, including theUSA, are expected to develop similar regulations.

Success Factors

Drivers

Lead Innovation

Smoother Drive

Consumer Demand

Added Customer
Convenience

Higher Autonomy

Increase Vehicle Safety

Sensor fusion technologies, high map accuracy and freshness.

SBD Insights
Taking ISA as an example, to show why map can act as an additional sensor on thevehicle, camera-based ISA can be sufficient in most of the scenarios, but cameras
handle conditional speed poorly which can impact compliance. A fusion approach canbe considered to mitigate the disadvantages of each sensor.
When it comes to safety regulation compliance, the required map freshness mayneed to improve (e.g. Euro NCAP stipulate the speed limits must be updated at least
quarterly), as incorrect map data can impact compliance.
European ISA regulations also require OEMs to have a long-term map updatestrategy, which OEMs already had in place voluntarily.

Sensor Costs<$1000$1000 - $5000>$5000Maturity
In-development

Pilot

Market ready

Map Level Required
SD Map

MD Map

HD Map11
11

Goals

Vehicle alignment

Vehicle alignment to refueling or recharging systems
Overview

Summary Table

Vehicle location is one of the primary data points that is needed to share with othersystems to achieve certain features. For example, in wireless charging the alignmentof the transmitter and receiver coils is significant for the high performance andefficiency of wireless power transfer. Some localization technologies considered here
include the UWB-based localization and camera and RF-based solution.
Nio launched their battery swapping service in January 2022 and upgraded theirbattery swapping stations in 2023, which enable Nio vehicles automatically park inthe battery swapping stations precisely and then conduct battery swapping. The
battery stations is equipped with 200+ sensors (cameras and ultrasonics) to localize
the vehicle and communicate to the vehicle to perform automatic maneuvering.

Success Factors

Drivers

Lead Innovation

Smoother Drive

Consumer Demand

Added Customer
Convenience

Higher Autonomy

Increase Vehicle Safety

Well-defined business model, V2I communication techniques and hardware, related
guidelines and standards.

SBD Insights
Vehicle alignment is the prerequisite condition for innovative solutions. The hardware
and software system need to be calibrated and well-developed to cooperate together
to achieve the features. This may require the solution providers work with car
manufacturers to gain more vehicle data and vehicle access. Another potential
bottleneck for these innovative solutions can be the scalability issue; finding asustainable way to gradually launch and expand the service can be challenging forthese solution providers.

Sensor Costs<$1000$1000 - $5000>$5000Maturity
In-development

Pilot

Market ready

Map Level Required
SD Map

MD Map

HD Map12
12

SolutionsⅼLocalization Inputs

ⅼ

GNSS

A note on global navigation satellite systems
Coverage of satellite constellations

Localization
Inputs

GLONASS• Global coverage since 2011

• Global coverage since 1995

• 1602 MHz band only

• 1176.45
band• Positional
metersConnectivityGPSaccuracyof

5-10• Positional
metersMaps

AI

Galileo• Global coverage.
• 1176.45
bands

Box Color

Constellation

Teal

QZSS

Dark Blue

NavICLight BlueGLONASS, GPS, BeiDou,
Galileo

Approximation of coverage shown

Geely

and

of

MHz

• 1176.45
bands

2-3

• Positional
metersNavIC• Low earth orbit (not GNNS)• Regional coverage• Can
supportV2X
autonomous driving• 1176.45
bands• Centimeter level accuracy• Global coverage since 20191575.42

accuracyandand• 3m accuracy

1575.42MHz• Positional accuracy of 3.5 –7.8 meters

BeiDouData
Collection

and1575.42and

1575.42

accuracy

MHz

of

2-3

QZSS
• Regional coverage since 2018
MHz

• Designed to enhance signal inurban canyons in Japan• 2 m accuracy, sub-meter withincluded augmentation signal

13
13

Case Studies

Hands-Free Driving

BMW HD maps for hands-free driving
Overview

Technical Information

BMW is utilizing HERE for Predictive Routing and Real-time traffic information, which is a
cloud-based routing functionality that learns individual driving patterns to propose more
personalized journeys. BMW group uses HERE HD Live Map for hands-free driving in the
United States and Canada. Hands-free driving is available on 7-Series, up to 85mph or 130km/h on selected highways.
The HERE HD Live Map provides road geometry, route profile and traffic signs, enablinghands-free driving on interstates and highways. HERE is planning to support the expansion oflevel 2+ support to all types of roads.

Sensor choice
Lidar

CameraLocalization Input
V2I

MapMatching

GNSS

Sensor-Based

Dead Reckoning/IMU

SAE Automation Level Demonstrated
L1

SBD Insights
HERE’s lane-level data serves as a redundant source of information to the vehicle on-board
sensors, which can provide additional quality assurances for the system. However, in reality,
the performance of the SAE level 2 hands-free driving is highly depended on how good theperception, decision and control systems are, as the live map data cannot determine thequality of the ADAS. Also, “hands free driving” can be a false term in the regions thatcurrently not legally allow driver’s hands off the steering wheels.

Radar

L2

L2+

L3

L4

L5

Localization Techniques & System Diagram

Radar

CameraTraffic Signs

Intel/Mobileye
chip

HERE HD map
Lane geometry

Future Possible Applications
Hands-free driving is considered as a SAE L2 ADAS, which needs constantdriver’s monitoring. This is the level that most OEMs and ADAS will stay for along period of time. However, this level of autonomy will have multiple
constraints so that many potential applications cannot be implemented since
the driver is still highly interacting with different ADAS.

14
14

Future Outlook

Data and map usage

OEM and Map Provider – pick and choose the data you use
The map can have varying levels of influenceGood signage
without complex
layouts

Road furniture
follows welldefined formats

Well definedlanes, and clearlymarkedThis is a potential operational design domain where dependence on a map canbe reduced• Currently, there is a clear use case for maps. The use of maps is a way of achieving
the required certainty in localization for autonomous functions.
• Rather than using a map in all cases, OEMs can approach the map as a sensor withvarying levels of influence. This helps distribute the safety burden more evenly
across other sensors.
• Level of trust and reliance on maps should depend on the type of information, theconfidence of the sensors, the parameters of the map (freshness, quality estimate
and precision). The system should find a middle ground between always trusting
the map and trusting a sensor more than a map.

15

• Maps can be expensive. Compilation and processing of maps is also costly. In
certain location types the map could be marked with ‘no map required here’.

Identify the data that allows you todifferentiate from competitors
Representation of datain the map ecosystem
Map providers may tailor a map to an
OEM. This cost of recompiling a map
each time is high. This is important
for an OEM to consider.
Map data that is not essential to its
operation and is generated by user.
This could be data related to regular
habits of the users or information
about destinations and journey times.
Core map data, provided by map supplier.

• Consider keeping the data that is not essential to map function, and not sharing
it with your map provider.
• As an OEM, you may have to accept that this will limit the map providers’ ability
to tailor and improve their system. Instead, in-house expertise would be needed
to benefit from this differentiating data.
• As shown above, there are different types of data that are present in the map
ecosystem. An OEM may wish to develop a strategy for identifying the data thatcan be used to help the OEM differentiate their ADAS from competitors.
• Expect crowd sourcing partners to be interested in receiving high quality data
and being involved in the specification of hardware (to ensure good quality data
is provided).
15

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