d3.1 report on lane level navigation application and ... · imu inertial measurement unit ins...
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Grant Agreement Number: 687458
Project acronym: INLANE
Project full title: Low Cost GNSS and Computer Vision Fusion for Accurate Lane Level
Navigation and Enhanced Automatic Map Generation
D. 3.1 Report on Lane level Navigation Application and Enhanced Maps
Due delivery date: 31-‐12-‐2016
Actual delivery date: 31-‐12-‐2016
Organization name of lead participant for this deliverable: Technical University Eindhoven (TUE)
Project co-‐funded by the European Commission within Horizon 2020 and managed by the European GNSS Agency (GSA)
Dissemination level
PU Public X PP Restricted to other programme participants (including the GSA)
RE Restricted to a group specified by the consortium (including the GSA)
CO Confidential , only for members of the consortium (including the GSA)
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Document Control Sheet Deliverable number: 3.1
Deliverable responsible: Technical University Eindhoven (TUE)
Workpackage: 3
Editor: Gijs Dubbelman
Author(s) – in alphabetical order
Name Organisation E-‐mail
Gijs Dubbelman TUE [email protected]
Roland van Venrooy TomTom [email protected]
Martijn de Greef TomTom [email protected]
Document Revision History
Version Date Modifications Introduced
Modification Reason Modified by
V 0.1 25/11/2016 First version Gijs Dubbelman
V 0.2 14/12/2016 Revision after review J. Eggert Gijs Dubbelman
V 1.0 22/12/2016 First final version after review J. Eggert Gijs Dubbelman
Abstract
This document reports on the progress of the deliverables:
• D3.3 Lane level navigation application for Android v1 • D3.5 Enhanced map databases and software v1 • D3.7 SDK for third-party interfacing
Legal Disclaimer The information in this document is provided “as is”, and no guarantee or warranty is given that the information is fit for any particular purpose. The above referenced consortium members shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials subject to any liability which is mandatory due to applicable law. © 2016 by INLANE Consortium.
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Abbreviations and Acronyms Acronym Definition
AC Alternating Current
ADAS Advanced Driver Assistance Systems
APK Android Application Package
ARB Architecture Review Board
ATX Advanced Technology Extended
BLE Bluetooth Low Energy
CAN Controller Area Network
CPU Central Processing Unit
DC Direct Current
DITCM Dutch Integrated Testsite Cooperative Mobility
DR Dead Reckoning
ECU Engine Control Unit
EDAS EGNOS Data Access Service
EDR Enhanced Data Rate
EGNOS European Geostationary Navigation Overlay Service
ENU East-North-Up
FPS Frames Per Second
GLONASS Global Navigation Satellite System
GMSL Gigabit Multimedia Serial Link
GNSS Global Navigation Satellite System
GPS Global Positioning System
GPU Graphics Processing Unit
HAD Highly Automated Driving
HDR High Dynamic Range
HMI Human Machine Interface
HTML Hypertext Markup Language
IDE Integrated Development Environment
IMU Inertial Measurement Unit
INS Inertial Navigation System
IoT Internet of Things
ITS Intelligent Transport System
KPI Key Performance Indicator
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LBDO Location-Based Dynamic Objects
LDM Local Dynamic Map
LVDS Low Voltage Differential Signaling
MEMS Microelectromechanical Systems
NDS Navigation Data Standard
NMEA National Marine Electronics Association
OEM Original Equipment Manufacturer
OS Operating System
PC Personal Computer
PCB Printed Circuit Board
PCI Peripheral Component Interconnect
R&D Research & Development
RAM Random-Access Memory
ROI Region of Interest
SDK Software Development Kit
SLAM Simultaneous Localization and Mapping
SoC System-on-Chip
TPEG Transport Protocol Experts Group
UAV Unmanned Aerial Vehicle
UGV Unmanned Ground Vehicle
UI User Interface
USB Universal Serial Bus
UTC Coordinated Universal Time
V2X Vehicle-to-everything
VSLAM Visual SLAM
WGS84 World Geodetic System 1984
WP Work Package
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Table of Contents Executive Summary ............................................................................................................................... 7 1. Introduction ..................................................................................................................................... 8
1.1 Purpose of Document ............................................................................................................ 8 1.2 Intended audience .................................................................................................................. 8
2. Embedded Platforms for Lane Level Navigation ............................................................................ 9 2.1 Off-the-shelf device with lane guidance functionality ............................................................. 9 2.2 Retrofit cooperative navigation prototype ............................................................................. 10 2.3 In-car lane navigation prototype for automated driving ........................................................ 11
3. HD Maps (TomTom) ..................................................................................................................... 12 3.1 3D-Lane Map layer ............................................................................................................... 13 3.2 Road-DNA layer ................................................................................................................... 13
4. Map-based positioning .................................................................................................................. 15 4.1 Lane matcher ....................................................................................................................... 15 4.2 RoadDNA correlator ............................................................................................................. 15
5. HMI ............................................................................................................................................... 17 5.1 Lane renderer ....................................................................................................................... 17 5.2 Lane advice HMI .................................................................................................................. 18
6. SDK for Interfacing to the Dynamic Lane Navigation Engine ....................................................... 19 6.1 Application interface ............................................................................................................. 19 6.2 Positioning interface ............................................................................................................. 19 6.3 Local dynamic map interface ............................................................................................... 19
7. Conclusion .................................................................................................................................... 20
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List of Figures Figure 1: Document structure with respect to deliverables and application components. ..................... 8 Figure 2: TomTom bridge navigation device extended with lane guidance functionality. ...................... 9 Figure 3: Cooperative navigation prototype ........................................................................................ 10 Figure 4: Set-up that will be available in the TUE-TASS autonomous driving research platform. ....... 11 Figure 5: TomTom HD maps layers .................................................................................................... 12 Figure 6: Visualization of TomTom RoadDNA. .................................................................................... 13 Figure 7: Example of RoadDNA. .......................................................................................................... 14 Figure 8: A 3-D visualization of the RoadDNA segment of Figure 7. ................................................... 14 Figure 9: Visualization of Lane detector results that are matched against the 3D Lane-map layer. .... 15 Figure 10: Visualization of RoadDNA correlation of Stereo point clouds. ............................................ 16 Figure 11: Overlay view with all icons (top row) active. ....................................................................... 17 Figure 12: Output of the 3D lane renderer. .......................................................................................... 18 Figure 13: 3D lane map presentation augmented with the lane plan .................................................. 18 Figure 14: Illustration of LDM layers and the IoT connectivity device. ................................................. 19 List of Tables Table 1: Overview of Lane level navigation platform prototypes ........................................................... 9
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Executive Summary This document reports the status of the lane-level navigation application and its key components after the first year (M12) of the INLANE project. As the total duration of the project is 30 months, this document does not reflect the final outcomes of the project. Specifically, this document reports on the progress of the deliverables:
• D3.3 Lane level navigation application for Android v1. • D3.5 Enhanced map databases and software v1. • D3.7 SDK for third-party interfacing.
The responsible partner and main contributor for these deliverables is TomTom. At TomTom INLANE is part of a broader R&D effort on lane level accurate positioning and navigation.
The key lesson learned during the first year of the project is that current and next-generation consumer-grade mobile (personal navigation) devices are not reliable enough (in terms of computation and sensing capabilities) to deliver a viable Lane navigation product experience. Therefore, a shift to more reliable after-market multi-functional devices or in-car systems is required. The concept, as proposed in INLANE, of an after-market/in-car sub-system with a HMI sub-system running on a personal mobile device, is still viable.
Regardless of the platform, reliable lane accurate positioning and lane accurate maps are still under development. The fusion, as addressed in INLANE, of multiple localization sources is paramount. Promising techniques are those who directly localize the vehicle in a map using real-time lane marking information or 3D point cloud data. Next generation maps will support this by having additional layers on top of the layers which are currently used for route planning. Envisioning the availability of a suitable platform and reliable lane accurate positioning and lane accurate maps, several HMI concepts are researched and presented.
Extensive effort is put in making existing and novel components available in RTMaps middleware, such that they can be integrated with the components of INLANE partners during the second year of the project. Furthermore, a first version of the third-party SDK has been released by TomTom.
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1. Introduction This document reports the status of the lane-level navigation application and its key components. The relations of the components, the deliverables, and the sections is illustrated below.
Figure 1: Document structure with respect to deliverables and application components.
1.1 Purpose of Document The purpose of this document is to report on the progress of the deliverables:
• D3.3 Lane level navigation application for Android v1. • D3.5 Enhanced map databases and software v1. • D3.7 SDK for third-party interfacing.
The responsible partner and main contributor for these deliverables is TomTom.
The relation of this document’s sections and the deliverables is:
In Section 2, the platforms for the lane level navigation application are discussed including an Android-based version (D3.3). For each platform the current status is provided.
In Section 3, the TomTom HD Maps are outlined (D3.5). These are next-generation enhanced maps that support automated driving and that are required for the Lane level navigation application (D3.3).
In Section 4, the core software engines that realize the lane-level navigation application (D3.3), using TomTom HD maps (D3.5), are detailed.
In Section 5, the HMI concepts that are under development for the Lane-level navigation application (D3.3) are provided.
In Section 6, information on the SDK for third party interfacing (D3.7) is provided.
1.2 Intended audience The primary audience for this deliverable are the project partners, the reference board, and the PO. Secondary, this report is made publically available. However, as it is a mid-term report and does not contain the final outcomes of the project, the usage of this document by (public) third parties is expected to be limited.
D3.5 Enhanced map DB Section 2
D3.5 Localization engines Section 4
D3.3 Application platforms Section 1
D3.3 Application HMIs Section 5
D3.7 SDK Section 6
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2. Embedded Platforms for Lane Level Navigation Next to the main-stream prototyping platform, an embedded platform was created for in-car experiments to run the ‘dynamic lane level navigation’ functionality taking embedded constraints into account of ‘realistic’ product concepts. The following product concepts were created:
Table 1: Overview of Lane level navigation platform prototypes
Platforms Section Map Computation Sensors Off-the-shelf 2.1 Regular Qualcomm Snapdragon Consumer-grade Retrofit prototype 2.2 Enhanced NVidia Tegra X1 Automotive-grade In-car prototype 2.3 Enhanced DrivePX2 or similar Automotive-grade
2.1 Off-‐the-‐shelf device with lane guidance functionality This prototype is created upgrading an off-the-shelf navigation device that integrates a camera. The navigation software on the device is extended with software detecting lane markings and matching the position with the observed lane markings to a lane position in the map. The navigation application is enhanced with a widget in the HMI display, showing the lane the car is driving in. For this product concept, the standard map as used in commercial navigation systems is used.
Figure 2: TomTom bridge navigation device extended with lane guidance functionality.
The current status is: This activity started before INLANE and finished in the first year of INLANE. The conclusion for now is that this approach using off-the-shelf navigation devices will not lead to a viable product, for reason that the fusion of consumer-grade GPS, current navigation maps and camera in an after- market device does not lead to the required lane positioning accuracy to be able to create a good user-experience. The estimated lane position is simply too many times off the real lane position, which renders the functionality unusable from a user perspective. Key shortcomings that were identified and that will be addressed in INLANE are: 1) current navigation maps do not contain detailed and accurate enough information regarding lanes, 2) current consumer-grade sensors, specifically cameras and GPS, do not meet the reliability requirements required for lane level navigation.
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2.2 Retrofit cooperative navigation prototype Existing navigation hardware (like the TomTom Bridge of Section 2.1) turns out to be not reliable enough to run more complex perception software to achieve a reliable lane positioning- and to run dynamic-lane-navigation functionality. Furthermore, it is expected that cars of the future will run the lane positioning software and the navigation software in different sub-systems, to separate the safety critical software in the car from the navigation software that is part of the infotainment domain.
To create a viable embedded platform that also distributes the two functions (i.e. perception+lane positioning and navigation+HMI) over different sub-systems, a hardware-setup was created that also separates these functions. In this setup the sub-systems can easily be retrofitted in existing cars for experiments by mounting the different sub-systems to the windscreen with sucking naps and use Wi-Fi for their connection. Details of the subsystems are:
• Navigation sub-system: An off-the-shelf tablet with a state-of-the-art CPU/ GPU combination that runs the dynamic-lane-navigation software and that connects to a lane positioning device via Wifi
• Perception/localization sub-system: A custom made hardware to run the positioning software that also acts as a communication gateway to the Cloud and V2X infrastructure. The hardware is based on a modular platform from a research partner that was developed for cooperative mobility trials. This platform is upgraded to integrate computing hardware with a state-of-the-art GPU capable (NVidia Tegra X1) of runnin perception/positioning software efficiently. Furthermore an interface was added to be able to connect to an Automotive grade camera with high dynamic range (>110 dB).
The current status is: • First prototype of the tablet running the dynamic-lane-navigation functionality is ready
and initial test drives are done on the Eindhoven test track that was created for INLANE. An initial version of the LDM (Local-Dynamic-Map) is implemented that is able to store dynamic objects received from the infrastructure in a lab environment. It is not yet operational in the field. The localization is still based on matching GPS positions to the HD-map and not yet on localization fusing different sensors.
• A prototype sample of the perception/localization sub-system is based on a platform created in another EU partner project and adapted for use in INLANE. This custom made hardware is ready and now being tested. In a next step, the lane positioning software engines will be ported to this platform.
Figure 3: Existing cooperative navigation prototype composed of sensor and computation modules (left) and display and HMI
module (right).
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2.3 In-‐car lane navigation prototype for automated driving The custom hardware for the cooperative navigation prototype (see Section 2.2) will most probably not suffice for localization in the context of automated driving. For that, fusion of different sensors (automotive grade camera-, lidar-, radar-, GPS-, and inertial sensors) will be necessary to create sufficient quality and redundancy to qualify for safety critical applications.
Figure 4: Set-up that will be available in the TUE-TASS autonomous driving research platform. The TomTom autonomous driving research platform will have a comparable set-up but will also include
hardware that is used to source map data for TomTom’s cartography products.
For lane navigation experiments (D3.3) and experiments with SLAM principles for the updates of HD-map layers (D3.5), test cars are being setup by the TUE (in cooperation with TASS) and TomTom. The TomTom car is an upgrade of a car that is also used to source data from which current and next-generation maps are produced, called Mobile Mapping (MoMa) vehicle. It is extended with an embedded platform that connects a variety of sensor configurations. This way, data that is recorded for map production and for experiments can be correlated from the same session with similar environmental conditions.
The current status is: that the hardware being is selected and the car is being built up.
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3. HD Maps (TomTom) Maps used in the INLANE project are based on NDS. NDS is a standardized format for automotive-grade navigation databases, jointly developed by automobile manufacturers and suppliers. The current NDS production maps are originally designed for road-level navigation systems. To make these maps suited for lane level navigation functionality, the NDS specification map is extended with extra attributes for precise localization and lane level navigation. The extra attributes are organized in NDS as extra layers stacked onto the maps currently used in production maps (SD maps), see also figure below. The NDS specification for the new attribute layers is not yet released and comes too late for INLANE. To be able to do experiments TomTom has created a proprietary run-time file format anticipating on the upcoming NDS standard with an access library that can be used by the lane navigation functionality (see Section 3.1 for more detail). In a later stage the proprietary run-time files can be exchanged by map files compiled according to the upcoming standard. In a first step, the 3D-lane attribute layer is added. The access library for the 3D-lane attributes layer is currently being tested in the Lane level navigation prototype.
New tooling is created to help the creation of the proprietary run-time map files in a semi-automated process. The HD map prototypes are created for the test track in Eindhoven. In a next step the access library will be extended with 3D attributes (RoadDNA) for accurate localization and will be made available for the main stream prototype development with the localization partners (See Section 3.2 for more detail). It is still under discussion whether the access library will be implemented as an RTMaps module or if RTMaps can use the library via UDP/TCP or similar.
Figure 5: TomTom HD maps layers (top) and coverage of TomTom HD maps in the Eindhoven test area
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(bottom).
3.1 3D-Lane Map layer The 3D-lane map layer is based on the digital-lane-model of NDS to describe the lane geometry. This geometry is used by the map matching-, lane matching-, lane guidance- and lane visualization functionality in the lane navigation component (see Section 5 for examples). Also special attributes are incorporated for 3D visualization purposes. Furthermore, the access layer accompanying the 3-D Lane Map layer allows retrieving the lane data as poly-lines.
3.2 Road-DNA layer For localization the HD map is enhanced with a 3D description of the environment called RoadDNA. It is derived from point clouds sourced with LIDAR sensors. RoadDNA data has been created for the test track in Eindhoven. Below a screenshot is shown of the RoadDNA map data rendered in a localization demonstrator integrating LIDAR, camera and GPS.
Figure 6: Visualization of TomTom RoadDNA.
RoadDNA is part of the TomTom map portfolio and provides a low storage/memory foot print 3D description of objects along road segments. The attributes provided by the RoadDNA map are
• The distance to the closest object as measured laterally to a line of reference, both to the left side as well as the right side of the measurement vehicle.
• The aforementioned reference line.
Examples of use of these attributes can be found in visualization applications where objects lateral to the ego vehicle are rendered to provide additional context. Another category of applications aims at determining the ego pose relative to an HD map. Positioning of an actor in an HD map can be implemented by aligning (correlating) distance measurements to those provided by the map. The outcome of this process is a map-matched pose (position and heading). In order to limit the storage footprint of the distance measurements, their discretized values are stored in images and represented by the pixel intensities. The horizontal pixel-to-pixel
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distance in these images corresponds to 50 cm of length parallel to the reference line. Vertically, a non-uniform sampling scheme is used with increasing sampling distance as height increases: e.g. the pixel-to-pixel distance corresponds to 1 cm in height at ground level and to approximately 50 cm at 5 m above the road surface. By storage of these images in a compressed format, the foot print can be limited to typically tens of kilobytes per kilometer.
Figure 7: Example of discretized lateral distance measurements stored as an image and part of the RoadDNA map. Driving direction is from left to right in the image, the top half of the image contains
measurement to the left of the vehicle, whereas the bottom half of the image represents measurement to the vehicle's right. Low intensity (darker) grey levels represent objects close by, with intensity increasing
as distance increases. Distinct features such as poles and trees can be clearly recognized.
Figure 8: A 3-D visualization of the RoadDNA segment of Figure 7.
More information on the usage of RoadDNA for positioning can be found in Section 4.2.
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4. Map-based positioning Improved vehicle localization accuracy is required for lane-level accurate positioning and navigation. Besides improvements in GNSS, an alternative is map-based positioning, i.e. matching or correlating current sensor observations with information in a map in order to determine the vehicle’s position in the map. In INLANE two approaches are developed: one using the 3D-Lane Map layer and one using the Road-DNA layer. Both are detailed below.
4.1 Lane matcher For the prototype of a navigation device with lane guidance functionality, a lane matcher was developed. In this prototype, a lane position is determined on the basis of standard GPS, standard map with lane attributes, and lane markings detected real-time from camera images, see Figure 9. The lane matcher is now being enhanced to achieve higher accuracy of the position by using the lane-map layer that is available in the HD map (instead of the standard map with lane attributes). This lane-matcher under development is also componentized for RTMaps to enable integration in the main stream prototype environment of INLANE. Alternatively, the lane detector of Vicomtech will be evaluated for this purpose.
Figure 9: Visualization of Lane detector results that are matched against the 3D Lane-map layer.
The current status is: that this new lane matcher is tested in the Retrofit cooperative navigation prototype (Section 2.2) that connects to more automotive grade components than the off-the-shelf prototype (Section 2.1), such as an ADAS camera, higher accuracy GPS receiver and HD map accurate maps.
4.2 RoadDNA correlator Software is developed to correlate LIDAR sensor information, i.e. dense 3D point clouds, with the RoadDNA map. This is prototyped outside of INLANE in an after-market device integrating a LIDAR and GPS hardware that can be mounted to the windscreen with a sucking nap. This device connects to a laptop running the correlation software that outputs a lane position. This demonstrator shows that it is possible to achieve 50 cm accurate positioning with this setup.
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Figure 10: Visualization of RoadDNA correlation of Stereo point clouds.
In a parallel research in inLance, a setup is created and tested that uses a stereo camera (instead of a LIDAR) to obtain the required 3D dense point clouds. A first functional prototype is running in a lab environment (see Figure 10), but no conclusions can yet be drawn from this development. The current status is: that the RoadDNA correlation software on basis of stereo vision will be fused with other localization components of INLANE partners
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5. HMI Different types of platforms are used in INLANE. To be able to accommodate developments and experiments on all platforms the HMI implementation is based on a QT platform that is derived from a commercial navigation application of TomTom. This platform is adapted to connect to the Lane Navigation engine developed for INLANE and to add featuring necessary to do specific experiments as defined in INLANE, such as the use of dynamic information in the navigation functionality. The HMI can be configured to present different views in the HMI. In the Navigation application views can be created for the 3D-lane rendering augmented with navigation information, a view for the RoadDNA rendering, a camera view and a view that is overlaid with the standard navigation HMI of TomTom for input of a destination address and other user control.
Figure 11: Overlay view with all icons (top row) active.
Above a screenshot is shown of the overlay of navigation information such as the traffic information, the speed limit information and guidance advices. This same platform is also used in other partner programs that research user-experience concepts for ADAS- and AD systems.
5.1 Lane renderer For the Lane navigation function, a renderer was built from scratch that is able to visualize the 3D-lane map geography augmented with navigation information such as the lane route, car position and dynamic objects in the LDM store. Below screenshots are shown of a 3D lane map presentation as rendered by the lane navigation engine augmented with speed limit signs in the LDM.
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Figure 12: Output of the 3D lane renderer.
5.2 Lane advice HMI For the lane navigation function, a lane planner is embedded that, based on the planned route, produces a Horizon of lanes that are part of the planned route. Below a screenshot is shown of a 3D lane map presentation augmented with the lane plan (top) and the advice to change lane (bottom).
Figure 13: 3D lane map presentation augmented with the lane plan (top) and the advice to change lane
(bottom)
The current status is: that the HMI overlay, the lane render, and the lane planner are ready to be used for the INLANE prototype.
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6. SDK for Interfacing to the Dynamic Lane Navigation Engine For INLANE a lane navigation SDK is built to be used by partners and third parties. It consists of an engine part, implementing the generic navigation functions and an application part, implementing the HMI. More information on the interfaces of the SDK is provided next.
6.1 Application interface The dynamic-lane-navigation engine is a component that uses the same interface to the application as the commercial navigation engine. This allows third parties to use the commercially available SDK of TomTom to apply the navigation engine. In a first step, this SDK is made available to the partners via the developer’s portal of TomTom to build experience with the interfaces. In a next step, the lane navigation engine will be made available via this portal enabling INLANE partners to build their own lane navigation experiments.
6.2 Positioning interface Extension of the lane navigation engine with a standard RTMaps interface to connect to lane positioning engines of partners is currently under development. First application is of this interface is planned as part of the cooperative navigation prototype as described in the previous chapter.
6.3 Local dynamic map interface A framework is developed to communicate dynamic data perceived in the vicinity of the car by sensors or via IoT connectivity in real time to the lane-navigation engine. This SDK is called the Location-Based-Dynamic-Object framework. Within the navigation engine it connects to an LDM store (local dynamic map). The framework used in INLANE is based on a framework that originally was developed for cooperative mobility trials and adapted to connect to the LDM store within the dynamic –lane-navigation engine.
A first prototype is available for in-lab an experiment that connects to a prototype service delivering real-time dynamic information that is shown on Matrix boards (lane blocks, variable speeds). This data is channeled via the Location Based Dynamic Object (LBDO) framework to the LDM store that visualizes the dynamic data in the map visualization. In a next step this framework is extended with a connection to V2X connectivity and to perception components.
Figure 14: Illustration of LDM layers and the IoT connectivity device.
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The current status is: that an automated build-environment is created for the lane navigation engine with support for mainstream intel-based prototyping platforms (PCs etc.) and for the Retrofit cooperative platform (Section 2.2). For those platforms, the SDK is made available for INLANE experiments. In a next step the environment will be extended with support for the In-car platform (Section 2.3) suited for AD experiments.
7. Conclusion The key lesson learned during the first year of the project is that current and next-generation consumer-grade mobile (personal navigation) devices are not reliable enough (in terms of computation and sensing capabilities) to deliver a viable Lane navigation product experience. Therefore, a shift to more reliable after-market multi-functional devices or in-car systems is required. The concept, as proposed in INLANE, of an after-market/in-car sub-system with an HMI sub-system running on a personal mobile device, is still viable. Regardless of the platform, reliable lane accurate positioning and lane accurate maps is still under development. The fusion, as addressed in INLANE, of multiple localization sources is paramount. Promising techniques are those who directly localize the vehicle in a map using real-time lane marking information or 3D point cloud data. Next generation maps will support this by having additional layers on top of the layers which are currently used for route planning. Envisioning the availability of a suitable platform and reliable lane accurate positioning and lane accurate maps, several HMI concepts are researched and presented.