Research

The BASELABS team has a long track record in research projects

BASELABS as a member of different consortiums as well as individual BASELABS team members have a long track record of participating in cooperative research projects. The topics under research cover a broad range of spectrums. The experiences and development results gained in these research projects which push the limits of technology continuously forward influence the development of our software tools and project work. As a user of BASELABS software, you benefit from this broad expertise.

Current Projects

InDrive – Automotive EGNSS Receiver for High Integrity Applications on the Drive

Objectives:

  • Development and demonstration of innovative solutions for semi-automatic driving that heavily rely on accurate and high-integrity satellite navigation
  • Demonstrate the future use of mass-market GNSS, targeting automotive applications with high demands for integrity by creating a framework that specifies the requirements for data acquisition, signal tracking and data fusion
  • Download InDrive Flyer

BASELABS contributions:

  • The data fusion in the project is developed by using BASELABS Create, the data fusion software library and BASELABS Code, the source code generator for complex data fusion algorithms. Thus, a 360°-environment perception based on different sensor technologies will be implemented.
  • BASELABS contributes its broad experiences in the development of software applications for driving assistance systems
  •  

Visit the project website.

AutoNet2030 – Towards a new co-operative automated driving technology

Objectives:

  • Development and test of a co-operative automated driving technology
  • De-centralized decision making strategy by mutual information sharing among nearby vehicles

Main Results:

  • AutoNet2030 control: distributed decision making in automation
  • AutoNet2030 perception: integrating 360° multi-sensor data
  • AutoNet2030 communications: extending V2X messaging
  • AutoNet2030 HMI: a dual-display (distributed) approach

Find further information on pages 5 and 6 of the AutoNet2030 booklet.

BASELABS Contributions:

  • BASELABS Connect (now vADASdeveloper) is used by all partners to develop and integrate their custom software components (will include support for interoperability with other platforms such as ADTF, RTmaps and MATLAB/Simulink)
  • BASELABS Create is used to develop new data fusion algorithms for autonomous driving in cooperative environments
  • BASELABS Code brings the prototyping code developed by partners easily to the embedded environments and ECUs of the test vehicles and trucks
  • BASELABS staff implemented a data fusion system for plausibility checking of C2C communication messages (CAM) (more info in this paper)
  • Project time frame: 2013-2016

Visit the project website.

AutoNet2030 Consortium and Media

Consortium

Video

Loading the player ...

The fusion of data from cameras, GPS and Car-2-Car communication (CAM)for the implementation of the systems is done using BASELABS data fusion software. In the video, the recording of measurement data and the data fusion prototype including the tracking of occluded objects is shown.

Application

Final Event

SADA – Smart Adaptive Data Aggregation

Objectives:

  • Definition of a machine-interpretable description of sensors and data
  • Development of sensor-independent data fusion algorithms
  • Research on a plug-and-play-approach for runtime-configurable data fusion

BASELABS Contributions:

  • Provision of existing data fusion algorithm implementations
  • Development of a runtime-configurable data fusion software module for a parking application
  • Project time frame: 2015-2018

Visit the project website.

Closed Projects (Examples)

GAIN – GAlileo for Interactive driviNg

Objectives:

  • Increasing the localization performance for GNSS-based positioning systems
  • Development of a novel multipath mitigation algorithm for reliable vehicle positioning

BASELABS Contributions:

  • BASELABS Connect (now vADASdeveloper) was used at several prototyping vehicles for data recording which includes CAN bus, Low-cost GPS sensor, High-precision GPS/INS reference system and a Front camera
  • Tight interfacing with V2V and V2I
  • The recorded data was replayed for evaluation and testing purposes
  • BASELABS Create has been used to implement a sophisticated data fusion algorithm for accurate and reliable vehicle positioning with GNSS/INS and multipath mitigation in urban areas (more info in this paper)
  • BASELABS provided a Local Dynamic Map (LDM) component which smothery integrates into BASELABS Connect (now vADASdeveloper) as a sensor. This component is ready to be used with V2X (CAM, DENM) and support lane-level road polygons for advances ADAS applications
  • Project time frame: 2010-2012

Visit the project website.

CoVel – Cooperative Vehicle Localization for Efficient Urban Mobility

Objectives:

  • Development of Lane Navigation Assistants (LNA) with a focus on urban areas
  • Lane-level vehicle positioning by enhanced positioning and map matching algorithms

BASELABS Contributions:

  • BASELABS Connect (now vADASdeveloper) was used at several prototyping vehicles for data recording which includes
    - CAN bus
    - Low-cost GPS sensor
    - High-precision GPS/INS reference system
    - Front camera
    - Tight interfacing with V2V and V2I
  • The recorded data was replayed for evaluation and testing purposes
  • Comprehensive event logging including time stamping was implemented for decentralized components in order to support system validation and verification
  • BASELABS provided a basic Local Dynamic Map (LDM) component
  • Project time frame: 2010-2012

Visit the project website.

CoVel Consortium and Media

Consortium

Introduction Video

Loading the player ...

Overview on the CoVeL project

EGNOS usage

Loading the player ...

This video briefly introduces the usage of EGNOS/EDAS for the CoVeL project.

CoVel Use Case

Loading the player ...

This video briefly introduces CoVeL's main idea.

Top of page