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The BASELABS team has a long track record in research projects

Research

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.

A few examples of our projects

PRoPART - Precise and Robust Positioning for Automated Road Transports

Collaborative perception for self-driving trucks within the PRoPART project

Objective:

Highly accurate positioning solution for automotive use, taking advantage of the excellent characteristics of Galileo signals and combining them with other positioning and sensor technologies.

BASELABS Contributions:

  • Creation of an environment model with collaborative perception, processing the sensor data from onboard sensors and V2X data from the infrastructure sensors. The environment model was created by implementing both a dynamic object fusion and an occupancy grid.
  • Developing of a Situation Assessment Module, which decides whether a lane-change maneuver is possible and safe. The decision making combined the results made by two independent paths: a collision detector and a check of occupancy in the neighboring area of the truck.

For more information visit the project website.

Watch the official video.

fast traffic

Real-time wireless communication between vehicles and infrastructure

Objectives:

  • Networking with infrastructure → Foresighted driving
  • Networking of vehicles → Avoiding congestion
  • High-precision positioning

BASELABS Contributions:

  • Multiple object tracking with V2X data from RSU (Road side unit) and on-board lidar data
  • Sensor fusion developed with BASELABS Create Embedded
  • Collision detection of vulnerable road users and wrong-way drivers 3D reconstruction of local vehicle environment in order to leverage GNSS multipath detectionImplementation on an ECU developed in the project

For more information visit the project website.

Watch the video summary of the project contents and get to know the people involved in the project. The video is sponsored by BASELABS, the software partner for sensor fusion in the project.

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

Here you find an interview with our CTO Dr. Eric Richter and electriveNet about the project SADA. 

The interview took place at the “Hannover Messe” and is in German language. For non-German speakers we have a news article in English language.

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 framework for complex data fusion algorithms.  Besides the environmental perception with sensor data of an in-vehicle camera, a holistic environmental model was implemented that incorporates information of Vehicle-to-everything communication (V2X) and of a high-precision map-based satellite positioning system.
  • BASELABS contributes its broad experiences in the development of software applications for driving assistance systems.

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

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
  • The CoVeL consortium consists of six partners with outstanding expertise and a wide experience in the field of satellite navigation, communications, and traffic management. The consortium covers the complete value chain and is supported by a traffic management authority and a university.
    The CoVeL consortium consists of six partners with outstanding expertise and a wide experience in the field of satellite navigation, communications, and traffic management. The consortium covers the complete value chain and is supported by a traffic management authority and a university.
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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

Contact & further information

  • BASELABS Create Embedded FAQ

    This page answers typical questions about sensor fusion technology, integration, and safety aspects of the sensor fusion library BASELABS Create Embedded.

    FAQ

  • Contact

    Get in touch with Eric Richter, Director Innovation Technology of BASELABS.

    Contact us

  • Sensor Fusion Hub

    Gain valuable insights into Sensor Fusion - From Object Fusion to Dynamic Grids. 

    Learn more
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