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Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving.

Jos Elfring¹, Rein Appeldoorn², Sjoerd van den Dries³

¹Integrated Vehicle Safety department, Netherlands Organization for Applied Scientific Research TNO, Helmond 5700 AT, The Netherlands. jos.elfring@tno.nl.

Sensors (Basel, Switzerland)

|October 12, 2016

Summary

This summary is machine-generated.

A new methodology and software architecture simplify multisensor data fusion for automated vehicles. This approach efficiently integrates diverse sensors and advanced driver assistance systems, reducing development effort and enhancing adaptability.

Keywords:

automated driving data fusion multisensor world modeling

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Area of Science:

Automotive Engineering
Sensor Fusion
Robotics

Background:

Automated vehicles increasingly rely on numerous perception sensors for advanced driver assistance systems (ADAS).
System redundancy for fail-safe operation further escalates the number of sensors, leading to complex integration challenges.
Existing multisensor data fusion architectures lack flexibility due to vehicle, sensor, and application diversity.

Purpose of the Study:

To present a methodology for creating a consistent environmental model for automated vehicles.
To introduce a flexible software architecture to support the proposed methodology.
To minimize the effort needed to update multisensor data fusion systems with new sensors or applications.

Main Methods:

Development of a adaptable methodology for multisensor data fusion.
Design of a modular software architecture to complement the methodology.
Validation through real-world experiments with diverse sensors and algorithms.

Main Results:

Demonstrated the effectiveness of the proposed methodology in building consistent environmental models.
Showcased the software architecture's capability to streamline updates for new sensors and ADAS functions.
Validated the system's performance and adaptability in various real-world scenarios.

Conclusions:

The presented methodology and software architecture offer an efficient solution for multisensor data fusion in automated vehicles.
This approach significantly reduces integration complexity and enhances system maintainability.
The findings support the development of more robust and adaptable automated driving systems.