The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot
Artikel i vetenskaplig tidskrift, 2020

As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles.

big data

Internet of things

reference architecture

IoT

data toolchain

automated vehicles

field operational tests

edge-to-cloud architectures

Författare

Johannes Hiller

RWTH Aachen University

Sami Koskinen

Teknologian Tutkimuskeskus (VTT)

Riccardo Berta

Università degli Studi di Genova

Nisrine Osman

Università degli Studi di Genova

Ben Nagy

Jaguar Land Rover

Francesco Bellotti

Università degli Studi di Genova

Ashfaqur Rahman

Jaguar Land Rover

Erik Svanberg

Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet, Olycksprevention

SAFER, Fordons- och trafiksäkerhetscentrum

Hendrik Weber

RWTH Aachen University

Eduardo H. Arnold

The University of Warwick

Mehrdad Dianati

The University of Warwick

Alessandro De Gloria

Università degli Studi di Genova

ELECTRONICS

2079-9292 (eISSN)

Vol. 9 5 809

L3Pilot - Piloting Automated Driving on European Roads

Europeiska kommissionen (EU) (723051), 2017-09-13 -- 2020-09-13.

Styrkeområden

Transport

Ämneskategorier

Programvaruteknik

Farkostteknik

Robotteknik och automation

DOI

10.3390/electronics9050809

Mer information

Senast uppdaterat

2021-09-07