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Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics

  • A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE)A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments.show moreshow less

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Metadaten
Author:Alberto Flores FernándezORCiD, Eduardo Sánchez MoralesORCiD, Michael BotschORCiD, Christian FacchiORCiD, Andrés García HigueraORCiD
Language:English
Document Type:Article
Year of first Publication:2022
published in (English):Sensors
Publisher:MDPI
Place of publication:Basel
ISSN:1424-8220
Volume:23
Issue:1
Pages:28
Article Number:159
Review:peer-review
Open Access:ja
Version:published
Tag:ADAS; Autonomous Driving; Inertial Navigation Systems; Machine Learning; On-Board Diagnostics; Transformer Neural Network
URN:urn:nbn:de:bvb:573-30464
Related Identifier:https://doi.org/10.3390/s23010159
Faculties / Institutes / Organizations:Fakultät Informatik
Fakultät Elektro- und Informationstechnik
CARISSMA Institute of Automated Driving (C-IAD)
CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2023/01/03