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Deep learning for lateral vehicle control – an end-to-end trained multi-fusion steering model

  • Deep Learning based behavior reflex methods found their way into modern vehicles. To model the human driving behavior it is not sufficient to rely solely on individual, noncontiguous camera frames without taking vehicle signals or road specific features into account. In this work four temporal fusion methods are evaluated based on three different Deep Learning models. The proposed spatio-temporal Mixed Fusion model extends the present end-to-end models and consist of multiple levels of fusions. The raw image data from a single front facing camera is mixed with recorded vehicle data and a map based predicted road bank angle gradient vector. The model accesses multiple time axes: temporal features of multiple image frames are extracted through a combination of Convolution and LSTM layers while it can also make assumptions about the future road condition with the use of upcoming Ground Truth road bank angle changes. Experiments are performed on a recorded data set of real world drivings. Results show, that this approach leads to anDeep Learning based behavior reflex methods found their way into modern vehicles. To model the human driving behavior it is not sufficient to rely solely on individual, noncontiguous camera frames without taking vehicle signals or road specific features into account. In this work four temporal fusion methods are evaluated based on three different Deep Learning models. The proposed spatio-temporal Mixed Fusion model extends the present end-to-end models and consist of multiple levels of fusions. The raw image data from a single front facing camera is mixed with recorded vehicle data and a map based predicted road bank angle gradient vector. The model accesses multiple time axes: temporal features of multiple image frames are extracted through a combination of Convolution and LSTM layers while it can also make assumptions about the future road condition with the use of upcoming Ground Truth road bank angle changes. Experiments are performed on a recorded data set of real world drivings. Results show, that this approach leads to an accurate imitation of the human driver with an inference capability of more than 60 FPS.show moreshow less

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Metadaten
Author:Johann Haselberger, Jian Chen, Bernhard SchickORCiD
DOI:https://doi.org/10.1007/978-3-658-26435-2_17
Identifier:978-3-658-26435-2 OPAC HS OPAC extern
Parent Title (English):10th International Munich Chassis Symposium 2019
Publisher:Springer
Place of publication:Wiesbaden
Document Type:conference proceeding (article)
Conference:International Munich Chassis Symposium, 10th, 2019, Munich
Language:English
Year of first Publication:2019
Tag:MLPaSSAD
Automated Driving; KI; automated lateral control; deep learning; vehicle dynamics
Number of pages:27 Seiten
First Page:201
Last Page:227
Institutes:IFM - Institut für Fahrerassistenz und vernetzte Mobilität
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften
Research focus:FSP2: Mobilität
Publication Lists:Schick, Bernhard
Haselberger, Johann
Research Project:MLPaSSAD – New Multi-Layer Platforms for Security and Safety-Relevant Automated Driving Functions
Publication reviewed:begutachtet
Release Date:2020/07/10
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