Path Planning for Highly Automated Driving on Embedded GPUs
The sector of autonomous driving gains more and more importance for the car makers. A key enabler of such systems is the planning of the path the vehicle should take, but it can be very computationally burdensome finding a good one. Here, new architectures in Electronic Control Units (ECUs) are requ...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Journal of Low Power Electronics and Applications |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2079-9268/8/4/35 |
| _version_ | 1828116850977800192 |
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| author | Jörg Fickenscher Sandra Schmidt Frank Hannig Mohamed Essayed Bouzouraa Jürgen Teich |
| author_facet | Jörg Fickenscher Sandra Schmidt Frank Hannig Mohamed Essayed Bouzouraa Jürgen Teich |
| author_sort | Jörg Fickenscher |
| collection | DOAJ |
| description | The sector of autonomous driving gains more and more importance for the car makers. A key enabler of such systems is the planning of the path the vehicle should take, but it can be very computationally burdensome finding a good one. Here, new architectures in Electronic Control Units (ECUs) are required, such as Graphics Processing Units (GPUs), because standard processors struggle to provide enough computing power. In this work, we present a novel parallelization of a path planning algorithm. We show how many paths can be reasonably planned under real-time requirements and how they can be rated. As an evaluation platform, an Nvidia Jetson board equipped with a Tegra K1 System-on-Chip (SoC) was used, whose GPU is also employed in the zFAS ECU of the AUDI AG. |
| first_indexed | 2024-04-11T13:04:46Z |
| format | Article |
| id | doaj.art-2d0a78e5cf2b44d5b400f14ef3169739 |
| institution | Directory Open Access Journal |
| issn | 2079-9268 |
| language | English |
| last_indexed | 2024-04-11T13:04:46Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Low Power Electronics and Applications |
| spelling | doaj.art-2d0a78e5cf2b44d5b400f14ef31697392022-12-22T04:22:50ZengMDPI AGJournal of Low Power Electronics and Applications2079-92682018-10-01843510.3390/jlpea8040035jlpea8040035Path Planning for Highly Automated Driving on Embedded GPUsJörg Fickenscher0Sandra Schmidt1Frank Hannig2Mohamed Essayed Bouzouraa3Jürgen Teich4Hardware/Software Co-Design, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91054 Erlangen, GermanyGIGATRONIK Ingolstadt GmbH, Ingolstadt, 85080 Gaimersheim, GermanyHardware/Software Co-Design, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91054 Erlangen, GermanyPre-/Concept Development Automated Driving, AUDI AG, 85045 Ingolstadt, GermanyHardware/Software Co-Design, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91054 Erlangen, GermanyThe sector of autonomous driving gains more and more importance for the car makers. A key enabler of such systems is the planning of the path the vehicle should take, but it can be very computationally burdensome finding a good one. Here, new architectures in Electronic Control Units (ECUs) are required, such as Graphics Processing Units (GPUs), because standard processors struggle to provide enough computing power. In this work, we present a novel parallelization of a path planning algorithm. We show how many paths can be reasonably planned under real-time requirements and how they can be rated. As an evaluation platform, an Nvidia Jetson board equipped with a Tegra K1 System-on-Chip (SoC) was used, whose GPU is also employed in the zFAS ECU of the AUDI AG.http://www.mdpi.com/2079-9268/8/4/35autonomous drivingpath planningembedded GPUsparallelization |
| spellingShingle | Jörg Fickenscher Sandra Schmidt Frank Hannig Mohamed Essayed Bouzouraa Jürgen Teich Path Planning for Highly Automated Driving on Embedded GPUs Journal of Low Power Electronics and Applications autonomous driving path planning embedded GPUs parallelization |
| title | Path Planning for Highly Automated Driving on Embedded GPUs |
| title_full | Path Planning for Highly Automated Driving on Embedded GPUs |
| title_fullStr | Path Planning for Highly Automated Driving on Embedded GPUs |
| title_full_unstemmed | Path Planning for Highly Automated Driving on Embedded GPUs |
| title_short | Path Planning for Highly Automated Driving on Embedded GPUs |
| title_sort | path planning for highly automated driving on embedded gpus |
| topic | autonomous driving path planning embedded GPUs parallelization |
| url | http://www.mdpi.com/2079-9268/8/4/35 |
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