Low Complexity Dynamic Obstacle Detection for Intelligent Road Infrastructure
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| Titel: | Low Complexity Dynamic Obstacle Detection for Intelligent Road Infrastructure |
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| Autoren: | Rakotovao, Tiana, Ménard, Paul, Bernier, Carolynn |
| Weitere Verfasser: | CEA, Contributeur MAP |
| Quelle: | 2024 IEEE SENSORS |
| Verlagsinformationen: | IEEE, 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | LIDAR, [MATH.MATH-PR] Mathematics [math]/Probability [math.PR], Dynamic obstacle detection, [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-ES] Computer Science [cs]/Embedded Systems, Intelligent Transportation Systems |
| Beschreibung: | We present a new lightweight algorithm for detecting the points related to dynamic obstacles within LIDAR point clouds. Thanks to its low complexity, the algorithm can be used either to enable near-sensor embedded functionalities or to enhance the capabilities of intelligent infrastructure in the C-ITS context. Experimental results on the real-world TUMTraf Intersection Dataset show that the proposed approach can run in real-time on an ARM Cortex A9 CPU while still reaching a detection precision of 69.1%, which is consistent with state of the art performance of deep neural network-based approaches The research leading to these results/this publication has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101069748—SELFY project. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor the granting authority can be held responsible for them will be added after the double-blind phase, as requested. |
| Publikationsart: | Article Conference object |
| Dateibeschreibung: | application/pdf |
| DOI: | 10.1109/sensors60989.2024.10784503 |
| DOI: | 10.5281/zenodo.14329053 |
| DOI: | 10.5281/zenodo.14329052 |
| Zugangs-URL: | https://cea.hal.science/cea-04799428v1 |
| Rights: | STM Policy #29 CC BY |
| Dokumentencode: | edsair.doi.dedup.....284f2928f30a55f5a7998f346195f52f |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | We present a new lightweight algorithm for detecting the points related to dynamic obstacles within LIDAR point clouds. Thanks to its low complexity, the algorithm can be used either to enable near-sensor embedded functionalities or to enhance the capabilities of intelligent infrastructure in the C-ITS context. Experimental results on the real-world TUMTraf Intersection Dataset show that the proposed approach can run in real-time on an ARM Cortex A9 CPU while still reaching a detection precision of 69.1%, which is consistent with state of the art performance of deep neural network-based approaches<br />The research leading to these results/this publication has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101069748—SELFY project. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor the granting authority can be held responsible for them will be added after the double-blind phase, as requested. |
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| DOI: | 10.1109/sensors60989.2024.10784503 |