مؤتمر
Machine learning for VRUs accidents prediction using V2X data
| العنوان: | Machine learning for VRUs accidents prediction using V2X data |
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| المؤلفون: | Ribeiro, Bruno Daniel Mestre Viana, Nicolau, Maria João, Santos, Alexandre |
| بيانات النشر: | ACM Press |
| سنة النشر: | 2023 |
| المجموعة: | Universidade of Minho: RepositóriUM |
| مصطلحات موضوعية: | accidents prediction, machine learning, vehicular communications, VRUs, Ciências Naturais::Ciências da Computação e da Informação |
| الوصف: | Intelligent Transportation Systems (ITS) are systems that consist on an complex set of technologies that are applied to road agents, aiming to provide a more efficient and safe usage of the roads. The aspect of safety is particularly important for Vulnerable Road Users (VRUs), which are entities for whose implementation of automatic safety solutions is challenging for their agility and hard to anticipate behavior. However, the usage of ML techniques on Vehicle to Anything (V2X) data has the potential to implement such systems. This paper proposes a VRUs (motorcycles) accident prediction system by using Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (pairing SUMO and ns-3). Results show that the proposed system is able to predict 96% of the accidents on Scenario A (with a 4.53s Average Prediction Time and a 41% Correct Decision Percentage (CDP) - 78 False Positives (FP)) and 95% on Scenario B (with a 4.44s Average Prediction Time and a 43% CDP - 68 FP). ; This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/00319/2020. |
| نوع الوثيقة: | conference object |
| وصف الملف: | application/pdf |
| اللغة: | English |
| ردمك: | 978-1-4503-9517-5 1-4503-9517-1 |
| العلاقة: | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT; Ribeiro, B., Nicolau, M. J., & Santos, A. (2023, March 27). Machine Learning for VRUs accidents prediction using V2X data. Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing. ACM. http://doi.org/10.1145/3555776.3578263Test; https://hdl.handle.net/1822/87711Test |
| DOI: | 10.1145/3555776.3578263 |
| الإتاحة: | https://doi.org/10.1145/3555776.3578263Test https://hdl.handle.net/1822/87711Test |
| حقوق: | info:eu-repo/semantics/restrictedAccess |
| رقم الانضمام: | edsbas.D36A63A7 |
| قاعدة البيانات: | BASE |
| ردمك: | 9781450395175 1450395171 |
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| DOI: | 10.1145/3555776.3578263 |