دورية أكاديمية
Using machine learning on V2X communications data for VRU collision prediction
| العنوان: | Using machine learning on V2X communications data for VRU collision prediction |
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| المؤلفون: | Ribeiro, Bruno, Nicolau, Maria João, Santos, Alexandre |
| بيانات النشر: | Multidisciplinary Digital Publishing Institute |
| سنة النشر: | 2023 |
| المجموعة: | Universidade of Minho: RepositóriUM |
| مصطلحات موضوعية: | Vehicular communications, Vulnerable road users, Collision prediction, Machine learning, Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática, Science & Technology |
| الوصف: | The datasets presented in this study are available in Zenodo at https://doi.org/10.5281/zenodo.7376770Test (accessed on 16 December 2022), reference number [23]. These datasets are the raw data used for the testing and training of the ML algorithms in this work. ; Intelligent Transportation Systems (ITSs) are systems that aim to provide innovative services for road users in order to improve traffic efficiency, mobility and safety. This aspect of safety is of utmost importance for Vulnerable Road Users (VRUs), as these users are typically more exposed to dangerous situations, and their vehicles also possess poorer safety mechanisms when in comparison to regular vehicles on the road. Implementing automatic safety solutions for VRU vehicles is challenging since they have high agility and it can be difficult to anticipate their behavior. However, if equipped with communication capabilities, the generated Vehicle-to-Anything (V2X) data can be leveraged by Machine Learning (ML) mechanisms in order to implement such automatic systems. This work proposes a VRU (motorcyclist) collision prediction system, utilizing stacked unidirectional Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (coupling the Simulation of Urban MObility (SUMO) and Network Simulator 3 (ns-3) tools). The proposed system performed well in two different scenarios: in Scenario A, it predicted 96% of the collisions, averaging 4.53 s for Average Prediction Time (s) (APT) and with a Correct Decision Percentage (CDP) of 41% and 78 False Positives (FPs); in Scenario B, it predicted 95% of the collisions, with a 4.44 s APT, while the CDP was 43% with 68 FPs. The results show the effectiveness of the approach: using ML methods on V2X data allowed the prediction of most of the simulated accidents. Nonetheless, the presence of a relatively high number of FPs does not allow for the usage of automatic safety features (e.g., emergency breaking in the passenger vehicles); thus, collision avoidance must ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1424-8220 |
| العلاقة: | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT; https://www.mdpi.com/1424-8220/23/3/1260Test; https://doi.org/10.5281/zenodo.7376770Test; Ribeiro, B.; Nicolau, M.J.; Santos, A. Using Machine Learning on V2X Communications Data for VRU Collision Prediction. Sensors 2023, 23, 1260. https://doi.orgTest/ 10.3390/s23031260; https://hdl.handle.net/1822/85377Test |
| DOI: | 10.3390/s23031260 |
| الإتاحة: | https://doi.org/10.3390/s23031260Test https://doi.org/10.5281/zenodo.7376770Test https://hdl.handle.net/1822/85377Test |
| حقوق: | info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: | edsbas.C1530143 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 14248220 |
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| DOI: | 10.3390/s23031260 |