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Machine Learning for VRUs accidents prediction using V2X data

Published: 07 June 2023 Publication History

Abstract

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).

References

[1]
ABI Research. 2022. Vehicle-to-Everything (V2X). https://www.abiresearch.com/market-research/product/7779722-vehicle-to-everything-v2x/. [Online; accessed 20-Sep-2022].
[2]
Abir Mchergui et al. 2021. Survey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs). Vehicular Communications (2021), 100403.
[3]
Bruno Ribeiro et al. 2022. Leveraging Vehicular Communications in Automatic VRUs Accidents Detection. In 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 326--331.
[4]
ETSI. 2019. ETSI TR 103 300-1 V2.1.1 Intelligent Transport System (ITS); Vulnerable Road Users (VRU) awareness; Part 1: Use Cases definition; Release 2.
[5]
European Commision. 2021. ITS & Vulnerable Road Users. https://ec.europa.eu/transport/themes/its/road/action_plan/its_and_vulnerable_road_users_en [Online; accessed October-2021].

Cited By

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  • (2025)VRDeepSafety: A Scalable VR Simulation Platform with V2X Communication for Enhanced Accident Prediction in Autonomous VehiclesWorld Electric Vehicle Journal10.3390/wevj1602008216:2(82)Online publication date: 6-Feb-2025

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cover image ACM Conferences
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
March 2023
1932 pages
ISBN:9781450395175
DOI:10.1145/3555776
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2023

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Author Tags

  1. vehicular communications
  2. VRUs
  3. accidents prediction
  4. machine learning

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SAC '23
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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

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View all
  • (2025)VRDeepSafety: A Scalable VR Simulation Platform with V2X Communication for Enhanced Accident Prediction in Autonomous VehiclesWorld Electric Vehicle Journal10.3390/wevj1602008216:2(82)Online publication date: 6-Feb-2025

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