Machine Learning Insights for Behavioral Data Analysis Supporting the Autonomous Vehicles Scenario

Prezioso, Edoardo; Giampaolo, Fabio; Mazzocca, Carlo; Bujari, Armir; Mele, Valeria; Amato, Flora

doi:10.1109/jiot.2021.3118834

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…Machine learning models are employed to control the vehicle and facilitate interactions with other CAVs and infrastructure [ 3 ]. The models will retrieve data collected by on-board sensors to make informed decisions [ 4 ]. Potential on-board sensors include: radio detection and ranging (radar), sound navigation and ranging (sonar), light detection and ranging (LiDAR), camera, and visible light communication [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Virtual Traffic Light Implementation on a Roadside Unit over 802.11p Wireless Access in Vehicular Environments

Wong

White

Gill

et al. 2022

Sensors

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Blind intersections have high accident rates due to the poor visibility of oncoming traffic, high traffic speeds, and lack of infrastructure (e.g., stoplights). These intersections are more commonplace in rural areas, where traffic infrastructure is less developed. The Internet of Vehicles (IoV) aims to address such safety concerns through a network of connected and autonomous vehicles (CAVs) that intercommunicate. This paper proposes a Road-Side Unit-based Virtual Intersection Management (RSU-VIM) over 802.11p system consisting of a Field-Programmable Gate Array (FPGA) lightweight RSU that is solar power-based and tailored to rural areas. The RSU utilizes the proposed RSU-VIM algorithm adapted from existing virtual traffic light methodologies to communicate with vehicles over IEEE 802.11p and facilitate intersection traffic, minimizing visibility issues. The implementation of the proposed system has a simulated cloud delay of 0.0841 s and an overall system delay of 0.4067 s with 98.611% reliability.

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Section: Introductionmentioning

confidence: 99%