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Dataset results
11 results for “Connected Vehicle”
Livorno, Urban driving, Connected vehicle detects fallen bicyle
<p><strong>Scenario description</strong>:</p> <p>Test session for fallen bicycle with connected but not automated vehicle</p> <p><strong>Session description</strong>:</p> <p>The fallen bicycle use case aims to demonstrate the possibility for a vehicle to detect in advance, using V2X communication, the presence of a fallen bicycle on the road. In case of fall, the bicycle signals its presence to the other vehicles using DENM messages.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
A Comprehensive Solution for Securing Connected and Autonomous Vehicles (presentation video)
<p>Video recording of the online presentation for the publication M. Kamal et al., "A Comprehensive Solution for Securing Connected and Autonomous Vehicles," 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2022, pp. 790-795, doi: 10.23919/DATE54114.2022.9774594.</p>
Identity Based Proxy Re-encryption Source Code for Security and Privacy in Connected Vehicle
<p>This is a source code for identity based proxy re-encryption using special string attribute for connected vehicle and Privacy, designed in python using Charm Cryptographic library using Pairing Group ss512 and 1024 bits.</p>
Project O2 - A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in Mixed Traffic
<p>The dataset includes Python code and VISSIM file for the research paper "A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in Mixed Traffic".</p> <p> </p> <p> </p>
Impact on energy and air quality of connected and autonomous vehicles in an urban context
<p>Despite numerous studies related to autonomous vehicles and connected vehicles (CAVs) and their impact on the economy or on traffic performance (eg, flow management, accidents), there are not many studies that relate these benefits to the environmental component. In this context, the objective of this work consisted in the integrated assessment of the impacts of CAVs on traffic performance, atmospheric emissions CO<sub>2</sub> and NO<sub>x, </sub>and air quality.</p> <p>To this end, a roundabout in the city of Aveiro was selected as a case study, and different scenarios were created: base scenario, considering the current typology of vehicles (conventional); scenario 2, considering defensive behavior CAVs; scenario 3, considering assertive behavior CAVs; and scenario 1, considering all types of vehicles mentioned above. To ensure a comprehensive analysis, all scenarios were evaluated for a period of 24 hours, corresponding to the period of the experimental campaign carried out, and a cascade of models was applied.</p> <p>First, the PTV VISSIM model was applied which allowed, configuring, calibrating and validating the network under study for an evaluation of the traffic performance. Second, the VSP model was applied to estimate atmospheric emissions, Finally, the CFD VADIS model was applied to air quality assessment.</p> <p>The results obtained allowed us to conclude that the introduction of CAVs, promotes longer travel times, especially during times of higher traffic, and an increase in emissions, mainly by the CAVs with defensive behavior. In terms of air quality, there were large differences in terms of NO<sub>2</sub> concentrations, with the CAVs promoting a degradation of air quality, especially during peak traffic hours.</p>
Examining drivers' behaviors to connected and automated vehicles
<p>It is envisioned that Connected and Automated Vehicles (CAVs) are the future of transportation as they can assist in minimizing some inefficiencies with the current transport systems. However, it is not clear how drivers of conventional vehicles would interact with CAVs in a mixed traffic environment containing both CAVs and human driven vehicles (HDVs). Thus, this study aims to investigate drivers’ behaviors towards CAVs through driving simulation experiment and national survey study. Two on-ramp and two off-ramp driving simulation scenarios were designed where drivers were asked to merge with two-lane highway in presence of HDVs and CAVs truck platoon in the on-ramp scenarios. In the two off-ramp scenarios, they were asked to take exit in presence of HDVs and CAVs truck platoon in the lane to their right. A before-after survey was conducted among the participant of the driving simulation experiment and an online survey was conducted to investigate their opinion in different traffic, road and environmental condition in presence of CAVs. Furthermore, two driving simulation scenarios were designed to test drivers’ behaviors during automated driving mode and their reaction when the control was shifted to the manual driving mode. Results from the on-ramp scenarios and off-ramp scenarios indicate that more than half of the drivers preferred to merge in front of CAV truck platoon and around two-third of the drivers chose to diverge behind the platoon. The online survey revealed that around two-third of the respondents would not overtake CAV platoon in two-lane two-way road, whereas around 60% would do so in case of three lane highway. During automation failure, drivers demonstrated lower take-over reaction time (TORt), lower deceleration and higher TTC in scenarios with non-driving related tasks (NDRT) compared to the scenarios with no NDRT (manual mode). The before-after survey results suggest that most of the drivers found the navigation with CAV easier after participating in the experiment.</p>
Emissions impact of connected and automated vehicle deployment in California - model results
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Livorno, Urban driving, Automated and connected vehicle and smart traffic light
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light.</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light sends SPaT and MAP messages describing the topology, actual status of the traffic light to other connected vehicles and to the oneM2M platform on the cloud.<br> An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, considering also other vehicles moving in front. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Drining in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
STRIDE Project C - Performance Measurement and Management Using Connected and Automated Vehicle Data
<p>The main objective of this study is to develop a methodological framework to estimate system performance measurements using CV data. The study also provides a validation of the framework as a proof of concept by determining performance measurements from traditional and CV data. In doing so, the microscopic simulation software VISSIM with trajectory conversion algorithm (TCA) is used to generate CV data, particularly basic safety message (BSM) for a study corridor located in Birmingham, AL. The estimated performance measures can be used by a system operator, planner, or an automated system to support decisions associated with these processes. The measurements can be also used to derive information for dissemination to travelers, third-party data aggregators, traveler information service providers, and other agencies. The collected and archived data includes real-world data collected from different sources in addition to simulation model results.</p>
Connected Emission Control Technologies for Freight Vehicles
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