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7 results for “V2X”
Comparing V2X and RADAR safety performance in NLOS scenarios
<p><strong>Scenario 1: </strong>Highway car following in road curve </p> <p>This scenario simulates a highway environment where two vehicles (HV and RV) communicate via V2X and HV is also equipped with radar sensor, while navigating a curved road. The leading remote vehicle (RV) is moving with constant speed and it is intially out of range of HV's radar sensor.</p> <p>Safety metrics such as Time-to-Collision (TTC) are evaluated to analyze the system's performance under the influence of NLOS situations and road curvature. <br><em>Dataset file: <code>Highway_road_curve_scenario.csv</code></em><br><br><strong>Scenario 2: </strong>Intersection scenario <br><br>This scenario involves two vehicles crossing each other paths and communicating via V2X at an intersection. Radar and V2X data are used to calculate safety indicators such as Time-to-Intersection (TTI), assessing the effectiveness of cooperative communication in mitigating collision risks. <br><em>Dataset file: <code>Intersection_scenario.csv</code></em></p>
Safety impact of DoS attacks on V2X-based collision warning
<p>The dataset represents Straight Crossing Path (SCP) intersection scenarios, where a Host Vehicle (HV) and a Remote Vehicle (RV) approach a right-angled intersection at different velocities and cross each other's paths simultaneously. By manipulating the starting positions, the driving scenarios were defined in such a way that the two vehicles collide in all cases.</p> <p>Scenarios were implemented with the following speed levels:</p> <table> <tbody> <tr> <td> <p><strong>Scenario</strong></p> </td> <td> <p><strong>RV speed [km/h]</strong></p> </td> <td> <p><strong>HV speed [km/h]</strong></p> </td> </tr> <tr> <td> <p>S1</p> </td> <td> <p>20</p> </td> <td> <p>40</p> </td> </tr> <tr> <td> <p>S2</p> </td> <td> <p>50</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>S3</p> </td> <td> <p>20</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>S4</p> </td> <td> <p>50</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>S5</p> </td> <td> <p>20</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>S6</p> </td> <td> <p>50</p> </td> <td> <p>130</p> </td> </tr> </tbody> </table> <p> </p> <p>We quantified the <a href="https://www.sciencedirect.com/science/article/pii/S2214209622000614" target="_blank" rel="noopener"><strong>safety risk (Safety Risk Index - SRI)</strong> </a>related to the specific V2X scenarios based on network performance metrics (End-to-End latency – E2E; Packet Delivery Ratio – PDR).</p> <p>In our dataset, we differentiated the strength of the attack based on the primary wireless communication parameters:</p> <p>· the attacker's data transmission rate (AR),</p> <p>· the attack packet length (APL).</p> <p>Based on the six driving scenarios (S1-S6) and the attack parameters (attack packet length, attack rate), 780 scenarios were simulated for a total of 15,600 unique test points (20 static spatial measurement point / scenario).</p>
DATASET: IQ SAMPLES OF LTE, 5G NR, WI-FI, ITS-G5, AND C-V2X PC5
<div> <div> <div> <h1>Dataset: IQ samples of LTE, 5G NR, WiFi, ITS-G5, and C-V2X PC5</h1> <p>The dataset comprises IQ samples captured from ITSG-5, C-V2X PC5, WiFi, LTE, 5G NR and Noise. In each dataset cluster, 7500 examples are collected from each considered technology. The dataset collected was used for technology recognition and traffic charchterization model. More details on the structure and performance of the model can be found <a href="https://www.sciencedirect.com/science/article/abs/pii/S2214209622001103">here</a>.</p> <p> </p> </div> </div> </div> <div> <div> <div> <h1>Description of Dataset Collection</h1> <h2>LTE</h2> <p>For LTE dataset collection, <a href="https://www.srslte.com/" target="_blank" rel="nofollow noopener">srsRAN</a>, an open-source SDR platform, is used. This LTE SDR implementation is used to collect samples for the LTE dataset. The indoor testbed setup used to collect our dataset consists of one eNB host PC and one UE host PC. Each host PC is connected to a USRP X310 board, which is used as the RF front end. We use the latest srsRAN version 21.04, which is installed on each host PC. For the LTE dataset collection, the FDD mode with a 10 MHz bandwidth and a 5.9 GHz center frequency is used for the down-link traffic. For the LTE dataset, the traffic load was varied between 5 and 50 Mbps, and the MCS used was varied by manually configuring from MCS index 1 to 28 .</p> <h2>5G NR</h2> <p>The <a href="https://openairinterface.org/" target="_blank" rel="nofollow noopener">OpenAirInterface</a> SDR solution is used for 5G NR dataset collection. OpenAirInterface is an open source SDR platform that provides a 3GPP compliant implementation of eNB, UE, and EPC. The OpenAirInterface SDR solution also includes a 5G non-stand alone (NSA) mode which supports 5G networks by using existing 4G infrastructure. This SDR-based 5G network setup is used to collect the IQ samples for the 5G NR dataset. For the 5G NR dataset collection, a 1:1 static TDD configuration is used for up-link and down-link traffic in NSA mode. Numerology 1 is used at 10 MHz bandwidth and center frequency of 5.9 GHz. For the 5G NR dataset, the MCS used was varied by manually configuring different values ranging from MCS index 1 to 28, and the traffic load was varied between 5 and 50 Mbps.</p> <h2>Wi-Fi</h2> <p>For the WiFi dataset collection, the <a href="https://github.com/open-sdr/openwifi" target="_blank" rel="nofollow noopener">openwifi</a> SDR solution is used. openwifi is an open-source full-stack IEEE 802.11 a/g/n SDR implementation based on the Xilinx Zynq System on Chip that includes a Field Programmable Gate Array (FPGA) and an ARM processor. For our dataset, we used an IEEE802.11n access point and a client connected to it. WiFi traffic generated cover a wide range of traffic loads, i.e., 10–200 packets per second (pps), with packet sizes ranging from 500 to 1500 bytes. The MCS used was varied by manually configuring the MCS index value between 0 and 7.</p> <h2>ITS-G5 and C-V2X PC5</h2> <p>The <a href="https://www.marquez-barja.com/images/papers/vehicuularcomms21-openaccess-version.pdf" target="_blank" rel="nofollow noopener">CAMINO</a> framework was used for the ITS-G5 and C-V2X PC5 dataset collection. CAMINO is a core framework for managing the V2X communication technologies, including ITS-G5, C-V2X PC5 and C-V2X Uu (5G/4G). The CAMINO framework is used to dynamically generate standardized C-ITS service packets, including Cooperative Awareness (CA), Decentralized Environmental Notification (DEN), and Infrastructure to Vehicle Information (IVI) message packets. The CAMINO software is implemented on the infrastructure deployed as part of the <a href="https://ieeexplore.ieee.org/document/9621229" target="_blank" rel="nofollow noopener">Belgian Smart Highway testbed</a>. The Smart Highway is a testbed deployed by IMEC on the E313 highway, near Antwerp, Flanders. The Smart Highway testbed consists of eight RSUs and 2 OBUs. Each RSU and OBU includes a general purpose CPU running the CAMINO software, and Cohda MK5 and MK6c modules, which are COTS ITS-G5 and C-V2X modules respectively. In our dataset collection, RSU4 is used as a transmitter, and a USRP N310 connected to RSU3 is used to capture and store the samples. To represent a wide range of traffic characteristics, different packet sizes of 300B for CA, 300B and 600B for DEN, and 600B for IVI packets were used at inter-packet intervals of 20, 50, 100, and 200 seconds. The MCS used were varied by manually changing the MCS index value in the configuration files of the Cohda devices. The MCS index of ITS-G5 varied from 0 to 7. For the C-V2X PC5, the MCS is varied between 0 and 20.</p> </div> </div> </div> <div> </div>
V2X measurement on M0 motorway with 4 vehicles
<p>V2X data collection under real world conditions.</p><p><strong>Applicability of this dataset</strong>:</p><ul><li>V2X traffic analysis (for research and application development purposes)</li><li>Comparison of unsecured and PKI-authenticated 802.11p-based V2X communication (ITS-G5)</li></ul><p><strong>Location</strong>: Hungary, Budapest + M0 motorway (ringroad around Budapest)</p><p><strong>Date</strong>: 19th May 2023</p><p><strong>Test participants</strong>:</p><ul><li><a href="https://www.bme.hu/?language=en">Budapest University of Technology and Economics</a><ul><li><a href="https://transportation.bme.hu/">Faculty of Transportation Engineering and Vehicle Engineering</a><ul><li><a href="https://www.auto.bme.hu/">Department of Automotive Technologies</a> - <a href="https://www.automateddrive.bme.hu/">BME Automated Drive Lab</a></li><li><a href="http://www.kjit.bme.hu/index.php/en/">Department of Control for Transportation and Vehicle Systems</a> - <a href="https://traffic.bme.hu/">BME Traffic Lab</a></li></ul></li><li><a href="https://www.vik.bme.hu/en/">Faculty of Electrical Engineering and Informatics</a><ul><li><a href="https://www.tmit.bme.hu/?language=en">Department Of Telecommunications and Media Informatics</a></li></ul></li></ul></li><li><a href="https://www.microsec.hu/en">Microsec Ltd. (PKI provider)</a></li></ul><p><strong>Communication hardware</strong>: 4x <a href="https://www.cohdawireless.com/solutions/hardware/mk5-obu/">Cohda Wireless MK5 OBUs</a> (2 vehicles with PKI-on, 2 vehicles with PKI-off) with default settings</p><p><strong>V2X stack</strong> : Cohda Wireless ETSI stack (Physical layer: ITS-G5)</p><p><strong>Security stack</strong>: <a href="https://www.microsec.hu/en/v2x-pki">Microsec V2X PKI</a></p><p><strong>Weather and road conditions</strong>: Clear and sunny weather, dry road</p><p><strong>The datasets includes information on</strong>:</p><ul><li>Decoded received and transmitted V2X messages (secured and not secured GeoNetworking packets)<ul><li><strong>Rx.json</strong> – includes all received V2X messages by that specific host vehicle (CAM, DENM) also including RSU messages (CAM, DENM, MAPEM, SPaTEM, IVIM)</li><li><strong>Tx.json</strong> – includes all transmitted V2X awareness messages by that specific host vehicle (CAM, DENM)</li></ul></li><li><strong>Protocol stack</strong> (encapsulated in messages)<ul><li>PHY layer (ITS-G5 parameters): channel number (CCH/SCH), MCS, Rx power, Rx noise</li><li>MAC layer: Ethernet header</li><li>Network layer: GeoNetworking header</li><li>Transport layer: Basic Transport Protocol (BTP)</li><li>Facilities layer: ETSI ITS (e.g. CAM, DENM etc.)</li></ul></li></ul><p><strong>Measurement sections</strong>:</p><ul><li>Section 1 (Budapest -> M0)</li><li>Section 2 (M0 motorway ring)</li><li>Section 3 (M0 -> Budapest)</li></ul>
V2X Security Threats for Cluser-based Evaluation
<p>These datasets are obtained from the datasets in OID. Those datasets were filtered in order to obtain only the data collected by the vehicles composing the clusters as shown in the paper Evaluation of VANET Datasets in context of an Intrusion Detection System published in 29th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2021).</p> <p>The data from the multiple maps were then grouped to compose the training dataset. The test dataset is similar to the one in the original map 7 datasets. Additionally, the datasets were converted into ARFF format used by weka. The conversion to CSV can easily be made by deleting the header.</p>
V2X Datasets - Accidents between Passenger Vehicles and Motorcycles
<p><strong>For more details, please refer to:</strong></p> <p><strong>Bruno Ribeiro, Maria João Nicolau, and Alexandre Santos. "Using machine learning on v2x communications data for vru collision prediction." <em>Sensors</em> 23.3 (2023): 1260.</strong></p> <p>A compilation of VANET datasets collected from simulations (VEINS, coupling SUMO and OMNeT++), containing accidents between passenger vehicles and motorcycles on an intersection (two different scenarios).</p> <p>The datasets consist on V2X messages with the following information: <br>NodeID, Sender Position X, Sender Position Y, Sender Position Z, Sender Speed, Sender Heading, Sender Acceleration, Sender Vehicle Length, Sender Vehicle Width, Timestamp, Sender Vehicle Type, Accident [Bool]</p>
V2X Security Threats
<p>A set of VANET datasets collected from simulations. These contain data from normal and attack messages. The datasets are divided depending on the attack: DoS and Fabrication attack.</p> <p>The DoS attack is then subdivided depending on the frequency of the attack.</p> <p>The fabrication attack is divided into attacks to several parameters, speed, acceleration, and heading.</p> <p>Further explanation can be found in F. Gonçalves, B. Ribeiro, O. Gama, J. Santos, A. Costa, B. Dias, M. J. Nicolau, J. Macedo, and A. Santos, “Synthesizing Datasets with Security Threats for Vehicular Ad-Hoc Networks,” in IEEE Globecom 2020: 2020 IEEE Global Communications Conference (GLOBECOM’2020), Taipei, Taiwan.</p>
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