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5 results for “Vehicle to Vehicle Communications”
A Dataset for Exploring Wi-Fi Network Diversity in Vehicle-to-Infrastructure Communication
<p><strong>Introduction:</strong></p> <p>This dataset contains space and time-indexed performance data for Wi-Fi communication between a moving vehicle and a set of stationary Access Points (APs). In order to allow comparisons between technologies, 3 different types of Wi-Fi are used in parallel: 800.11n, ac, and ad.</p> <p>For more information, please consult the following article: <a href="https://www.cs.vassar.edu/~rpachecomeireles/research/papers/vnc-2020.pdf"><em>Exploring Wi-Fi Network Diversity for Vehicle-to-Infrastructure Communication</em></a>, Rui Meireles, António Rodrigues, Andrei Stanciu, Ana Aguiar, Peter Steenkiste, in the 2020 IEEE Vehicular Networking Conference (VNC 2020), December 2020, <a href="https://doi.org/10.1109/VNC51378.2020.9318407">doi:10.1109/VNC51378.2020.9318407</a>. Video presentation available <a href="https://youtu.be/IREMIGV4XLc">here</a>.</p> <p><strong>Experiment description:</strong></p> <ul> <li> <p>The AP was placed at the corner of a residential area intersection while the mobile client drove a circuit around it. The mobility pattern is shown in the animated file <code>vehicle-movement.gif</code>.</p> </li> <li> <p>The data is divided into traces, gathered on different dates, using different vehicles to support the AP on the roof, as shown below:</p> </li> </ul> <table> <tbody><tr> <th>trace nr</th> <th>date</th> <th>start time</th> <th>n(n)</th> <th>n(ac)</th> <th>n(ad)</th> <th>AP vehicle</th> <th>n(clients)</th> </tr> </tbody><tbody> <tr> <td>302</td> <td>2019-08-20</td> <td>10:28:45</td> <td>3262</td> <td>2787</td> <td>423</td> <td>2001 Honda Civic sedan</td> <td>1</td> </tr> <tr> <td>303</td> <td>2019-08-20</td> <td>11:26:23</td> <td>3374</td> <td>3027</td> <td>312</td> <td>-</td> <td>2</td> </tr> <tr> <td>304</td> <td>2019-08-20</td> <td>12:39:46</td> <td>1711</td> <td>216</td> <td>14</td> <td>-</td> <td>3 (n & ac) 2 (ad)</td> </tr> <tr> <td>401</td> <td>2019-08-22</td> <td>10:19:24</td> <td>1685</td> <td>1681</td> <td>545</td> <td>2003 Peugeot Partner</td> <td>1</td> </tr> <tr> <td>402</td> <td>2019-08-22</td> <td>10:48:26</td> <td>2859</td> <td>2827</td> <td>764</td> <td>-</td> <td>2</td> </tr> <tr> <td>403</td> <td>2019-08-22</td> <td>11:39:36</td> <td>135</td> <td>135</td> <td>116</td> <td>-</td> <td>2</td> </tr> <tr> <td>404</td> <td>2019-08-22</td> <td>11:42:50</td> <td>114</td> <td>114</td> <td>53</td> <td>-</td> <td>2</td> </tr> <tr> <td>405</td> <td>2019-08-22</td> <td>11:45:07</td> <td>2019</td> <td>2019</td> <td>507</td> <td>-</td> <td>2</td> </tr> </tbody> </table> <ul> <li><strong>APs:</strong> all positioned at coordinates {lat : 41.111879, lon : -8.631146}</li> </ul> <table> <tbody><tr> <th>ap</th> <th>device</th> <th>802.11 type</th> <th>channel</th> <th>cntr. freq (MHz)</th> <th>bw (MHz)</th> </tr> </tbody><tbody> <tr> <td>unifi-003</td> <td>ubiquiti ac lite</td> <td>n</td> <td>6</td> <td>2437</td> <td>20</td> </tr> <tr> <td>unifi-001</td> <td>-</td> <td>ac</td> <td>40</td> <td>5200</td> <td>40</td> </tr> <tr> <td>tp-01</td> <td>tp-link talon ad7200*</td> <td>ad</td> <td>1</td> <td>60480</td> <td>2160</td> </tr> </tbody> </table> <p>*running tp-link's original firmware, not OpenWrt</p> <ul> <li><strong>Main clients:</strong> all positioned in the moving vehicle's roof, a vw golf mk3</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link archer t4uh</td> <td>2</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-03)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <ul> <li><strong>Background clients:</strong> the purpose is to increase channel util.</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> <th>position</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w2</td> <td>fixed, ~2m away from AP</td> </tr> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w2</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-04)</td> <td>-</td> <td>macbook</td> <td>stopped vehicle's roof</td> </tr> </tbody> </table> <ul> <li><strong>Monitor nodes:</strong> all positioned in the moving vehicle's roof</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link talon ad7200 (tp-02)</td> <td>8</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-02)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <p>Dataset structure:</p> <p>All the packet captures are already digested and ready to use in the file <code>wifi-exp-log-summary.csv</code>. An explanation of the fields below:</p> <ul> <li><strong>systime</strong> : system time (1 Hz resolution) that this row refers to. All node clocks were synchronized through NTP.</li> <li><strong>traceNr</strong> : nr. of the trace the row belongs to.</li> <li><strong>lon</strong> : longitude (in degrees) reported by the receiver's GPS at <code>systime</code></li> <li><strong>lat</strong> : latitude reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverAlt</strong> : altitude (in meters) reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverX</strong> : x coordinate of the receiver's position when space is discretized as a Cartesian plane and the sender is set to be the origin of the coordinate system. The x axis corresponds to east-west (positive values are east, negative values are west). Unit is meters.</li> <li><strong>receiverY</strong> : y coordinate of the receiver's position when space is discretized as a Cartesian plane</li> <li><strong>receiverDist</strong> : distance (in meters) of receiver to ap(s)</li> <li><strong>receiverSpeed</strong> : speed (in m/s) reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverId</strong> : system-specific id for the client (in the vehicle)</li> <li><strong>senderId</strong> : system-specific id for the ap serving the client (side of the road)</li> <li><strong>isIperfOn</strong> : 1 if row's <code>systime</code> corresponds to a period where our UDP packet consumer application is known to have been running on the receiver side.</li> <li><strong>isInLap</strong> : 1 if this row's systime has been marked as being part of a time period where clients were doing laps around the APs, 0 otherwise.</li> <li><strong>rssiMean</strong> : the mean of the RSSI (Received Signal Strength Indicator) values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>snrMean</strong> : SNR (signal to noise ratio) retrieved from 802.11ad sectore sweep frames.</li> <li><strong>channelFreq</strong> : center frequency of the WiFi channel used, in MHz.</li> <li><strong>channelBw</strong> : bandwidth of the WiFi channel used, in MHz.</li> <li><strong>channelUtil</strong> : percentage of time the wireless medium was sensed to be busy during the 1-second period systime period the row refers to. <strong>In traces 40x, the 802.11n and ac routers didn't log channel busy time, and as such we had to approximate channel util. based on x,y coordinates and nr. of active clients.</strong></li> <li><strong>wifiType</strong> : 802.11 type (e.g., n, ac or ad).</li> <li><strong>nrClients</strong> : nr. of parallel clients operating in <code>wifiType</code> mode, on the same channel and bandwidth as <code>receiverId</code>.</li> <li><strong>dataRateMedian</strong> : the median of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>dataRateMean</strong> : the mean of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>nBytesReceived</strong> : total number of bytes received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>tghptConsumer</strong> : throughput reported by the UDP packet consumer application, during the 1-second period systime period the row refers to.</li> <li><strong>nRetries</strong> : nr. of WLAN-level re-transmissions on 1 second period</li> <li><strong>meanBeaconRssi</strong> : mean RSSI measured from beacons in 1 second period. nan values are filled with -100 dBm.</li> <li><strong>meanInterBeaconTime</strong> : mean interval between consecutive beacons, within 1 second period. nan values are filled with 1 sec.</li> <li><strong>nBeacons</strong> : total nr. of beacons received by client within 1 second period.</li> </ul>
Autonomous Vehicle Communication Strategies Modeled in Virtual Reality
<p>We sought to better understand how autonomous vehicle (AV) communication strategies impact human road users’ perceptions and behaviors. More specifically, we explored the impact of different external human-machine interface (eHMI) designs on understanding, task load, comfort, trust, acceptance, and reaction time. To accomplish this, we created virtual reality (VR) scenarios where human participants interacted with AVs. Participants experienced biking, driving, and pedestrian simulators and were brought back after initial testing to explore acclimation and learning effects. In terms of perceptions, the presence of an eHMI was the strongest predictor of understanding, comfort, trust, and acceptance outcomes in the statistical models when controlling for all other variables. There was a clear divide between text-based eHMIs and non-text eHMIs, with text-based eHMIs reporting better perception scores and the LED Windshield reporting the worst perception scores. There were perception acclimation effects detected (most notable for task load and comfort), but they had less of an impact than the presence of an eHMI. Perception outcomes had weaker relationships with participant characteristics than with AV characteristics. While behavioral outcomes should be interpreted with caution because of low participant sample sizes, behavioral results largely mirrored perception results in that significant reductions in reaction time were observed with the presence of an eHMI (3.69 second reduction), yielding (3.16 second reduction), and acclimation (0.134 second reduction per trial). Results suggest that eHMI design, AV behavior, and acclimation are most impactful in terms of both perceptions and reaction time.</p>
KR, Vehicle quality of service support, Tethering via vehicle using mmWave communication
<p>Use Case Category: <strong>Vehicle quality of service support</strong><br> User Story: <strong>Tethering via vehicle using mmWave communication</strong><br> Location: Korean (KR) trial site</p> <p>According to 3GPP TS 22.186 R16, Vehicle quality of service support “enables a V2X application to be timely notified of expected or estimated change of quality of service before actual change occurs and to enable the 3GPP System to modify the quality of service in line with V2X application’s quality of service needs. Based on the quality of service information, the V2X application can adapt behaviour to 3GPP System’s conditions. The benefits of this use case group are offerings of smoother user experience of service”.</p> <p>User Story: <strong>Tethering via vehicle using mmWave communication</strong></p> <p>Tethering via Vehicle use case enables in-vehicle UEs and pedestrian UEs to access the network with the help of a vehicle relay which is deployed at a vehicle. For in-vehicle UEs, through Tethering via Vehicle use case, it is possible to avoid high penetration loss occurring from the metallic vehicle surface, thereby achieving more reliable wireless connectivity as well as reduced UE power consumption. The in-vehicle UEs are also benefited from the minimized handover operations. Only the vehicle relays involve in the handover operations. For pedestrian UEs, tethering via Vehicle use case enables more reliable connectivity, increased throughput and reduced UE power consumption since it reduces the communication range of the pedestrian UEs.</p> <p>As a deployment scenario for Tethering via Vehicle use case, a vehicle relay can have the Internet connectivity to the network through a base station (BS) including macrocell BS, microcell BS, and BS-type road side unit (RSU). Another UE such as UE-type RSU can also provide the Internet connectivity to the network. Non-terrestrial links such as satellite BS, satellite relay, and high-altitude platform (HAP) can also be used to provide the Internet connectivity to the network.</p> <p>Tethering via Vehicle use case generally supports eMBB-type services such as web surfing, FTP, and video streaming. Hence, it intrinsically requires high data throughput up to several Gbps. In order to satisfy such very high throughput, large bandwidth is necessary which is quite difficult in lower frequency bands below 6 GHz. Therefore, mmWave frequency band should be employed to support such high throughput and to satisfy the Tethering via Vehicle use case.</p>
EVALUATION OF ADVANCED VEHICLE AND COMMUNICATION TECHNOLOGIES THROUGH TRAFFIC MICROSIMULATION
<p>This folder contains products developed from STRIDE I-5 project.</p> <p>This project builds on a previously funded STRIDE project (D4) where a simulation extension was built using the micro simulator VISSIM to accurately represent vehicle autonomy and connectivity, and their operational and environmental effects.</p> <p>In this project, the research team expanded the functionality of the simulation extension to:</p> <p>i. Model human driven vehicles in the presence of AVs. An aggressive merging behavior model was implemented in VISSIM to study potential queue-jumping behavior at a freeway on-ramp. The research team also considered implementing this model in the open-source simulator SUMO.</p> <p>ii. Consider advanced vehicle dynamics and enable users to customize driver, vehicle, operating environment, and operating mode separately.</p> <p>iii. Incorporate a real-time optimization tool (RIO) previously developed with funding from the National Science Foundation (NSF). RIO jointly optimizes vehicle trajectories and signal control by taking advantage of CAV technologies.</p> <p> </p> <p>In this project, the research team conducted the following educational activities:</p> <p>i. Developed and conducted a nation-wide survey to understand the needs of the CAV education.</p> <p>ii. Developed six instructional modules for CAV education.</p> <p>iii. Developed a training module to model CAVs in microsimulation environment.</p> <p> </p> <p>The products developed from this project are:</p> <p>i. An enhanced simulation extension that can be used as a “plug and play” solution to model CAVs and human driven vehicles in presence of CAVs with options to consider advanced vehicle dynamics and incorporate signal/trajectory optimization.</p> <p>ii. Educational modules on how CAVs impact planning, design, modeling, and analysis of transportation facilities along with a training module to model CAVs using VISSIM</p>
Vehicle Sensing and Communications using LED Headlights to Enhance the Performance of Intelligent Transportation Systems: Proof of Concept, Implementation, and Applications
<p>Corresponding data set for Tran-SET Project No. 18ITSOKS01. Abstract of the final report is stated below for reference:</p> <p>"This project investigates the use of vehicle light-emitting diode (LED) headlamp devices for improving the accuracy and reliability of traffic (sensing and communication) data measurements required for developing effective intelligent transportation systems (ITS) technologies and solutions.</p> <p>Vehicular communication and sensing technologies are mainly based on conventional radio frequency (RF) or laser technologies. These systems suffer from several issues such as RF interference and poor performance in scenarios where the incidence angle between the speed detector and the vehicle is rapidly varying. Introducing a new sensing technology will add diversity to these systems and enhance the reliability of the real-time data. In this project, we proposed and investigated a novel speed estimation sensing system named “Visible Light Detection and Ranging (ViLDAR)” (patent pending).</p> <p>ViLDAR utilizes visible light-sensing technology to measure the variation of the vehicle’s headlamp light intensity to estimate the vehicle speed. Similarly, visible light sensing technology is used for data communication purposes, where the vehicle headlamp is utilized for wireless data transmission purposes. This project outlines the ViLDAR system simulations, implementation including hardware and software components, experimental evaluation in both laboratory and outdoor environments. The experimental measurement settings of the ViLDAR experiments are detailed. Encouraging results for both sensing and communication scenarios are obtained. The outcome of this proof-of-concept study both in the laboratory and outdoor validates the merit of the proposed technology in speed estimation (sensing) and data communication. The outcomes of this project will inspire a wide and diverse range of researchers, scientists and practitioners from the ITS community to explore this new and exciting technology. This project built initial steps in exploring this new sensing and communication modality using vehicle headlamps, leaving open a wide field for exploration and novel research."</p>
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