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109 results for “highways”
Brainport, Highway pilot, road side camera detection
<p><strong>Scenario description</strong>:</p> <p>The road side camera detects objects (obstacle) on the test track and publishes the ANO IoT message.</p> <p><strong>Session description</strong>:</p> <p>10 minute observation of a section of the Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Livorno, Highway pilot, only connected cars
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events:</p> <p>1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with only connected cars, lap of 12,3 km on the highway. The goal is to check all the systems and data management before the next test session with AD cars.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting 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_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, data management connected car
<p><strong>Scenario description</strong>: Dynamic speed adaptation due to puddle on the road</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway" at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events:</p> <p>1 The puddle monitoring system of the highway trigger a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of dangerous area, as notified by the «smart road», the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>pre-test session with only connected cars, lap of 12,3 km on the highway. Goal is to check all the system and data management.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting 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_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated car, two connected cars, smart highway
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events: </p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and two connected cars, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting 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_HighwayPilot_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 Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated and connected car and one connected car
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events:</p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and one connected car, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting 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_HighwayPilot_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 Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Fig. 8 in Gyrochorte "highways" and their environmental significance in shallow-marine sediments
Fig. 8. Gyrochorte "highways" documenting a sequorichnial behaviour of the producers from different localities. A, B. Agrio Formation, Cretaceous, Neuquén Basin, Argentina. A. Bed surface showing interference ripples cross-cut by Spongeliomorpha ichsp. (Sp). B. Ripple-trough fill reworked by Chondrites ichsp. (Ch) producers (black doted lines). C. Lajas Formation, Upper Jurassic, Neuquén Basin, Argentina; converging (black arrows), diverging white arrows), and overlapping Gyrochorte ichsp. (white doted line). D. Middle Jurassic, Helvetic realm, exact location not recorded, Switzerland (NMB O157) showing parallel Gyrochorte ichsp., 2 parallel traces (white doted line between them), 3 parallel traces (black doted line between them). Specimens A–C left in the field.
Fig. 6 in Gyrochorte "highways" and their environmental significance in shallow-marine sediments
Fig. 6. Trace fossils co-occurring with "burrowing highways" of Gyrochorte ichsp. A. Mutual cross-cutting of Gyrochorte ichsp. (1, 2) and Phycosiphon incertum Fischer-Ooster, 1856 (Ph) exhibiting typical elbow-shaped turns (white arrows); Gyrochorte comosa Heer, 1865 overprinting Phycosiphon ichsp. (1, white doted line; bilobate crest of Gyrochorte well visible); Phycosiphon ichsp. overprinted by G. comosa (2, white doted line; bilobate crest of Gyrochorte ichsp. not clearly developed while bioturbated); G. comosa showing transition to Gyrochorte variabilis Fürsich, Alberti, and Pandey, 2017 expressed by fan-like appearance (shift of causative tube marked by black arrow). B. Gyrochorte comosa associated with Ptychoplasma ichsp. (P); sequorichnial behaviour of several Gyrochorte ichsp. producers is evidenced by parallel and/or overlapping traces (parallel traces, white doted lines; diverging/converging traces, white arrows). C. Parallel Gyrochorte comosa showing sequorichial behaviour (white arrows) associated with bivalve trace Siphonichnus ichsp. (b) (surrounded by black doted line) and cross-section of Lockeia ichsp. (surrounded by white doted line); G. comosa showing transition to G. variabilis expressed by fan-like appearance (shift of causative tube marked by black arrow). All specimens left in the field.
Fig. 4 in Gyrochorte "highways" and their environmental significance in shallow-marine sediments
Fig. 4. Different ichnospecies of Gyrochorte encountered in the Mulichinco Formation, Lower Cretaceous, Puerto Curaco, Neuquén Province, Argentina field photographs). A. Two parallel Gyrochorte comosa Heer, 1865 (black arrows, lower left), one continuing as such (black arrow, upper right), and one diverging from parallel course and becoming Gyrochorte variabilis Fürsich, Alberti, and Pandey, 2017 (shift of causative tube indicated by white arrow). B. Gyrochorte variabilis exhibiting circular, slightly bilobate pads (black arrows). C. Gyrochorte variabilis composed of elongate pads (black arrow) becoming oblique with slight zigzag pattern (white arrow). For details about G. variabilis see Fürsich et al. (2017). All specimens left in the field.
Fig. 3. Trace fossil Gyrochorte comosa Heer, 1865 in Gyrochorte "highways" and their environmental significance in shallow-marine sediments
Fig. 3. Trace fossil Gyrochorte comosa Heer, 1865 on bed surfaces structured by wave ripples; Mulichinco Formation, Lower Cretaceous, Puerto Curaco, Neuquén Province, Argentina (field photographs). A. Gyrochorte comosa on bed surface exhibiting parallel-crested, symmetric ripples. B. Parallelcrested, slightly asymmetric ripples traversed by G. comosa being more frequent at the stoss-side and the crest of the ripples. C. Bed surface structured by interfering symmetric ripples documenting sequorichnial behaviour of G. comosa producers (black arrows, "converging" traces; white arrows, "diverging" traces); note acute angle between diverging or converging traces. D. Bed surface exhibiting parallel-crested, slightly asymmetric ripples; G. comosa is overtopped by sand at the lee side of the ripples (white arrows). All specimens left in the field.
Fig. 1 in Gyrochorte "highways" and their environmental significance in shallow-marine sediments
Fig. 1. Location map (based on Schwarz 2012), asterisk marks the study area within the Neuquén Basin (highlighted) while the inset refers to the location of the detailed map within South America.
Fig. 7 in Gyrochorte "highways" and their environmental significance in shallow-marine sediments
Fig. 7. "Burrowing highways" of Gyrochorte comosa Heer, 1865 in the Mulichinco Formation, Lower Cretaceous, Puerto Curaco, Neuquén Province, Argentina (field photographs). Crowded parallel and overlapping G. comosa concentrated at ripple crest (A–E). Note acute angle between sequorichnial Gyrochorte ichsp. (arrows, traces converging to and diverging from a parallel course; doted lines, parallel/overlapping traces). All specimens left in the field.
Dataset: Highway Holdings Limited (HIHO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Рис. 4. Карта-схема мест встреч пятнистого оΛеня в Нижнем Приамурье в 1979–2021 гг. КваΑраты — места фоторегистрации: 1 — верховья рр. Обор и Àурмин; 2, 3 — Анюйский национаΛьный парк; круги — места встреч по Λитературным и опросным Αанным: 1 — окрестности с. Кутузовка (место первой регистрации в 1979 г.); 2 — верховья р. СиΑима; 3 — устье р. Нижняя Буге; 4 — бассейн р. Мухен; 5–8 — Анюйский национаΛьный парк (соответственно, р. Пихца, урочище Сира, окрестности с. Арсеньево, устье р. СоΛоми); 9 — среΑнее течение р. СоΛоми; 10 — 76 км трассы ΔиΑога — Ванино; 11 — бассейн р. Кия; 12 — бассейн р. ХойΑур; 13 — бассейн р. Нюра Fig. 4. A schematic map of sika deer sightings in the Lower Amur Region in 1979-2021. Squares designate sites of photo recording: 1 — upper reaches of the rivers Obor and Durmin; 2, 3 — Anyui National Park; circles designate sightings sites according to the literature and the survey data: 1 — vicinity of the village Kutuzovka (the place of the first registration in 1979); 2 — upper reaches of the river Sidima; 3 — the mouth of the river Lower Buge; 4 — the Mukhen River basin; 5-8 —Anyui National Park (respectively, the Pikhtsa River, the Sira tract, the vicinity of the village Arsenyevo, the mouth of the Solomi River); 9 — the middle course of the Solomi River; 10 — 76 km of the Lidoga-Vanino Highway; 11 — the Kiya River basin; 12 — the Khoydur River basin; 13 — the Nyura River basin in New data on the distribution of sika deer Cervus nippon Temminck, 1838 in the Lower Amur Region
Рис. 4. Карта-схема мест встреч пятнистого оΛеня в Нижнем Приамурье в 1979–2021 гг. КваΑраты — места фоторегистрации: 1 — верховья рр. Обор и Àурмин; 2, 3 — Анюйский национаΛьный парк; круги — места встреч по Λитературным и опросным Αанным: 1 — окрестности с. Кутузовка (место первой регистрации в 1979 г.); 2 — верховья р. СиΑима; 3 — устье р. Нижняя Буге; 4 — бассейн р. Мухен; 5–8 — Анюйский национаΛьный парк (соответственно, р. Пихца, урочище Сира, окрестности с. Арсеньево, устье р. СоΛоми); 9 — среΑнее течение р. СоΛоми; 10 — 76 км трассы ΔиΑога — Ванино; 11 — бассейн р. Кия; 12 — бассейн р. ХойΑур; 13 — бассейн р. Нюра Fig. 4. A schematic map of sika deer sightings in the Lower Amur Region in 1979-2021. Squares designate sites of photo recording: 1 — upper reaches of the rivers Obor and Durmin; 2, 3 — Anyui National Park; circles designate sightings sites according to the literature and the survey data: 1 — vicinity of the village Kutuzovka (the place of the first registration in 1979); 2 — upper reaches of the river Sidima; 3 — the mouth of the river Lower Buge; 4 — the Mukhen River basin; 5-8 —Anyui National Park (respectively, the Pikhtsa River, the Sira tract, the vicinity of the village Arsenyevo, the mouth of the Solomi River); 9 — the middle course of the Solomi River; 10 — 76 km of the Lidoga-Vanino Highway; 11 — the Kiya River basin; 12 — the Khoydur River basin; 13 — the Nyura River basin
Figure 3 in Highways for red wood ants (Hymenoptera: Formicidae): a new method to increase the size of anthills
Figure 3. Changes in linear indicators of height and diameter of the nest mounds in the control and experimental groups of F. rufa (A) and F. polyctena (B) during the 2023 season.
Figure 1 in Highways for red wood ants (Hymenoptera: Formicidae): a new method to increase the size of anthills
Figure 1. Location of the studied RWA nest mounds. On the left is the Bilychi Forest (Formica polyctena), on the right the Holosiyivo Forest (Formica polyctena, F. rufa).
Figure 2. Red wood ant highways. A in Comparison of the movement speed of three ant (Hymenoptera: Formicidae) species along trails
Figure 2. Red wood ant highways. A) Formica rufa workers on a log and B) lined highway from the mound to the forage tree.
Fig. 2 in Impacts of a highway on the population genetic structure of a threatened freshwater turtle (Glyptemys insculpta)
Fig. 2. Estimate of short-term gene flow among populations north and south of Interstate Highway 88 (gray bar) and the Susquehanna River (dashed line) shown with 95% confidence intervals. Circle size reflects relative sample size. Values inside of circles represent the contribution of gene flow from within populations.
Fig. 1. Study area. Interstate Highway 88 in Impacts of a highway on the population genetic structure of a threatened freshwater turtle (Glyptemys insculpta)
Fig. 1. Study area. Interstate Highway 88 (I-88) and the Susquehanna River (Susq.) bisect Otsego and Delaware Counties, New York, USA.
How do King Cobras move across a major highway? Unintentional wildlife crossing structures may facilitate movement.
<p>Data and code for Jones et al. (2021): How do King Cobras move across a major highway? Unintentional wildlife crossings may facilitate movement.</p> <p>Including: telemetry data, shapefiles, and R scripts to reproduce analysis.</p>
Text-fig. 4. Palynological diagrams of Late Miocene deposits, North Caucasus. a. Tuapse highway bridge. b. Gaverdovsky. c. Volchaya Balka. in Late Miocene (Early Turolian) Vertebrate Faunas And Associated Biotic Record Of The Northern Caucasus: Geology, Taxonomy, Palaeoenvironment, Biochronology
Text-fig. 4. Palynological diagrams of Late Miocene deposits, North Caucasus. a. Tuapse highway bridge. b. Gaverdovsky. c. Volchaya Balka.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.