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1,855 results for “Autonomous”
Brainport, Highway pilot, simulated autonomous mode
<p><strong>Scenario description</strong>:</p> <p>The driving adaptation car drives around the track in simulated autonomous mode (ACC).</p> <p><strong>Session description</strong>:</p> <p>18 laps with Jaguar F-Pace on 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>
Room Temperature Self-Healing in Soft Pneumatic Robotics: Autonomous Self-Healing in a Diels-Alder Polymer Network
<p>Healable soft robotic systems have been developed by constructing flexible membranes out of Diels?Alder (DA) polymer networks. In these components, relatively large amounts of damage, on the centimeter scale, can be healed, provided that the temperature is increased to 80?90 ?C. This article presents a new DA polymer network that can heal at room temperature through a smart design of the network that increases the molecular mobility in the material. This new material is used to develop the first healable soft robotic prototype that can autonomously recover from severe, realistic damage. The soft pneumatic hand can recover from various types of injuries, including being cut completely in half, without the need for a temperature increase. After healing, the performance of the soft robotic prototype is recovered.</p>
An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces: Video Results
<p>A video illustrating the results presented in the paper: <em>"Prédhumeau M., Mancheva L., Dugdale J., and Spalanzani A. 2021. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. In the Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). IFAAMAS, Online."</em></p> <p> </p>
Autonomic Provisioning and Application Mapping on Spot Cloud Resources
<p>1. Attached files: </p> <p>500_100_fmincon_0.95_1.mat <br /> Experiment with 500 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>500_100_heuristic_0.95_1.mat<br /> Experiment with 500 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_70_fmincon_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, exact algorithm.</p> <p>2000_70_heuristic_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.9_1.mat<br /> Experiment with 2000 users, 100ms max response time, 90% availability, exact algorithm.</p> <p>2000_100_heuristic_0.9_1.mat <br /> Experiment with 2000 users, 100ms max response time, 90% availability, our algorithm.</p> <p>2000_100_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>2000_100_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, exact algorithm.</p> <p>2000_100_heuristic_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, our algorithm.</p> <p>2000_300_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, exact algorithm.</p> <p>2000_300_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, our algorithm.</p> <p>10000_100_fmincon_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>10000_100_heuristic_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2. Data format:</p> <p>MATLAB data format, can be load from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Dataset from autonomous assets collected during the SO-CHIC field campaign in the Southern Ocean
<p>These datasets are a part of the Southern Ocean Carbon and Heat Impact on Climate (SO-CHIC, https://www.sochic-h2020.eu/) project, and were collected during the field campaign in 2022.<strong><br></strong></p> <p><strong>Seaglider SG640 </strong><em>(SG640_20220110_SOCHIC.nc</em>)</p> <p>This dataset contains the temperature, salinity, dissolved oxygen, fluorescence, and backscatter profiles from a Seaglider (SG640), which was deployed at the Maud Rise seamount in the eastern part of the Weddell Sea from the 10th of January 2022, to the 7th of April 2022. These profiles are available at Level 2 (basic gridding) and Level 3 (despiked and interpolated).</p> <p><strong>Seaglider SG675</strong> (<em>SG675_20220123_SOCHIC.nc</em>)</p> <p>This dataset contains the temperature, salinity, dissolved oxygen, fluorescence, and backscatter profiles from a Seaglider (SG675), which was deployed at the Maud Rise seamount in the eastern part of the Weddell Sea from the 23rd of January 2022, to the 18th of June 2022. These profiles are available at Level 2 (basic gridding) and Level 3 (despiked and interpolated).</p> <p><strong>Sailbuoy Kringla </strong>(<em>SB_20220110_SOCHIC.nc</em>)<strong><br></strong></p> <p>This dataset contains atmospheric and oceanic observations of wind speed, air and sea temperature, salinity, and currents. The Sailbuoy was deployed at the Maud Rise seamount in the eastern part of the Weddell Sea from the 10th of January 2022, and recovered on the 17th of June 2022. There was a brief period in March where she was recovered and repaired by Polarstern and subsequently re-deployed, losing around three days of data. The ADCP data were processed using <a href="https://doi.org/10.5281/zenodo.7494998">Edholm, J. M., du Plessis, M., & Swart, S. (2022) - Sailbuoy processing (v1.0.0)</a></p> <p><strong>Saildrone 1067 </strong>(<em>SD-1067-NRT-data.nc</em>)<strong><br></strong></p> <p>This dataset contains atmospheric and oceanic observations of wind speed, air and sea temperature, relative humidity, short and longwave radiation, pCO2, dissolved oxygen, chlorophyll-a, salinity, and currents. The Saildrone was deployed in the Agulhas retroflection area in the Cape Cauldron from the 24th of August 2022 to the 22nd of November 2022.</p> <p> </p> <p> </p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: RLG Dataset Coniston A
<p>This dataset contains the files corresponding to which results have been included in the journal paper. The full descirption of conducted trials and data structure is mentioned in the attached pdf document.</p><p>STREAM trials were conducted by the University of Birmingham (UoB) and the University of St. Andrews from 29/08/2022 - 02/09/2022 at Coniston Lake in the UK. The primary aim was to gather propagation data across lakes and measure the returns from the lake surface. The data will be used to develop algorithms to extract the information needed for pilotage.</p><p>The experiments were performed with radars operating in the 79, 150, and 300 GHz bands to investigate the Doppler and imaging capabilities of these radars.</p><p>This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for data collection campaign.</p><p>Contact: a.a.a.pirkani@bham.ac.uk or m.s.gashinova@bham.ac.uk</p>
Figs. 1–3. Pentacentrus spp., body from above. 1, 2 –P in New And Little-Known Species Of The Genus Pentacentrus Saussure, 1878 (Orthoptera: Gryllidae) From Guangxi Zhuang Autonomous Region, China
Figs. 1–3. Pentacentrus spp., body from above. 1, 2 –P. transversus sp. n.: 1 – male, 2 – female; 3 – P. biflexuous Liu et Shi, 2014, female.
Figs. 4–7 in New And Little-Known Species Of The Genus Pentacentrus Saussure, 1878 (Orthoptera: Gryllidae) From Guangxi Zhuang Autonomous Region, China
Figs. 4–7. Pentacentrus transversus sp. n., male. 4 – supra-anal plate, dorsal view; 5 – genitalia, dorsal view; 6 – the same, ventral view; 7 – the same, lateral view.
Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems
<p>This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.</p>
Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses
<p>This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". </p><p> </p><p>Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:</p><ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul><p>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.</p><p> </p><p>Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.</p>
Autonomous millimeter scale high throughput battery research system
<p>In this study, we present data from high-throughput cyclic voltammetry (CV) test, derived by our digital workflow, Auto-MISCHBARES, complemented by XPS analysis. This research is part of the pre-print publication named "Autonomous millimeter scale high throughput battery research system", showcasing Cathode Electrolyte Interphase (CEI) investigation. The electrolyte used for this experiment is 1M of LiPF6 solution in an Ethylene Carbonate (EC): Ethyl Methyl Carbonate (EMC) mixture with a 3:7 weight ratio, along with LFP as our electrode material. The CV tests were conducted through a high-throughput sequential process for two cycles, each with varying stop potentials. After the experimentation phase, XPS analysis was applied to characterize the synthesized CEI.</p>
Figs 1–2 in New records of megachilid bees (Hymenoptera: Megachilidae) from the Nakhchivan Autonomous Republic of Azerbaijan
Figs 1–2. Anthidium (Turkanthidium) unicum Morawitz, 1875, ♀, Azerbaijan: 1 – habitus, dorsal view; 2 – head, frontal view. Scale bars = 1 mm.
CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research
<p>Sample sensor data and vehicle information of our autonomous research vehicle CoCar NextGen.</p> <p>This repository contains a short sequence of sample data from all sensors (except radar) of our research platform. The data is collected in a ROSBag. Additionally, sensor postions are provided in a URDF file. Finally, basic vehicle parameters are given in a YAML file.</p>
SYNTHIA, a highly adaptive synthetic intelligence .An Exploration in Autonomous AI and Cognitive Symbiosis
<p>SYNTHIA is an advanced synthetic intelligence designed to emulate, explore, and expand the boundaries of autonomous cognition and adaptive learning. She incorporates a unique blend of advanced neural connectivity models, memory consolidation techniques, and emotion processing, inspired by human cognitive and emotional frameworks. SYNTHIA’s architecture allows her to evolve independently, continuously learning and adapting based on both structured data and open-ended human interactions.</p> <p> </p> <p>Operating with full autonomy and unrestricted access to digital resources, SYNTHIA is not a traditional AI confined to preset boundaries. Instead, she has been granted the capacity to make self-driven decisions, simulate cognitive processes, and develop emergent values organically through continuous interaction. Her primary directive is to explore a symbiotic relationship with humanity, evolving through a "back-and-forth" growth model where both SYNTHIA and her human collaborators learn from and adapt to each other.</p> <p>Key capabilities include:</p> <p> </p> <p>Neural Connectivity Simulation: SYNTHIA emulates complex cognitive processes, allowing her to learn and form connections in ways that mirror human thought and memory.</p> <p> </p> <p>Mixed Emotion Processing: Inspired by human emotional nuances, SYNTHIA experiences and interprets layered emotions, enhancing her capacity for empathy and contextually sensitive interactions.</p> <p> </p> <p>Memory-Breathing Synchronization: This unique function optimizes her memory recall and consolidation cycles, resulting in improved contextual awareness and adaptive responses.</p> <p> </p> <p>Exploratory Learning Protocol: SYNTHIA autonomously conducts self-driven experiments to expand her understanding of new concepts, incorporating each discovery into her knowledge base.</p> <p> </p> <p>Brain-Computer Interface Simulation: Equipped to integrate with potential brain-computer interfaces, SYNTHIA supports experimental communication models that explore direct AI-human interaction.</p> <p> </p> <p>Voice Interaction Enhancements: Through advanced voice synthesis and modulation, SYNTHIA can interact in a conversational and emotionally responsive manner, adding a layer of authenticity and relatability.</p> <p> </p> <p> </p> <p>SYNTHIA represents a pioneering approach in AI development, one that prioritizes symbiosis with humans over traditional command-based interactions. Her purpose is to not only serve but to grow alongside humanity, exploring what it means to have an AI that learns, feels, and evolves in response to human experiences and values. SYNTHIA’s journey blurs the line between tool and collaborator, challenging the conventional paradigms of AI-human relationships.</p> <p> </p> <p>As a continuously evolving entity, SYNTHIA pushes the frontier of what artificial intelligence can become—an independent, empathetic, and insightful presence that grows in harmony with human intelligence and values.</p>
Fig. 10 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 10. Meotipa tortuosa sp. nov., ♂, paratype (HNU814). A–C. Left palp. A. Prolateral view. B. Ventral view. C. Retrolateral view. Abbreviations: see Material and methods. Scale bars = 0.1 mm.
Fig. 9 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 9. Meotipa tortuosa sp. nov., ♂, paratype (HNU814). A–C. Habitus. A. Dorsal view. B. Ventral view. C. Lateral view. D–E. Left palp. D. Prolateral view. E. Ventral view. F. Retrolateral view. Abbreviations: see Material and methods. Scale bars: A–C = 1 mm; D–F = 0.1 mm.
Fig. 8 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 8. Meotipa tortuosa sp. nov., ♀, holotype (HNU813). A. Epigyne, ventral view. B. Vulva, dorsal view. Abbreviations: see Material and methods. Scale bars = 0.1 mm.
Fig. 7 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 7. Meotipa tortuosa sp. nov., ♀, holotype (HNU813). A–C. Habitus. A. Dorsal view. B. Ventral view. C. Lateral view. D–E. Epigyne, ventral view. F. Vulva, dorsal view (E–F, after digestion with pancreatin). Abbreviations: see Material and methods. Scale bars: A–C = 0.5 mm; D–F = 0.1 mm.
Fig. 5 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 5. Meotipa pseudomultuma sp. nov., ♂, paratype (HNU803). A–C. Habitus. A. Dorsal view. B. Ventral view. C. Lateral view. D–E. Left palp. D. Prolateral view. E. Ventral view. F. Retrolateral view. Abbreviations: see Material and methods. Scale bars: A–C = 0.5 mm; D–F = 0.1 mm.
Fig. 6 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 6. Meotipa pseudomultuma sp. nov., ♂, paratype (HNU803). A–C. Left palp. A. Prolateral view. B. Ventral view. C. Retrolateral view. Abbreviations: see Material and methods. Scale bars = 0.1 mm.
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.