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1,654 results for “Automation”
Brainport, Automated valet parking, autonomous parking after dropoff
<p><strong>Scenario description</strong>:</p> <p>The Vehicle received parking command message (AutoPilot.VehicleCommand) containing the destination parking spot and the free obstacle route and drives from the drop-off position and parks to the destination parking spot. During the parking process the vehicle send the two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The TNO vehicle parks autonomously from the dropoff to the selected parking spot in the parking place at the automotive campus. </p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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>
Brainport, Automated valet parking, TNO vehicle, automated pickup
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination pickup location and the free obstacle route and drives from the parking spot and parks to the destination pickup location. During the collection process the vehicle sends two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The pickup Scenario with TNO vehicle. TNO vehicle parks autonomously from the parking spot to the pickup location at the parking area on the automotive campus.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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>
Brainport, Automated valet parking, RS camera parking spot occupancy
<p><strong>Scenario description</strong>:</p> <p>RS Camera parking spot occupancy detection and publication of the iot message from type AutoPilot.ParkingSpotDetection to the PMS via IoT platforms</p> <p><strong>Session description</strong>:</p> <p>A AD-car parks to the selected parking spot the rs camera detect the car at the parking spot and publish the occupancy information to the PMS for parking management purpose</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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>
Brainport, Automated valet parking, dropoff scenario, DLR vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination parking spot and the free obstacle route and drives from the drop-off position and parks to the destination parking spot. During the parking process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The dropoff scenario with DLR vehicle. DLR vehicle parks autonomously from the dropoff location to the selected parking spot at the parking area on DLR test area in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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>
Brainport, Automated valet parking, pickup DLR automated vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination pickup spot and the free obstacle route and drives from the parking spot and parks to the destination pickup spot. During the collection process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The pickup scenario with DLR vehicle. DLR vehicle parks autonomously from the parking spot to the destination pickup spot at the parking area on DLR test site in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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>
Brainport, Automated valet parking, dropoff automated DLR vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination parking spot and the free obstacle route and drives from the drop-off position and parks to the destination parking spot. During the parking process the vehicle send two type of messages ( AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The dropoff scenario with DLR vehicle. DLR vehicle parks autonomously from the dropoff location to the selected parking spot at the parking area on DLR test area in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_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_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_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, Urban driving, Automated vehicle detects fallen bicycle
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching a fallen bicycle.</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. The AD car publishes the detected event to the oneM2M and safely reduces its speed until to stop. 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 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>
Vigo, Automated Valet Parking, Drop off and Pick up
<p><strong>Scenario description:</strong></p> <p>The vehicle starts parked in the drop off area. A drop off request is made to the parking service through the mobile phone application. Once the parking manoeuvre is finished, a pick up request is made. The test ends when the vehicle has been parked in the pick up area and the app informs the user.</p> <p><strong>Session description:</strong></p> <p>Human factor tests performed at Praza do Rei parking in Vigo. It is an enclosed area with no GPS coverage, so a LIDAR indoor positioning system is used.</p> <p><strong>Datasets descriptions:</strong><br> <br> <strong>AUTOPILOT_Vigo_UrbanDriving_Vehicle: </strong>Data extracted from the CAN of the vehicle. Contains data regarding the state of the vehicle, such as speed, engine speed or fuel consumption.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_VehicleDynamics: </strong>Data extracted from the CAN of the vehicle.Contains data regarding the dynamic state of the vehicle, such as speed limit, acceleration or yaw rate.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_DriverVehicleInteraction: </strong>Data extracted from the CAN of the vehicle. Contains all the information about the actions that the driver can perform, such as the state of the brake, the steering wheel and the throttle.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_EnviromentSensorsAbsolute: </strong>Data extracted from the sensors of the vehicle in absolute coordinates.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving__EnviromentSensorsRelative: </strong>Data extracted from the sensors of the vehicle in relative coordinates.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_Positioning:</strong> Data from the GPS of the vehicle, such as latitude longitude and heading.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_IoT: </strong>Data of the communications via IoT, coming from the OBU of the vehicle and the IoT server that contains the Parking Service.</p> <p> </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>
spanichella/RP_EMSE_MCR_2019 v.1.0.1 Second release of of the replication Package for the paper "An Empirical Investigation of Relevant Changes and Automation Needs in Modern Code Review".
<p>Replication Package for the paper "An Empirical Investigation of Relevant Changes and Automation Needs in Modern Code Review"</p> <p>Structure</p> <pre><code>project_raw_data/ gerrit_review_comments.csv gerrit_review_changes.csv survey_raw_data/ google_forms_survey.pdf google_forms_survey.csv RQ1_taxonomy_mcr/ RQ1_inception_phase/ initial_taxonomy.pdf intermediate_taxonomy.pdf RQ1_definition_phase/ Q1.2_evaluation_survey.csv cram_classified.csv cram.pdf RQ2_automation_needs/ Q2.1-Q2.5_evaluation_survey.xlsx Q2.6-Q2.7_evaluation_survey.xlsx Q2.1-Q2.7_question_index.csv Now "RQ3_automated_support/" contain the results concerning RQ2.1 in the paper. content explained in the README.md file located in "RQ3_automated_support/README.md" </code></pre> <p>Contents of the Replication Package</p> <p><strong>project-raw-data/</strong> contains the data used for the creation of our taxonomies, it includes information about the ten open-source projects.</p> <ul> <li><code>gerrit_review_comments.csv</code> - information about all in-line review comments used for this paper</li> <li><code>gerrit_review_changes.csv</code> - information about all patches analyzed that contain the in-line comments</li> </ul> <p><strong>survey_raw_data/</strong> contains information about the survey conducted for the paper.</p> <ul> <li><code>google_forms_survey.pdf</code> - the distributed <em>Google Forms</em> of our survey</li> <li><code>google_forms_survey.csv</code> - all survey answers obtained from 52 survey participants</li> </ul> <p><strong>RQ1_taxonomy_mcr/</strong> contains information and data about the elicited taxonomies in our paper (RQ1).</p> <ul> <li><strong>RQ1_inception_phase/</strong> <ul> <li><code>initial_taxonomy.pdf</code> - initial taxonomy obtained in the inception phase of our paper</li> <li><code>intermediate_taxonomy.pdf</code> - intermediate taxonomy after integrating and merging the initial taxonomy with the one by Beller <em>et al</em> [1]</li> </ul> </li> <li><strong>RQ1_definition_phase/</strong> <ul> <li><code>Q1.2_evaluation_survey.csv</code> - relevant survey feedback with additional taxonomy categories integrated into <em>CRAM</em></li> <li><code>cram_classified.csv</code> - classified review comments (<code>gerrit_review_comments.csv</code>) into <em>CRAM</em></li> <li><code>cram.pdf</code> - <em>CRAM</em> taxonomy</li> <li><code>cram_classified_with_frequency_information2020.xls</code> - it contain the information used to compute the frequency of CRAM changes, derived by the analysis of the 211 commits</li> </ul> </li> </ul> <p><strong>RQ2_automation_needs/</strong> contains the encoded evaluation of the survey question Q2.1-2.7 for RQ2</p> <ul> <li><code>Q2.1-Q2.5_evaluation_survey.xlsx</code> - the encoded evaluation of the survey questions Q2.1-Q2.5 (used for the <em>Automation Needs</em> Section in the paper) and contains the following sheets: <ul> <li><strong>all Findings</strong>: Very detailed findings matrix distilled from all answers concerning possible automated solutions or general possibilities to achieve automation in MCR. Every feedback was analyzed and decomposed into single findings. These findings are grouped, into categories of our Taxonomy of Code Changes in MCR (CRAM). Red represent in the feedback-text where the corresponding category was distilled from.</li> <li><strong>unique Findings</strong>: As one participant could mention the same categories/solutions in multiple feedbacks, the following matrix is cleaned of any duplication of participant answers. Multiple feedbacks containing the same information by one participant were removed, leaving only distinct occurances.</li> <li><strong>aggregated by Solution</strong>: Aggregated feeback clustered into abstracted solutions and the number of times participants mentioned the solution.</li> <li><strong>aggregated by Taxonomy</strong>: Aggregated feeback grouped by low-level categoried in CRAM.</li> </ul> </li> <li><code>Q2.6-Q2.7_evaluation_survey.xlsx</code> - the encoded evaluation of the survey questions Q2.6-Q2.7 (used for the <em>Automation Needs</em> Section in the paper) and contains the following sheets: <ul> <li><strong>all Findings</strong>: Very detailed findings matrix distilled from all answers concerning possible techniques, approaches and data to achieve automation in MCR. Every feedback was analyzed and decomposed into single findings. These findings are grouped, into categories of our Taxonomy of Code Changes in MCR (CRAM). Red represent in the feedback-text where the corresponding category was distilled from.</li> <li><strong>unique Findings</strong>: As one participant could mention the same categories/solutions in multiple feedbacks, the following matrix is cleaned of any duplication of participant answers. Multiple feedbacks containing the same information by one participant were removed, leaving only distinct occurances.</li> <li><strong>aggregated by low-level taxonomy</strong>: Aggregated mentionings of approaches/data by developers in the survey grouped by low-level taxonomy category.</li> <li><strong>aggregated by high-level taxonomy</strong>: Aggregated mentionings of approaches/data by developers in the survey grouped by high-level taxonomy category.</li> </ul> </li> <li><code>Q2.1-Q2.7_question_index.csv</code> - table of IDs given to each participant-question pair for Q2.1-Q2.7 in order to trace back the feeback.</li> <li><code>cram_survey-with_criticality_and_feasibility2020.xls</code> and <code>cram_survey-with_relevance_and_completeness_information2020.xls</code>: they contain we results of the survey, involving 14 additional participants (12 developers and 2 researchers), not involved in the aforementioned survey, and performed to qualitatively assess the relevance and completeness of the identified MCR change types as well as assess how critical and feasible to implement are some of the identified techniques to support MCR activities.</li> </ul> <p><strong>RQ2_1_automated_support/</strong> (or <strong>RQ3_automated_support/</strong> )- content explained in the README.md file located in "RP_EMSE_MCR_2019/tree/master/EMSE_MCR_2019/RQ3_automated_support/README.md"</p> <p>References</p> <p>[1] Moritz Beller, Alberto Bacchelli, Andy Zaidman, and Elmar Juergens. 2014. Modern code reviews in open-source projects: which problems do they fix?. In Proceedings of the 11th Working Conference on Mining Software Repositories (MSR 2014). ACM, New York, NY, USA, 202-211. DOI: <a href="http://dx.doi.org/10.1145/2597073.2597082">http://dx.doi.org/10.1145/2597073.2597082</a></p>
BAGS: an automated barcode, audit & grade system for DNA barcode reference libraries
<p>Biodiversity studies greatly benefit from molecular tools, such as DNA metabarcoding, which provides an effective identification tool in biomonitoring and conservation programmes. The accuracy of species-level assignment, and consequent taxonomic coverage, relies on comprehensive DNA barcode reference libraries. The role of these libraries is to support species identification, but accidental errors in the generation of the barcodes may compromise their accuracy. Here we present an R-based application, BAGS (Barcode, Audit & Grade System; https://github.com/tadeu95/BAGS), that performs automated auditing and annotation of cytochrome c oxidase subunit I (COI) sequences libraries, for a given taxonomic group of animals, available in the Barcode of Life Data System (BOLD). This is followed by implementing a qualitative ranking system that assigns one of five grades (A to E) to each species in the reference library, according to the attributes of the data and congruency of species names with sequences clustered in Barcode Index Numbers (BINs). Our goal is to allow researchers to obtain the most useful and reliable data, highlighting and segregating records according to their congruency. Different tests were performed to perceive its usefulness and limitations. BAGS fulfils a significant gap in the current landscape of DNA barcoding research tools by quickly screening reference libraries to gauge the congruence status of data and facilitate the triage of ambiguous data for posterior review. Thereby, BAGS has the potential to become a valuable addition in forthcoming DNA metabarcoding studies, in the long term contributing to globally improve the quality and reliability of the public reference libraries.</p>
Automated vegetation cover estimation from close-range photogrammetric point clouds in mountain terrain for comparison of vegetation location properties - Dataset
<p>Vegetation cover data of the used plots, showing values for manually digitized, in-situ, and photogrammetric methods.</p>
OggyBug: A Test Automation Tool in Chatbots
<p>Backup video for the presentation of the paper titled "OggyBug: A Test Automation Tool in Chatbots", published in SAST - CBSoft 2020</p>
Data for the MLCS 2020 paper "A Year of Automated Anomaly Detection in a Datacenter"
<p>This contains the data used for the paper by Ahmed et. al in the MLCS 2020 paper "A Year of Automated Anomaly Detection in a Datacenter". Each of the four CSV files corresponds to one of the quarters discussed in the paper, and each has a metadata file containing information about the query that produced them. The CSV files contain the 'raw' log messages, and an eventID that identifies which pattern the log entry matched; the eventID is used to group together log messages of the same type. These logfiles were collected on the CloudLab facility (https://cloudlab.us/) from Jan 1 - Dec 30, 2019.</p> <p>The violated_unviolated_sessions_*.txt files each contain 20 randomly-selected sessions: half of the sessions were labeled by the invariant miner as being 'normal', and the other half 'anomalous'. CloudLab developers and system administrators were asked to label these sessions manually (and were not given the invariant miner's labels). The corresponding *_manual_labels.txt contain the labels that the administrators assigned, and in some cases additional correspondence with the administrators and information about which manual labels matched the invariant miner and which did not.</p>
Automated design of synthetic microbial communities
<p>In naturally occurring microbial systems, species rarely exist in isolation. There is strong ecological evidence for a positive relationship between species diversity and the functional output of communities. The pervasiveness of these communities in nature highlights that there may be advantages for engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigates issues often found in engineering a monoculture, especially when functional complexity is increasing. Here, we demonstrate a methodology for designing robust synthetic communities that use quorum sensing to control amensal bacteriocin interactions in a chemostat environment. We explore model spaces for two and three strain systems, using Bayesian methods to perform model selection, and identify the most robust candidates for producing stable steady state communities. Our findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and tuning the community composition.</p>
Released Experimental Dataset for Sampled Automated Machine Learning
<p>Released Experimental Dataset of "Doing More with Less: Characterizing Dataset Downsampling for AutoML"</p> <p> </p> <p>Experiments were run for 5 and 60 minutes on 16 datasets:<br> 4 small: < 10.000<br> 5 medium: < 100.000<br> 7 large: > 100.000<br> </p>
Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning
<p>COVID-19 plasma samples spectrometry datasets for machine learning input. Used in the work of article Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning, currently under submittion.</p> <p>Abstract:</p> <p>COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) was enrolled from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules that are related to the disease’s pathophysiology and several discriminating features to patient’s health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings the disease’s pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using Machine Learning algorithms, transforming this screening approach in a tool with great potential for real-world application. </p>
Automated prediction of visual complexity of web pages: Tools and evaluations
<p>This dataset includes the screenshots of the web pages used for the evaluation of ViCRAM which is described in the following paper:</p> <p>Eleni Michailidou, Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2020. Automated Prediction of Visual Complexity of Web Pages: Tools and Evaluations. International Journal of Human-Computer Studies (SCI-E, SSCI), 145, 102523.</p>
Evaluation results for When a Computer Cracks a Joke: Automated Generation of Humorous Headlines
<p>Evaluation results for the paper:</p> <p>Alnajjar, K., & Hämäläinen, M. (2021) When a Computer Cracks a Joke: Automated Generation of Humorous Headlines. In<em> The Proceedings of the Twelfth International Conference on Computational Creativity, ICCC’21</em>.</p> <p>The table has the aggregated evaluation results for each evaluation question. The left and right columns are the title before and after the replacement word. The replacement column show the humorous word and original column the word that existed in the headline before the replacement. The system column indicates whether the humorous headline was produced by our system or by a human.</p>
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.