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9,302 results for “DETECT”
Livorno, Urban driving, basic session with detection
<p>Scenario description:</p> <p>Detect a pedestrian crossing with the red light and send DENM message to warn vehicles of the presence of the pedestrian</p> <p>Applicable datasets:</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>
Livorno, Urban driving, Connected vehicle detects fallen bicyle
<p><strong>Scenario description</strong>:</p> <p>Test session for fallen bicycle with connected but not automated vehicle</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.</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>
Livorno, Urban driving, Pedestrian detection
<p><strong>Scenario description</strong>:</p> <p>The pedestrian detection use case aims to demonstrate the possibility for a vehicle to be informed, using V2X communication, of the presence of a pedestrian crossing the road when the traffic light is green for the vehicle. In case of presence of a pedestrian, the RSU with detection capabilities signals the presence of the pedestrian to the vehicles using DENM messages.</p> <p><strong>Session description</strong>:</p> <p>Pedestrian detection</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 Drining 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>
Brainport, Urban driving, route request, VRU detection
<p><strong>Scenario description</strong>:</p> <p>Smartphone 3120 sends request, route 2 is being blocked by crowd (depending on run this changes), vehicle receives correct route and drives from west to east. Underway 2 VRU (3121 & 3122) crosses the road.</p> <p><strong>Session description</strong>:</p> <p>Complete use case: Request vehicle, choose route on CEMA and GeoFenching VRU detection with 2 smartphone detection</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_EAI2Mobile</strong>: Data from the service to the mobile</p> <p>Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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_UrbanDriving_IOT_CEMA_Message</strong>: Data from the service to the vehicle</p> <p>Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message</strong>: Data from the vehicle to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_IOT_VehicleStatus</strong>: Data sent from the vehicle to the service</p> <p>Dataset Description This dataset contains the current status of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_SmartphoneGPS</strong>: Data sent by the mobile to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the current status of the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the requests for a taxi from the mobile phones</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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, Urban driving, VRU smartphone detection, camera detection
<p><strong>Scenario description</strong>:</p> <p>Only GeoFenching VRU detection with 3 smartphone detection<br> Test detection of multiple VRUs close to each other and compare with camera detections.<br> Test different size GeoFence area (20m wide x 50m long) of detection with different pedestrian walking paths (for pedestrian prediction) see Test plan Table 4</p> <p><strong>Session description</strong>:</p> <p>Route is fixed, vehicle drives north - south. Underway 1 group of 3 VRU crosses the road.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_EAI2Mobile</strong>: Data from the service to the mobile</p> <p>Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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_UrbanDriving_IOT_CEMA_Message</strong>: Data from the service to the vehicle</p> <p>Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message</strong>: Data from the vehicle to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_IOT_VehicleStatus</strong>: Data sent from the vehicle to the service</p> <p>Dataset Description This dataset contains the current status of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_SmartphoneGPS</strong>: Data sent by the mobile to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the current status of the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the requests for a taxi from the mobile phones</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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, Urban driving, baseline test, camera detection
<p><strong>Scenario description</strong>:</p> <p>Base line test: no CEMA, no GeoFencing enabled. Vehicle only brakes on camera detection, when vehicle is blocked on its route by a crowd</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_EAI2Mobile</strong>: Data from the service to the mobile</p> <p>Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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_UrbanDriving_IOT_CEMA_Message</strong>: Data from the service to the vehicle</p> <p>Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message</strong>: Data from the vehicle to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_IOT_VehicleStatus</strong>: Data sent from the vehicle to the service</p> <p>Dataset Description This dataset contains the current status of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_SmartphoneGPS</strong>: Data sent by the mobile to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the current status of the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the requests for a taxi from the mobile phones</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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, Urban driving, route request, rerouting reception, VRU detection
<p><strong>Scenario description</strong>:</p> <p>Smartphone 3120 sends request, route 2 is being blocked by crowd (depending on run this changes), vehicle receives correct route and drives from west to east. Underway 1 VRU crosses the road.</p> <p><strong>Session description</strong>:</p> <p>Complete use case: Request vehicle, choose route on CEMA and GeoFenching VRU detection with 1 smartphone detection</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_EAI2Mobile</strong>: Data from the service to the mobile</p> <p>Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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_UrbanDriving_IOT_CEMA_Message</strong>: Data from the service to the vehicle</p> <p>Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message</strong>: Data from the vehicle to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_IOT_VehicleStatus</strong>: Data sent from the vehicle to the service</p> <p>Dataset Description This dataset contains the current status of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_SmartphoneGPS</strong>: Data sent by the mobile to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the current status of the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the requests for a taxi from the mobile phones</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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, Urban driving, car rebalancing, VRU detection, rerouting
<p><strong>Scenario description</strong>:</p> <p>Precondition:<br> - AD Vehicle (3177) is at starting position<br> - VRU with Smartphone (3120) is at end position<br> - VRU with Smartphone (3121) and FlowRadar (3175) is positioned halfway the vehicle track<br> - CEMA device is on route 2 and detecting crowd<br> <br> Initiate Rebalancing phase:<br> 1. Smartphone 3120: send taxi request (send end location to vehicle (lon / lat))<br> Initiate Routing phase:<br> 2. CEMA devices detect crowds on route 1 or route 2 --> dynamically change routes base on CEMA<br> 3. Motion planning: Vehicle uses CEMA detection and call taxi request as input for choosing one of the two routes<br> Initiate Urban Driving phase (incl. VRU detection)<br> 4. Vehicle starts driving<br> 5. Underway VRU (3121) is detected using smartphone and FlowRadar ITS-G5<br> 6. With GeoFenching, vehicle starts slowing down on smartphone<br> 7. With camera detection vehicle brakes when VRU is inline of camera<br> 8. After VRU passes, vehicle continues journey<br> 9. Vehicle arrives at requested end point (at VRU 3120).</p> <p><strong>Session description</strong>:</p> <p>Rebalancing:<br> - relocate the vehicle on request<br> Urban Driving:<br> - rerouting on crowd estimation (CEMA)<br> - VRU detection with GeoFenching in vehicle (speed reduction) and warning on VRU smartphone</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_EAI2Mobile</strong>: Data from the service to the mobile</p> <p>Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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_UrbanDriving_IOT_CEMA_Message</strong>: Data from the service to the vehicle</p> <p>Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message</strong>: Data from the vehicle to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_IOT_VehicleStatus</strong>: Data sent from the vehicle to the service</p> <p>Dataset Description This dataset contains the current status of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_SmartphoneGPS</strong>: Data sent by the mobile to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the current status of the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the requests for a taxi from the mobile phones</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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, Urban driving, request route, VRU detection, route correction
<p><strong>Scenario description</strong>:</p> <p>Complete use case: Request vehicle, choose route on CEMA and GeoFenching VRU detection with 4 smartphone detections<br> Test detection of multiple VRUs close to each other and compare with camera detections.<br> Test GeoFence area of detection with different pedestrian walking paths (for pedestrian prediction)<br> Make comparison with GeoFenching enabled (runs 1-12) and with GeoFencing disabled (runs 13-25)</p> <p><strong>Session description</strong>:</p> <p>Smartphone 3120 sends request, route 2 is being blocked by crowd (depending on run this changes), vehicle receives correct route and drives from west to east. Underway 2 groups of each 2 VRU crosses, or walk alongside the road (see Test Plan Table 3 for exact walking patterns).</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_EAI2Mobile</strong>: Data from the service to the mobile</p> <p>Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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_UrbanDriving_IOT_CEMA_Message</strong>: Data from the service to the vehicle</p> <p>Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message</strong>: Data from the vehicle to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_IOT_VehicleStatus</strong>: Data sent from the vehicle to the service</p> <p>Dataset Description This dataset contains the current status of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_SmartphoneGPS</strong>: Data sent by the mobile to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the current status of the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the requests for a taxi from the mobile phones</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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, Urban driving, fixed route, VRU detection
<p><strong>Scenario description</strong>:</p> <p>Only GeoFenching VRU detection with 3 smartphone detection<br> Test detection of multiple VRUs close to each other and compare with camera detections.<br> Test different size GeoFence area (20m wide x 50m long) of detection with different pedestrian walking paths (for pedestrian prediction) </p> <p><strong>Session description</strong>:</p> <p>Route is fixed, vehicle drives north - south. Underway 1 group of 3 VRU crosses the road.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_EAI2Mobile</strong>: Data from the service to the mobile</p> <p>Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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_UrbanDriving_IOT_CEMA_Message</strong>: Data from the service to the vehicle</p> <p>Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message</strong>: Data from the vehicle to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_IOT_VehicleStatus</strong>: Data sent from the vehicle to the service</p> <p>Dataset Description This dataset contains the current status of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_SmartphoneGPS</strong>: Data sent by the mobile to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the current status of the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the requests for a taxi from the mobile phones</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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, Highway pilot, car in manual mode, camera detection
<p><strong>Scenario description</strong>:</p> <p>The detection car drives around the track in manual mode, with Camera detection ON.</p> <p><strong>Session description</strong>:</p> <p>25 laps with VW Tiguan 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>
Brainport, Highway pilot, car in manual mode, IMU detection
<p><strong>Scenario description</strong>:</p> <p>The detection car drives around the track in manual mode, with IMU detection ON.</p> <p><strong>Session description</strong>:</p> <p>25 laps with VW Tiguan 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>
Brainport, Highway pilot, detection car reference laps
<p><strong>Scenario description</strong>:</p> <p>The detection car drives around the track in manual mode, at an average speed close to laps for detections, but with all detections systems disabled.</p> <p><strong>Session description</strong>:</p> <p>25 laps with VW Tiguan 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>
Brainport, Highway pilot, detection car, manual mode, camera and IMU detection on
<p><strong>Scenario description</strong>:</p> <p>The detection car drives around the track in manual mode, with Camera and IMU detection ON.</p> <p><strong>Session description</strong>:</p> <p>25 laps with VW Tiguan 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>
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, 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>
Livorno, Urban driving, detection of fallen bicycle
<p><strong>Scenario description</strong>: Test session for fallen bicycle with AD and connected vehicle</p> <p><strong>Session description</strong>: 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 safely reduces its speed and stops.</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>
Database of global dam detections
<p>GPKG package of detected global dams from global imagery.</p>
Reproduction package for the paper "Near-ultraviolet detections of four dwarf nova candidates in the globular cluster 47 Tucanae"
<p>This is a basic reproduction package for the paper "Near-ultraviolet detections of four dwarf nova candidates in the globular cluster 47 Tucanae" by <a href="https://www.aanda.org/articles/aa/abs/2020/02/aa37043-19/aa37043-19.html">Modiano et al. (2020)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Distribution of interplanetary dust detected by the Juno spacecraft and its contribution to the Zodiacal Light
<p>The Zodiacal light is sunlight reflected by dust in the inner solar system. Variations in the Zodiacal light with ecliptic latitude reveal discrete bands of dust orbiting near the ecliptic plane. The Juno spacecraft, in transit from earth to Jupiter, recorded a sufficient number of impacts with this dust to characterize their distribution in space for the first time. </p> <p>This dataset (filename IDP_List.txt) contains a time-ordered list of all IDP impact detections along with supplementary engineering and ephemeris information. The file is an ASCII text file and the file format is described in the word document (IDP_List_Format.docx).</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.