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16 results for “Highway pilot”
Brainport, Highway pilot, control center ADAS instructions publication
<p><strong>Scenario description</strong>:</p> <p>Hazards have been detected and populate the map. Control Center publishes ADAS instructions for some hazards.</p> <p><strong>Session description</strong>:</p> <p>Control Center published ADAS Instructions for 4 identified hazards of the 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, 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, but receiving adaption instructions
<p><strong>Scenario description</strong>:</p> <p>The driving adaptation car is driven around the track in manual mode, but driving instructions are communicated to the driver.</p> <p><strong>Session description</strong>:</p> <p>12 laps with Jaguar F-Pace on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
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, simulated autonomous mode
<p><strong>Scenario description</strong>:</p> <p>The driving adaptation car drives around the track in simulated autonomous mode (ACC).</p> <p><strong>Session description</strong>:</p> <p>18 laps with Jaguar F-Pace on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
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, driving adaptation at hazards locations
<p><strong>Scenario description</strong>:</p> <p>The driving adaptation car drives around the track in simulated autonomous mode (ACC) and applies ADASINs at Hazards locations.</p> <p><strong>Session description</strong>:</p> <p>25 laps with Jaguar F-Pace on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
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, Highway pilot, only connected cars
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events:</p> <p>1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with only connected cars, lap of 12,3 km on the highway. The goal is to check all the systems and data management before the next test session with AD cars.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, data management connected car
<p><strong>Scenario description</strong>: Dynamic speed adaptation due to puddle on the road</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway" at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events:</p> <p>1 The puddle monitoring system of the highway trigger a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of dangerous area, as notified by the «smart road», the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>pre-test session with only connected cars, lap of 12,3 km on the highway. Goal is to check all the system and data management.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated car, two connected cars, smart highway
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events: </p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and two connected cars, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated and connected car and one connected car
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events:</p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and one connected car, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated and connected car
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events:</p> <p>1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car, lap of 12,3 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 the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, connected car system and data management
<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 shares 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>pre-test session with only connected cars, lap of 11,6 km on the highway. Goal is to check all the system and data management.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated and two connected cars
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events: </p> <p>1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and two connected cars, lap of 12,3 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 Pilot session 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 Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Pilot session 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>
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.