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24 results for “Automated Vehicles”
Lithium-Ion Batteries in Automated Guided Vehicles (AGVs) dataset for article "Automated Battery Power Fade Estimation for Fast Charge and Discharge Operations"
<p>Dataset of aggregated information related to discharge-only cycles of lithium-ion battery packs employed in Automated Guided Vehicle systems.</p> <p>The dataset supports the study in conference article "Automated Battery Power Fade Estimation for Fast Charge and Discharge Operations"</p>
Brainport, Automated valet parking, dropoff scenario TNO vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination parking spot and the free obstacle route and drives from the drop-off position and parks to the destination parking spot. During the parking process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The dropoff scenario with TNO vehicle. TNO vehicle parks autonomously from the dropoff location to the selected parking spot at the parking area on the automotive campus.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Brainport, Automated valet parking, TNO vehicle, automated pickup
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination pickup location and the free obstacle route and drives from the parking spot and parks to the destination pickup location. During the collection process the vehicle sends two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The pickup Scenario with TNO vehicle. TNO vehicle parks autonomously from the parking spot to the pickup location at the parking area on the automotive campus.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Brainport, Automated valet parking, dropoff scenario, DLR vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination parking spot and the free obstacle route and drives from the drop-off position and parks to the destination parking spot. During the parking process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The dropoff scenario with DLR vehicle. DLR vehicle parks autonomously from the dropoff location to the selected parking spot at the parking area on DLR test area in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Brainport, Automated valet parking, pickup DLR automated vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination pickup spot and the free obstacle route and drives from the parking spot and parks to the destination pickup spot. During the collection process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The pickup scenario with DLR vehicle. DLR vehicle parks autonomously from the parking spot to the destination pickup spot at the parking area on DLR test site in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Brainport, Automated valet parking, dropoff automated DLR vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination parking spot and the free obstacle route and drives from the drop-off position and parks to the destination parking spot. During the parking process the vehicle send two type of messages ( AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The dropoff scenario with DLR vehicle. DLR vehicle parks autonomously from the dropoff location to the selected parking spot at the parking area on DLR test area in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Livorno, Urban driving, Automated vehicle detects fallen bicycle
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching a fallen bicycle.</p> <p><strong>Session description</strong>:</p> <p>The fallen bicycle use case aims to demonstrate the possibility for a vehicle to detect in advance, using V2X communication, the presence of a fallen bicycle on the road. In case of fall, the bicycle signals its presence to the other vehicles using DENM messages. The AD car publishes the detected event to the oneM2M and safely reduces its speed until to stop. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Examining drivers' behaviors to connected and automated vehicles
<p>It is envisioned that Connected and Automated Vehicles (CAVs) are the future of transportation as they can assist in minimizing some inefficiencies with the current transport systems. However, it is not clear how drivers of conventional vehicles would interact with CAVs in a mixed traffic environment containing both CAVs and human driven vehicles (HDVs). Thus, this study aims to investigate drivers’ behaviors towards CAVs through driving simulation experiment and national survey study. Two on-ramp and two off-ramp driving simulation scenarios were designed where drivers were asked to merge with two-lane highway in presence of HDVs and CAVs truck platoon in the on-ramp scenarios. In the two off-ramp scenarios, they were asked to take exit in presence of HDVs and CAVs truck platoon in the lane to their right. A before-after survey was conducted among the participant of the driving simulation experiment and an online survey was conducted to investigate their opinion in different traffic, road and environmental condition in presence of CAVs. Furthermore, two driving simulation scenarios were designed to test drivers’ behaviors during automated driving mode and their reaction when the control was shifted to the manual driving mode. Results from the on-ramp scenarios and off-ramp scenarios indicate that more than half of the drivers preferred to merge in front of CAV truck platoon and around two-third of the drivers chose to diverge behind the platoon. The online survey revealed that around two-third of the respondents would not overtake CAV platoon in two-lane two-way road, whereas around 60% would do so in case of three lane highway. During automation failure, drivers demonstrated lower take-over reaction time (TORt), lower deceleration and higher TTC in scenarios with non-driving related tasks (NDRT) compared to the scenarios with no NDRT (manual mode). The before-after survey results suggest that most of the drivers found the navigation with CAV easier after participating in the experiment.</p>
Emissions impact of connected and automated vehicle deployment in California - model results
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Brainport, Automated valet parking, pickup scenario with DLR vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination pickup spot and the free obstacle route and drives from the parking spot and parks to the destination pickup spot.During the collection process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The pickup scenario with DLR vehicle. DLR vehicle parks autonomously from the parking spot to the destination pickup spot at the parking area on DLR test site in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Livorno, Urban driving, Automated vehicle and smart traffic light
<p><strong>Scenario description</strong>:</p> <p> </p> <p><strong>Session description</strong>:</p> <p> </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, Automated and connected vehicle and smart traffic light
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light.</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light sends SPaT and MAP messages describing the topology, actual status of the traffic light to other connected vehicles and to the oneM2M platform on the cloud.<br> An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, considering also other vehicles moving in front. 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 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>
Livorno, Urban driving, Automated vehicle approaching intersection
<p><strong>Scenario description</strong>:</p> <p>Test session for AD vehicle approaching an intersection with jaywalking at traffic light</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light with a stereocamera sends SPaT and MAP messages describing the topology, actual status of the traffic light, presence of pedestrian, and jaywalking occurrence to other connected vehicles (via DENM) and to the oneM2M platform on the cloud. An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, or even stop to avoid collision with pedestrian. The influence of other vehicles moving in front is considered too.</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>
Automated Vehicles
<p><em><strong>In the first Rebalance Dialogue, <a href="https://rebalancemobility.eu/andrea-ricci/">Andrea Ricci</a> moderated a conversation between <a href="https://rebalancemobility.eu/ghadir-pourhashem/">Ghadir Pourhashem</a> and <a href="https://rebalancemobility.eu/cristina-pronello/">Cristina Pronello</a> about how Automated vehicles (AV) are expected to release people from the technical constraint of driving and allow people to do multiple tasks at the same time.</strong></em></p> <p>Under the assumption that a cultural shift is needed before the massive adoption of AV, REBALANCE has identified, among others, a variety of ingrained cultural and value-driven attitudes that is likely to play a fundamental role in the future of AV, beyond their technological performance: fear of innovation, fear of privacy breaches, fear of losing control, impoverishment of the human dimension of the travel experience, and ethical issues.</p> <p>During the conversation, many questions arose about whether and how to address these factors in order to impel people towards cultural shifts that would facilitate the uptake of AV. Forceful policies or nudges might initiate a slippery slope towards the dangers of an “ethical state”.</p> <p>The discussants agreed that freedom and control are two major concepts that come into play when considering people’s reliance on automation. Passivity can easily be associated with a diminished experience of freedom or loss of control. One of the most exploited arguments to overcome and compensate for this distrust is the alleged increase in road safety.</p> <blockquote> <p>The promised environmental benefits of driverless vehicles often come by the hand of shared mobility. This innovative duo might become a successful symbiotic couple or, on the contrary, this partnership could hinder their diffusion.</p> </blockquote> <p>According to the participants, the impact of vehicle automation on social inclusiveness remains uncertain. Further isolation and alienation cannot be ruled out. The paragon of the currently observed effects of COVID-19 on mobility choices might be valid once dictated whether they are transitory or permanent.</p> <p>Regarding the technological aspect and the scepticism to adopt new technologies because of lack of experience (or knowledge), the doubt in the minds of the participants appeared when considering that most technologically innovative solutions for social and environmental sustainability are mainly patching problems created by previous technological innovation. Technologies are created for doing business and not for solving problems. AV or Electric vehicles are just an attempt by the automotive sector to stay alive. Until the identification of the outset of technological progress that intrinsically leads us to sustainability, current prevailing values (such as efficiency, speed, cost savings, technological progress “per se”) perpetuate this headlong flight. The integration of technological progress will have to deal with two sets of goals, which are incompatible, or at least partly in contrast with one another: to increase speed and efficiency (cost, energy, time…); to increase comfort, safety and quality of life.</p> <p>The younger generations are shifting priority values, but it is not clear yet how this translates into the adoption of technological innovation and, particularly in the case of AVs.</p> <p>Then the conversation dealt with several topics regarding AV reliability such as misconceptions and incoherence about the use of time in AV/public transport. In that sense,</p> <blockquote> <p><strong>the rejection of AV might not be related to fear of innovation but to the impossibility to reproduce human thinking and moral decisions that bring no confidence in the machine. Socialisation and conviviality thanks to AV goes with many question marks (gender, age gap).</strong></p> </blockquote> <p>In the domains of privacy and personal data, we might find privacy issues with personal data and sharing technologies. Unfortunately, no data are publicly available for innovations and research.</p> <p>Aspirational values come into play when recognizing that the most important trip one person does is associated with emotion and pleasure. Therefore, the pleasure of driving or the preference for driving oneself can also be related to fear to change habits. There could be a confrontation between improvement of quality of life versus optimisation and misperceived opinions as facts. Maybe social inclusion will not come by sharing.</p> <p>Governance raised different points in the dialogue such as confidence of the public in policy-makers, the way of presenting to the public possible options (data, conclusions…) and targeted nudges.</p> <p>Finally, other factors agreed by the discussants that could hinder the adoption of new technologies are accidents and failures of self-driving vehicles, the fear of innovation, and ethical issues.</p>
Managing Human Factors in Automated Vehicle Development: Towards Challenges and Practices
<p>This data set complements our publication with the title:</p> <p>"Managing Human Factors in Automated Vehicle Development: Towards Challenges and Practices" to appear in Proceedings of IEEE 31st International Requirements Engineering Conference (RE'23), Hannover, Germany, 2023 </p> <p>To support our research, we are providing two files:</p> <p>a) Interview Guide<br> b) Overview of Themes</p> <p>Please note that the themes mentioned in the theme file are not directly relevant to this specific paper. They relate to another study conducted using the same dataset. We have included these themes to demonstrate that the dataset was used for two separate papers, each examining different aspects.</p>
Data for project: A quantitative investigation into the impact of partially automated vehicles on vehicle miles travelled in California
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STRIDE Project C - Performance Measurement and Management Using Connected and Automated Vehicle Data
<p>The main objective of this study is to develop a methodological framework to estimate system performance measurements using CV data. The study also provides a validation of the framework as a proof of concept by determining performance measurements from traditional and CV data. In doing so, the microscopic simulation software VISSIM with trajectory conversion algorithm (TCA) is used to generate CV data, particularly basic safety message (BSM) for a study corridor located in Birmingham, AL. The estimated performance measures can be used by a system operator, planner, or an automated system to support decisions associated with these processes. The measurements can be also used to derive information for dissemination to travelers, third-party data aggregators, traveler information service providers, and other agencies. The collected and archived data includes real-world data collected from different sources in addition to simulation model results.</p>
Automated vehicles and central business district parking: the effects of drop-off-travel on traffic flow and vehicle emissions
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