Find research datasets worth reusing

Data for - The environmental footprint of transport by car using renewable energy

Replacing fossil fuels in the transport sector by renewable energy will help combat climate change. However, lowering greenhouse gas emissions by switching to alternative fuels or electricity can come at the expense of land and water resources. To understand the scale of this possible tradeoff we compare and contrast carbon, land and water footprints per driven km in midsize cars utilizing conventional gasoline, biofuels, bioelectricity, solar electricity and solar-based hydrogen. Results show that solar-powered electric cars have the smallest environmental footprints per km, followed by solar-based hydrogen cars, and that biofuel-driven cars have the largest footprints.

opencc-by-4.0Jun 2016View details →

Photonics4All Bookmark Car (German)

The purpose of the bookmarks for the project Photonics4all is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction). Do you know why the rear brake lights of cars are red? Red light can be seen better than other colours when it's raining or foggy because red light is made of longer wavelengths than other colours and scatters less. Shorter wavelengths - such as blue light - scatter more - which is why the sky appears blue. The light from red light appears brighter from further away than other colours would. Our eyes are also more sensitive in the dark to the colour red than any other colour - which is why brake lights are coloured red. Nowadays, LEDs are replacing old light bulbs, bringing better lighting, better energy efficiency and more design freedom to our cars. All thanks to progress with Photonics.

opencc-by-4.0Oct 2022View details →

I-CAR door crashworthiness validation

During an impact, a key component to protect the occupants of a vehicle is the door. In line with the novel design and manufacturing process of vehicle doors which has been carried out in AVANGARD project, several crash tests on the developed vehicle were carried out to demonstrate the effectiveness of these actions. The results of tests were satisfactory, but especially for the doors in the case of the side impact test. Considering that it is the most critical case for the door, it is considered a great success. In both tests, due to the low weigh of the structure, the vehicle is ejected, resulting in only a small deformation of the structure. In addition, owing to the doors are very rigid, there is almost no intrusion.

opencc-by-4.0Oct 2022View details →

Experimental fatigue test on I-CAR structure

In order to validate the welding procedures developed during AVANGARD project, a fatigue test of the vehicle structure was performed. As can be seen in the video, the vehicle underwent the entire test without visible damage. On the other hand, once the test was completed, non-destructive inspection with penetrate liquids verified that there were no cracks. The quality of the joints is therefore corroborated.

opencc-by-4.0Jun 2024View details →

Carli Zoeller (z0718)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Carli Zoeller musiXplora-ID: z0718 musiXplora-URI: <a href="https://musixplora.de/mxp/z0718">https://musixplora.de/mxp/z0718</a> Gender: m Date of Birth: 26 March 1840 Place of Birth: Berlin Date of Death: 13 July 1889 Place of Death: London First Mentioned: 1873 Professions (Musical): Dirigent, Instrumentensammler, Komponist Other Places of Activity: London Ausbildung: <table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>LehrerInnen und AusbilderInnen</td><td>Schüler</td><td>Hubert Ries</td><td><a href="https://musixplora.de/mxp/r0584">r0584</a></td></tr></tbody></table> Changelog:   - v0.0.1: Initial Upload.

opencc-by-4.0Feb 2023View details →

Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes

Background Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc. A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation). <a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a> Methodology This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden. This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7). Data description The current data product covers seven data files. (1) Agents' experienced activity plans File name: 1_activity_plans.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> act_id </td> <td> Activity index of each agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> deso </td> <td> Zone code of Demographic statistical areas (DeSO)1 </td> <td> String </td> <td> - </td> </tr> <tr> <td> POINT_X </td> <td> Coordinate X of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> POINT_Y </td> <td> Coordinate Y of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> act_purpose </td> <td> Activity purpose (work, home, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> mode </td> <td> Transport mode to reach the activity location (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> dep_time </td> <td> Departure time in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> trav_time </td> <td> Travel time to reach the activity location </td> <td> String </td> <td> hour:minute:second </td> </tr> <tr> <td> trav_time_min </td> <td> Travel time in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> speed </td> <td> Travel speed to reach the activity location </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> distance </td> <td> Travel distance between the origin and the destination </td> <td> Float </td> <td> km </td> </tr> <tr> <td> act_start </td> <td> Start time of activity in minute (0-1439) </td> <td> Integer </td> <td> minute </td> </tr> <tr> <td> act_time </td> <td> Activity duration in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> act_end </td> <td> End time of activity in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> score </td> <td> Utility score of the simulation day given by MATSim </td> <td> Float </td> <td> - </td> </tr> </tbody> </table> 1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a>   (2) Travel trajectories File name: 2_input_zip Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> time </td> <td> Time in second in a simulation day (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation2 </td> <td> String </td> <td> - </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> link </td> <td> Nearest road link consistent with (5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> vehicle </td> <td> Vehicle ID identical to person </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table> 2 One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)   (3) Overnight charger access File name: 3_home_charger_access.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table>   (4) Car fleet composition File name: 4_car_fleet.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> income_class </td> <td> Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> </tbody> </table>   (5) Road network with slope information File name: 5_road_network_with_slope.shp (5 files in total) <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> length </td> <td> The length of road link </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> freespeed </td> <td> Free speed </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> capacity </td> <td> Number of vehicles </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> permlanes </td> <td> Number of lanes </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> oneway </td> <td> Whether the segment is one-way (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> modes </td> <td> Transport mode (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> link_id </td> <td> Link ID </td> <td> String </td> <td> - </td> </tr> <tr> <td> from_node </td> <td> Start node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> to_node </td> <td> End node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> count </td> <td> Aggregated traffic (number of cars travelled per day) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> slope </td> <td> Slope in percent from -6% to 6% </td> <td> Float </td> <td> - </td> </tr> <tr> <td> geometry </td> <td> LINESTRING (SWEREF99TM) </td> <td> geometry </td> <td> meter </td> </tr> </tbody> </table>   (6) Simulation scenarios specifying the parameter sets File name: 6_scenarios.txt <table> <tbody> <tr> <td> Parameter set (paraset) </td> <td> Strategy 1 </td> <td> Strategy 2 </td> <td> Strategy 3 </td> <td> Fast charging power (kW) </td> <td> Minimum parking time for charging (min) </td> <td> Intermediate charging power (kW) </td> </tr> <tr> <td> 0 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 1 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 2 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 3 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> </tbody> </table>   (7) Time history of travel trajectories and charging of the simulated BEVs File name: 7_output.zip Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> <tr> <td> seq </td> <td> Sequence ID of time history by agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> time </td> <td> Time (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> purpose </td> <td> Valid for activities (home, work, school, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation </td> <td> String </td> <td> - </td> </tr> <tr> <td> link </td> <td> Link ID (link_id in File 5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> distance_driven </td> <td> Cumulative driven distance in the simulation day </td> <td> Float </td> <td> km </td> </tr> <tr> <td> energy_1 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 1) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_2 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 2) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_3 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 3) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> charger_1 </td> <td> Power rating of the charger (Strategy 1) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_2 </td> <td> Power rating of the charger (Strategy 2) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_3 </td> <td> Power rating of the charger (Strategy 3) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> soc_1 </td> <td> State of charge (0-1, Strategy 1) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_2 </td> <td> State of charge (0-1, Strategy 2) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_3 </td> <td> State of charge (0-1, Strategy 3) </td> <td> Float </td> <td> - </td> </tr> </tbody> </table>

Brainport, Urban driving, car rebalancing, VRU detection, rerouting

Scenario description: Precondition: - AD Vehicle (3177) is at starting position - VRU with Smartphone (3120) is at end position - VRU with Smartphone (3121) and FlowRadar (3175) is positioned halfway the vehicle track - CEMA device is on route 2 and detecting crowd Initiate Rebalancing phase: 1. Smartphone 3120: send taxi request (send end location to vehicle (lon / lat)) Initiate Routing phase: 2. CEMA devices detect crowds on route 1 or route 2 --> dynamically change routes base on CEMA 3. Motion planning: Vehicle uses CEMA detection and call taxi request as input for choosing one of the two routes Initiate Urban Driving phase (incl. VRU detection) 4. Vehicle starts driving 5. Underway VRU (3121) is detected using smartphone and FlowRadar ITS-G5 6. With GeoFenching, vehicle starts slowing down on smartphone 7. With camera detection vehicle brakes when VRU is inline of camera 8. After VRU passes, vehicle continues journey 9. Vehicle arrives at requested end point (at VRU 3120). Session description: Rebalancing: - relocate the vehicle on request Urban Driving: - rerouting on crowd estimation (CEMA) - VRU detection with GeoFenching in vehicle (speed reduction) and warning on VRU smartphone Datasets descriptions: AUTOPILOT_BrainPort_UrbanDriving_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle AUTOPILOT_BrainPort_UrbanDriving_EAI2Mobile: Data from the service to the mobile Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position AUTOPILOT_BrainPort_UrbanDriving_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_UrbanDriving_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_UrbanDriving_IOT_CEMA_Message: Data from the service to the vehicle Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message: Data from the vehicle to the service Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle AUTOPILOT_BrainPort_UrbanDriving_IotVehicleMessage: Data sent between all devices, vehicles and services 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. AUTOPILOT_BrainPort_UrbanDriving_IOT_VehicleStatus: Data sent from the vehicle to the service Dataset Description This dataset contains the current status of the vehicle AUTOPILOT_BrainPort_UrbanDriving_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS AUTOPILOT_BrainPort_UrbanDriving_SmartphoneGPS: Data sent by the mobile to the service Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus: Data sent from the mobile to the service Dataset Description This dataset contains the current status of the mobile AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest: Data sent from the mobile to the service Dataset Description This dataset contains the requests for a taxi from the mobile phones AUTOPILOT_BrainPort_UrbanDriving_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_UrbanDriving_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

Brainport, Highway pilot, car in manual mode, camera detection

Scenario description: The detection car drives around the track in manual mode, with Camera detection ON. Session description: 25 laps with VW Tiguan on Automotive Campus Test Track Datasets descriptions: AUTOPILOT_BrainPort_HighwayPilot_AdasCommand: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle AUTOPILOT_BrainPort_HighwayPilot_Anomaly: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_HighwayPilot_Hazard: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage: Data sent between all devices, vehicles and services 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. AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_HighwayPilot_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

Brainport, Highway pilot, car in manual mode, but receiving adaption instructions

Scenario description: The driving adaptation car is driven around the track in manual mode, but driving instructions are communicated to the driver. Session description: 12 laps with Jaguar F-Pace on Automotive Campus Test Track Datasets descriptions: AUTOPILOT_BrainPort_HighwayPilot_AdasCommand: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle AUTOPILOT_BrainPort_HighwayPilot_Anomaly: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_HighwayPilot_Hazard: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage: Data sent between all devices, vehicles and services 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. AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_HighwayPilot_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

Brainport, Highway pilot, car in manual mode, IMU detection

Scenario description: The detection car drives around the track in manual mode, with IMU detection ON. Session description: 25 laps with VW Tiguan on Automotive Campus Test Track Datasets descriptions: AUTOPILOT_BrainPort_HighwayPilot_AdasCommand: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle AUTOPILOT_BrainPort_HighwayPilot_Anomaly: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_HighwayPilot_Hazard: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage: Data sent between all devices, vehicles and services 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. AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_HighwayPilot_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

Brainport, Highway pilot, detection car reference laps

Scenario description: 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. Session description: 25 laps with VW Tiguan on Automotive Campus Test Track Datasets descriptions: AUTOPILOT_BrainPort_HighwayPilot_AdasCommand: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle AUTOPILOT_BrainPort_HighwayPilot_Anomaly: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_HighwayPilot_Hazard: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage: Data sent between all devices, vehicles and services 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. AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_HighwayPilot_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

Brainport, Highway pilot, detection car, manual mode, camera and IMU detection on

Scenario description: The detection car drives around the track in manual mode, with Camera and IMU detection ON. Session description: 25 laps with VW Tiguan on Automotive Campus Test Track Datasets descriptions: AUTOPILOT_BrainPort_HighwayPilot_AdasCommand: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle AUTOPILOT_BrainPort_HighwayPilot_Anomaly: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_HighwayPilot_Hazard: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage: Data sent between all devices, vehicles and services 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. AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_HighwayPilot_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

Livorno, Highway pilot, only connected cars

Scenario description: Precondition: 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. Actions or events: 1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone. 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. 3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed. 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). Session description: 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. Datasets description: AUTOPILOT_Livorno_HighwayPilot_Vehicle_all: Data generated from the vehicle sensors 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 AUTOPILOT_Livorno_HighwayPilot_V2X_all: V2V messages during the Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. AUTOPILOT_Livorno_HighwayPilot_IoT_all: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.

Livorno, Highway pilot, data management connected car

Scenario description: Dynamic speed adaptation due to puddle on the road Precondition: 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. Actions or events: 1 The puddle monitoring system of the highway trigger a puddle hazard warning for a specific extended zone. 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. 3 At the end of dangerous area, as notified by the «smart road», the vehicle will recover the legally allowed cruise speed. 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). Session description: 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. Datasets description: AUTOPILOT_Livorno_HighwayPilot_Vehicle_all: Data generated from the vehicle sensors 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 AUTOPILOT_Livorno_HighwayPilot_V2X_all: V2V messages during the Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. AUTOPILOT_Livorno_HighwayPilot_IoT_all: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.

Livorno, Highway pilot, one automated car, two connected cars, smart highway

Scenario description: Precondition: 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. Actions or events:          1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform. 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. 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 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 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). Session description: Test session with one AD+connected car and two connected cars, lap of 11,6 km on the highway. Datasets description: AUTOPILOT_Livorno_HighwayPilot_Vehicle_all: Data generated from the vehicle sensors 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 AUTOPILOT_Livorno_HighwayPilot_V2X_all: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. AUTOPILOT_Livorno_HighwayPilot_IoT_all: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.