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403 Research products, page 1 of 41

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  • 2012-2021
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  • Open Access
    Authors: 
    Vrscaj, Darja;
    Publisher: 4TU.Centre for Research Data
    Country: Netherlands

    In this paper, we researched user attitudes and ethical issues of the cars of the future beyond automation. In order to assess adolescents��� attitudes regarding the car of the future as presented by car manufacturers, we conducted a survey with over 200 participants. We made a 7.47 minute video collage from well-known car brands��� promotional material, which we showed to the survey participants in order to analyze their responses. The video features 10 different car manufacturers, including BMW, Mercedes-Benz, Google car, Rolls Royce, Amber mobility, Volkswagen, Byton, Nissan, and Honda. Our respondents appeared to be more concerned about other aspects of the car of the future than automation. Instead, their most commonly raised ethical concerns were the extensive use of AI, recommender systems, and related issues of autonomy and personal privacy. In line with Responsible Research and Innovation (RRI) as well as Ethically Aligned Design (EAD) frameworks, we were able to identify the misalignment between what is currently imagined as the car of the future and the values held by prospective users, the challenges facing responsible governance, and suggest how to develop more desirable cars for the future

  • Research data . 2020 . Embargo End Date: 01 Aug 2020
    Open Access
    Authors: 
    Elsahly, Osama;
    Publisher: Mendeley
    Country: Netherlands

    In order to determine the saturation flow rate at signalized intersections in the UAE, this research used data that was collected from two intersections in one of the major cities in UAE (Sharjah) by video cameras. Based on data for 154 cycles, The mean saturation flow rate for through lane, shared lane and left lane groups are 2104, 1904 and 1603 vehicles per hours, respectively. The results show that the saturation flow rate for through lanes is 2,104 vehicles per hour, which is higher than the ideal value suggested by HCM by about 11%. Therefore, using the value suggested by the HCM can underestimate the capacity of signalized intersections in the UAE.

  • Open Access English
    Authors: 
    Herrmann, Martin; Strohbeck, Jan; M��ller, Johannes; Tsaregorodtsev, Alexander; Mertens, Max; Buchholz, Michael;
    Publisher: Universität Ulm
    Country: Germany
    Project: EC | ICT4CART (768953)

    Urban intersections are often unclear due to occlusions by buildings, vegetation, and other traffic participants. Thus, crossing an intersection or merging into a priority road at an intersection is a complex task that challenges human drivers and automated vehicles. Furthermore, traffic light signals may hamper the traffic flow leading to unnecessary energy consumption. Altogether, urban intersections are an accident hotspot with a high risk for fatalities of vulnerable road users like pedestrians or cyclists. The presented video shows results from the ICT4CART project, where two intersections near Ulm were equipped with intelligent infrastructure. One is a signalized intersection, broadcasting signal state, and prediction information to connected vehicles. They can adjust their trajectory to improve driving comfort and energy consumption based on this information. The other intersection is unsignalized and very unclear due to occlusions. However, the intelligent infrastructure consisting of ten monocular cameras detects all traffic participants, tracks their state over time, and broadcasts an environment model with this information to connected vehicles. A predictive motion planning can use this information to adapt the vehicle's trajectory to improve driving comfort, energy consumption, and safety of all traffic participants close to the vehicle.

  • Open Access
    Authors: 
    Straka, Milan;
    Publisher: Mendeley

    This is the data associated with the publication entitled "Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data". The link to the ArXiv pre-print is: https://arxiv.org/abs/2006.01672 The data was compiled from various GIS public data sources, as well as from the EVnetNL dataset (please refer to the Supplementary Information of the publication). Feature matrix, without charging pool features, is in the file "feature_matrix.csv"; matrix containing only charging pool features is in the file "chargin_pool_features.csv". Columns correspond to features. The latitude and longitude are rounded to one decimal place, due to business confidentiality reason. Response vector is in the file "energy_log_response.csv" and the value corresponds to the logarithm of consumed energy at the charging pools transformed using the standardization (mean = 0, standard deviation = 1). The first column in all the files is the charging pool identifier, however, the response vector can be matched with matrices by rows. Brief description of the feature matrix is in the file "predictor_desc_pub.csv". For the full description of features please refer to the Supplementary Information of the publication.

  • Open Access
    Authors: 
    He, Chao;
    Publisher: Data Archiving and Networked Services (DANS)
    Country: Netherlands

    This data is experimental data for the paper "Online Estimation of Driving Range for Battery Electric Vehicles Based on Battery State-Of-Charge and Actual Driving Cycle". The data include the driving cycle data collected from a battery electric vehicle operating on actual roads in Beijing, China in 2018 and 2019.

  • Open Access
    Authors: 
    Stoyanovich, Sawyer; Zeyu Yang; Hanson, Mark; Hollebone, Bruce P; Orihel, Diane M; Palace, Vince; Rodriguez-Gil, Jose R; Faragher, Robert; Fatemah S Mirnaghi; Keval Shah; +1 more
    Publisher: Wiley
    Project: NSERC

    The main petroleum product transported through pipelines in Canada is diluted bitumen (dilbit), a semi-liquid form of heavy crude oil mixed with natural gas condensates to facilitate transport. The weathering, fate, behaviour, and environmental effects of dilbit are crucial to consider when responding to a spill, however few environmental studies on dilbit have been completed. Here we report on 11-day long experimental spills of dilbit (Cold Lake Winter Blend) in outdoor micro-cosms meant to simulate a low-energy aquatic system containing natural lake water and sedi-ments treated with a low (1:8,000 oil:water) and high (1:800 oil:water) volume of dilbit. In the first 24 hours of the experiment, volatile hydrocarbons quickly evaporated from the dilbit, result-ing in increased dilbit density and viscosity. These changes in dilbit’s physical and chemical properties ultimately led to its submergence after 8 days. We also detected rapid accumulation of polycyclic aromatic compounds in the water column of the treated-microcosms following the spills. Our study provides new information on the environmental fate and behaviour of dilbit in a freshwater environment that will be critical to environmental risk assessments of proposed pipe-line projects. In particular, our study demonstrates the propensity for dilbit to sink under ambient environmental conditions in fresh waters typical of many boreal lakes.

  • Open Access English
    Authors: 
    Haas, Christian; Casey, Alec; Lensu, Mikko;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | SAFEWIN (233884)
  • Open Access
    Authors: 
    AVR; CNIT; LINKS; TIM;
    Publisher: Zenodo
    Project: EC | AUTOPILOT (731993), EC | AUTOPILOT (731993)

    Scenario description: Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light. Session description: 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. 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. Datasets descriptions: AUTOPILOT_Livorno_UrbanDriving_Vehicle_all: Data generated from the vehicle sensors 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 AUTOPILOT_Livorno_UrbanDriving_V2X_all: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. AUTOPILOT_Livorno_UrbanDriving_IoT_all: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.

  • Open Access
    Authors: 
    Milovanoff, Alexandre; Posen, I. Daniel; MacLean, Heather L.;
    Publisher: Zenodo

    This repository contains the raw data of the inputs and results presented in the paper "Will technology save the climate? The challenge of light-duty vehicle fleet electrification" published in Nature Climate Change (2020) by Alexandre Milovanoff, I. Daniel Posen, and Heather L. MacLean (Department of Civil & Mineral Engineering, University of Toronto).

  • Open Access
    Authors: 
    TNO; TASS; NXP; Technolution; NEVS (National Electric Vehicle Sweden);
    Publisher: Zenodo
    Project: EC | AUTOPILOT (731993), EC | AUTOPILOT (731993)

    Scenario description: Platoon formation and platooning, from Helmond to Eindhoven and back to the Automotive Campus. - Starting in urban area with speed limits of 15 and 30 km/h. - Driving East on the Europaweg with speed limits of 50 and 70 km/h. This includes 3 crossings with traffic lights. - Driving on the the N270, along the Automotive Campus. One crossing with traffic lights, just before the A270. - Driving on the A270 (speed limit 100 km/h). Interrupted by one traffic light. - U-turn at the fly-over or at the end of the A270, to return the same way to the Automotive Campus. Session description: Platoon formation and platooning, with live traffic light data included in planner. - Live traffic light data available for planner - Driver uses the Android app - Starting at default locations - Platooning (CACC and lane keeping) on the A270 when possible. Datasets descriptions: AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction: Data extracted from the CAN of the vehicle This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_Platooning_IotVehicleMessage: Data sent between all devices, vehicles and services 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_Platooning_PlatoonFormation: Data sent from PlatoonService to vehicle This dataset contains information about the route and speed for a specific vehicle for forming a platoon AUTOPILOT_BrainPort_Platooning_PlatooningAction: Data logged by vehicle This dataset contains information about the current status of the platooning AUTOPILOT_BrainPort_Platooning_PlatooningEvent: Data logged by vehicle This dataset contains information about the identifiers used for each specific platooning event AUTOPILOT_BrainPort_Platooning_PlatoonStatus: Data sent by vehicle to PlatoonService This dataset contains information about the current status of the platooning AUTOPILOT_BrainPort_Platooning_PositioningSystem: Data from GPS on the vehicle This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_Platooning_PositioningSystemResample: Data from GPS on the vehicle This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds AUTOPILOT_BrainPort_Platooning_PSInfo: Data sent by PlatoonService to the vehicle This dataset contains speed and route information for the vehicle to create a platoon AUTOPILOT_BrainPort_Platooning_Target: Data from sensors on the vehicle Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor AUTOPILOT_BrainPort_Platooning_Vehicle: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_Platooning_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors AUTOPILOT_BrainPort_Platooning_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

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