| 1. |
- Barnard, Yvonne, et al.
(författare)
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Selection of operator support systems across modes
- 2010
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This Deliverable provides an outline of the process involved in task 2.2 of the ITERATE project forselecting an appropriate set of hypotheses to be tested inWorkpackages 3 (Experimental design andscenario specification), and 4 (Experimental studies).During a workshop with all partners involved in the project, hypotheses were formulated for thethree main system functionalities outlined in D2.1: those that support speed management (SpeedManagement), systems that support system object detection and avoidance (Collision Avoidance)and those that monitor operator state (Operator State). In formulating these hypotheses, partnerswere encouraged to consider systematically the effect of operator state and different operatorgroups on interaction with these three systems. To allow succinct formulation of hypotheses, fouroperator based parameters which are thought to affect operator behaviour with the system, wereidentified: sensation-seeking, hazard perception, fatigue and (high and low) task demand. Informulating hypotheses, partnerswere encouraged to consider the whole process linking a cause toan effect, with a clear mechanism that would link the two.
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| 2. |
- Barnard, Yvonne, et al.
(författare)
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Specification of test procedures for the simulator experiments
- 2010
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In Deliverable 3.1 of Workpackage 3, we discuss the methodology developed and applied in the European ITERATEproject (IT for Error Remediation And Trapping Emergencies). This methodology has as its objective to design experiments that will provide data to seed the ITERATE model. In the ITERATE project a high-level theoretical model of vehicle operator behaviour has been developed in Workpackage 1, specifying the factors that play a role in the influence of innovative support systems on vehicle operation in potentially dangerous situations. Themodel isapplicable for different surface transport modes: road vehicles, rail transport and ships. Themodel will be calibrated by experiments investigating how the different factors interact. One hundred and sixty car drivers and 160 train drivers in five countries will drive with a static driving simulator, and 64 drivers (both train and car) with full motion simulators. Finally an executable simulation model will be constructed with the aim to predict the effects of support systems on operator behaviour and risk.
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| 3. |
- Innamaa, Satu, et al.
(författare)
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Methodological challenges related to real-world automated driving pilots
- 2019
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Ingår i: ITS World Congress Proceedings.
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Konferensbidrag (refereegranskat)abstract
- This paper discusses the methodological challenges related to automated driving (AD) pilots in the realworld, providing an overview of some of the solutions offered by the L3Pilot project. Although theoverall methodology defined for Field Operational Tests (FOTs) has been developed quite extensivelyfor driver support systems, our efforts in the L3Pilot project show that the evaluation process can beadapted to suit the needs of AD pilot projects, as long as some caveats related to the pilot nature of ADstudies are acknowledged. The AD pilots currently in place around the world provide important insightsinto t he impacts of AD on their us ers, other road us ers and the societ y at large. However, as thes e s yst emsmature, large-scale FOTs will be needed as (closer to) ex-post evaluation, to verify the assessed impacts.This paper outlines the challenges and offers some solutions for working towards that goal.
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| 4. |
- Kyriakidis, Miltos, et al.
(författare)
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A human factors perspective on automated driving
- 2019
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Ingår i: Theoretical Issues in Ergonomics Science. - : Taylor & Francis. - 1463-922X .- 1464-536X. ; 20:3, s. 223-249
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Tidskriftsartikel (refereegranskat)abstract
- Automated driving can fundamentally change road transportation and improve quality of life. However, at present, the role of humans in automated vehicles (AVs) is not clearly established. Interviews were conducted in April and May 2015 with 12 expert researchers in the field of human factors (HFs) of automated driving to identify commonalities and distinctive perspectives regarding HF challenges in the development of AVs. The experts indicated that an AV up to SAE Level 4 should inform its driver about the AV's capabilities and operational status, and ensure safety while changing between automated and manual modes. HF research should particularly address interactions between AVs, human drivers and vulnerable road users. Additionally, driver-training programmes may have to be modified to ensure that humans are capable of using AVs. Finally, a reflection on the interviews is provided, showing discordance between the interviewees’ statements – which appear to be in line with a long history of HFs research – and the rapid development of automation technology. We expect our perspective to be instrumental for stakeholders involved in AV development and instructive to other parties.
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| 5. |
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| 6. |
- Merat, Natasha, et al.
(författare)
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An Overview of Interfaces for Automated Vehicles (inside/outside) (Deliverable D2.1 in the H2020 MSCA ITN project SHAPE-IT)
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This Deliverable starts with a short overview of the design principles and guidelines developed for current Human Machine Interfaces (HMIs), which are predominantly developed for manually driven vehicles, or those with a number of Advanced Driver Assistance Systems (ADAS), at SAE Levels 0 and 1 (SAE, 2018). It then provides an overview of how the addition of more capable systems, and the move to higher levels of vehicle automation, is changing the role the human inside an Automated Vehicle (AV), and the ways in which future automated vehicles at higher levels of automation (SAE level 4 and 5) must communicate with other road users, in the absence of an “in charge” human driver. It is argued that such changes in the role of the driver, and more transfer of control to the AV and its different functionalities, means that there will be more emphasis on the roles and responsibilities of HMIs for future AVs. In parallel, the multifaceted nature of these HMI, presented from different locations, both in and outside the vehicles, using a variety of modalities, and engaging drivers in a two-way interaction, means that a new set of design guidelines are required, to ensure that the humans interacting with AVs (inside and outside the vehicle) are not distracted and overloaded, that they remain situation aware and understand the capabilities and limitations of the system, having the right mental model of system capabilities and their responsibilities, as responsible road users, at all times Following a summary of suggested frameworks and design principles which highlight the significant change needed for new AV HMIs, an overview of results from studies investigating human interaction with internal (or iHMIs), and external (or eHMIs), is provided, with examples of new and innovative methods of communication between humans and their vehicles. The Deliverable then provides a summary of the innovative approaches that will be tackled by the ESRs of the project, which focus on factors such as use of AI and AR for future design of more intuitive and transparent HMI, studying how HMI can support the long term interaction of humans with AVs, and the use of neuroergonomic methods for developing safer HMIs. The Deliverable concludes by summarising how each ESR’s project contributes to the development of HMIs for future AVs.
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| 7. |
- Merat, Natasha, et al.
(författare)
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Design guidelines for acceptable, transparent, and safe AVs in urban environments : Deliverable 2.6 in the EC ITN project SHAPE-IT
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This Deliverable summarises the work of ESRs 1, 5, 7 and 12 of the SHAPE-IT project that considered how a range of human states such as attention, fatigue and mental workload are affected by SAE Level 2 and 3 automated vehicles (AVs), when compared to manual driving, and what this means for AV and road safety. The studies also consider how AV safety and acceptance can increase with Human Machine Interface (HMI) transparency, and what factors contribute to the improvement of this transparency. An investigation of what aspects of an AV’s HMIs, its driving environment and driving style, contribute to the perceived safety, comfort and trust for its users is provided, and new methods and frameworks for enhancing these states are introduced. By considering how human factors concepts and knowledge should be embraced by software developers and AV engineers, ESRs 8 and 15 highlight the importance of a multidisciplinary approach to AV development. Finally, the work of ESR2 focuses on how AV trust, acceptance, and transparency changes with prolonged and repeated use of AVs, emphasising that successful deployment of AVs must embrace human factors knowledge during all stages of AV development. This work also highlights that as long as AVs require human interaction and intervention, including a diverse user group, and ensuring the appropriate level of trust is built at each stage of the interaction, will improve the correct and safe use of AVs.
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| 8. |
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| 9. |
- Tabone, Wilbert, et al.
(författare)
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Vulnerable road users and the coming wave of automated vehicles: Expert perspectives
- 2021
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Ingår i: Transportation Research Interdisciplinary Perspectives. - : Elsevier BV. - 2590-1982. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Automated driving research over the past decades has mostly focused on highway environments. Recent technological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human-machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed during the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV-VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV-VRU interaction, but they noted that implicit communication will remain dominant and advised against text-based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakeholders and researchers concerned with the relationship between VRUs and AVs in future urban traffic.
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| 10. |
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| 11. |
- Zhang, Chi, 1992, et al.
(författare)
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Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings
- 2023
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Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. - Anchorage, Alaska, Canada, : IEEE. - 9798350346916 - 9798350346923
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Konferensbidrag (refereegranskat)abstract
- Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features.
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