scholarly journals Communications and Driver Monitoring Aids for Fostering SAE Level-4 Road Vehicles Automation

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 228 ◽  
Author(s):  
Felipe Jiménez ◽  
José Naranjo ◽  
Sofía Sánchez ◽  
Francisco Serradilla ◽  
Elisa Pérez ◽  
...  

Road vehicles include more and more assistance systems that perform tasks to facilitate driving and make it safer and more efficient. However, the automated vehicles currently on the market do not exceed SAE level 2 and only in some cases reach level 3. Nevertheless, the qualitative and technological leap needed to reach level 4 is significant and numerous uncertainties remain. In this sense, a greater knowledge of the environment is needed for better decision making and the role of the driver changes substantially. This paper proposes the combination of cooperative systems with automated driving to offer a wider range of information to the vehicle than on-board sensors currently provide. This includes the actual deployment of a cooperative corridor on a highway. It also takes into account that in some circumstances or scenarios, pre-set or detected by on-board sensors or previous communications, the vehicle must hand back control to the driver, who may have been performing other tasks completely unrelated to supervising the driving. It is thus necessary to assess the driver’s condition as regards retaking control and to provide assistance for a safe transition.

2019 ◽  
Vol 3 (2) ◽  
pp. 29 ◽  
Author(s):  
Yannick Forster ◽  
Sebastian Hergeth ◽  
Frederik Naujoks ◽  
Josef Krems ◽  
Andreas Keinath

The development of automated driving will profit from an agreed-upon methodology to evaluate human–machine interfaces. The present study examines the role of feedback on interaction performance provided directly to participants when interacting with driving automation (i.e., perceived ease of use). In addition, the development of ratings itself over time and use case specificity were examined. In a driving simulator study, N = 55 participants completed several transitions between Society of Automotive Engineers (SAE) level 0, level 2, and level 3 automated driving. One half of the participants received feedback on their interaction performance immediately after each use case, while the other half did not. As expected, the results revealed that participants judged the interactions to become easier over time. However, a use case specificity was present, as transitions to L0 did not show effects over time. The role of feedback also depended on the respective use case. We observed more conservative evaluations when feedback was provided than when it was not. The present study supports the application of perceived ease of use as a diagnostic measure in interaction with automated driving. Evaluations of interfaces can benefit from supporting feedback to obtain more conservative results.


Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 277 ◽  
Author(s):  
Christina Kurpiers ◽  
Bianca Biebl ◽  
Julia Mejia Hernandez ◽  
Florian Raisch

In SAE (Society of Automotive Engineers) Level 2, the driver has to monitor the traffic situation and system performance at all times, whereas the system assumes responsibility within a certain operational design domain in SAE Level 3. The different responsibility allocation in these automation modes requires the driver to always be aware of the currently active system and its limits to ensure a safe drive. For that reason, current research focuses on identifying factors that might promote mode awareness. There is, however, no gold standard for measuring mode awareness and different approaches are used to assess this highly complex construct. This circumstance complicates the comparability and validity of study results. We thus propose a measurement method that combines the knowledge and the behavior pillar of mode awareness. The latter is represented by the relational attention ratio in manual, Level 2 and Level 3 driving as well as the controllability of a system limit in Level 2. The knowledge aspect of mode awareness is operationalized by a questionnaire on the mental model for the automation systems after an initial instruction as well as an extensive enquiry following the driving sequence. Further assessments of system trust, engagement in non-driving related tasks and subjective mode awareness are proposed.


Author(s):  
Petter Gottschalk

The chief executive officer (CEO) is the only executive at level 1 in the hierarchy of an organization (Carpenter & Wade, 2002). All other executives in the organization are at lower levels. At level 2, we find the most senior executives. Level 3 includes the next tier of executives. In our perspective of promoting the chief information officer (CIO) to be the next CEO, we first have to understand the role of the CEO. Therefore, the first chapter of this book is dedicated to the topic of CEO successions (Zhang & Rajagopalan, 2004).


2019 ◽  
Vol 11 (3) ◽  
pp. 40-58 ◽  
Author(s):  
Philipp Wintersberger ◽  
Clemens Schartmüller ◽  
Andreas Riener

Automated vehicles promise engagement in side activities, but demand drivers to resume vehicle control in Take-Over situations. This pattern of alternating tasks thus becomes an issue of sequential multitasking, and it is evident that random interruptions result in a performance drop and are further a source of stress/anxiety. To counteract such drawbacks, this article presents an attention-aware architecture for the integration of consumer devices in level-3/4 vehicles and traffic systems. The proposed solution can increase the lead time for transitions, which is useful to determine suitable timings (e.g., between tasks/subtasks) for interruptions in vehicles. Further, it allows responding to Take-Over-Requests directly on handheld devices in emergencies. Different aspects of the Attentive User Interface (AUI) concept were evaluated in two driving simulator studies. Results, mainly based on Take-Over performance and physiological measurements, confirm the positive effect of AUIs on safety and comfort. Consequently, AUIs should be implemented in future automated vehicles.


Author(s):  
Shiyan Yang ◽  
Jonny Kuo ◽  
Michael G. Lenné

Objective The paper aimed to investigate glance behaviors under different levels of distraction in automated driving (AD) and understand the impact of distraction levels on driver takeover performance. Background Driver distraction detrimentally affects takeover performance. Glance-based distraction measurement could be a promising method to remind drivers to maintain enough attentiveness before the takeover request in partially AD. Method Thirty-six participants were recruited to drive a Tesla Model S in manual and Autopilot modes on a test track while engaging in secondary tasks, including temperature-control, email-sorting, and music-selection, to impose low and high distractions. During the test drive, participants needed to quickly change the lane as if avoiding an immediate road hazard if they heard an unexpected takeover request (an auditory warning). Driver state and behavior over the test drive were recorded in real time by a driver monitoring system and several other sensors installed in the Tesla vehicle. Results The distribution of off-road glance duration was heavily skewed (with a long tail) by high distractions, with extreme glance duration more than 30 s. Moreover, being eyes-off-road before takeover could cause more delay in the urgent takeover reaction compared to being hands-off-wheel. Conclusion The study measured off-road glance duration under different levels of distraction and demonstrated the impacts of being eyes-off-road and hands-off-wheel on the following takeover performance. Application The findings provide new insights about engagement in Level 2 AD and are useful for the design of driver monitoring technologies for distraction management.


2015 ◽  
Vol 25 (8) ◽  
pp. 1631-1636 ◽  
Author(s):  
Jeffrey P. Jacobs ◽  
Gil Wernovsky ◽  
David S. Cooper ◽  
Tom R. Karl

AbstractIn the domain of paediatric and congenital cardiac care, the stakes are huge. Likewise, the care of these children assembles a group of “A+ personality” individuals from the domains of cardiac surgery, cardiology, anaesthesiology, critical care, and nursing. This results in an environment that has opportunity for both powerful collaboration and powerful conflict. Providers of healthcare should avoid conflict when it has no bearing on outcome, as it is clearly a squandering of individual and collective political capital.Outcomes after cardiac surgery are now being reported transparently and publicly. In the present era of transparency, one may wonder how to balance the following potentially competing demands: quality healthcare, transparency and accountability, and teamwork and shared decision-making.An understanding of transparency and public reporting in the domain of paediatric cardiac surgery facilitates the implementation of a strategy for teamwork and shared decision-making. In January, 2015, the Society of Thoracic Surgeons (STS) began to publicly report outcomes of paediatric and congenital cardiac surgery using the 2014 Society of Thoracic Surgeons Congenital Heart Surgery Database (STS-CHSD) Mortality Risk Model. The 2014 STS-CHSD Mortality Risk Model facilitates description of Operative Mortality adjusted for procedural and patient-level factors.The need for transparency in reporting of outcomes can create pressure on healthcare providers to implement strategies of teamwork and shared decision-making to assure outstanding results. A simple strategy of shared decision-making was described by Tom Karl and was implemented in multiple domains by Jeff Jacobs and David Cooper. In a critical-care environment, it is not unusual for healthcare providers to disagree about strategies of management of patients. When two healthcare providers disagree, each provider can classify the disagreement into three levels:• SDM Level 1 Decision: “We disagree but it really does not matter, so do whatever you desire!”• SDM Level 2 Decision: “We disagree and I believe it matters, but I am OK if you do whatever you desire!!”• SDM Level 3 Decision: “We disagree and I must insist (diplomatically and politely) that we follow the strategy that I am proposing!!!!!!”SDM Level 1 Decisions and SDM Level 2 Decisions typically do not create stress on the team, especially when there is mutual purpose and respect among the members of the team. SDM Level 3 Decisions are the real challenge. Periodically, the healthcare team is faced with such Level 3 Decisions, and teamwork and shared decision-making may be challenged. Teamwork is a learned behaviour, and mentorship is critical to achieve a properly balanced approach. If we agree to leave our egos at the door, then, in the final analysis, the team will benefit and we will set the stage for optimal patient care. In the environment of strong disagreement, true teamwork and shared decision-making are critical to preserve the unity and strength of the multi-disciplinary team and simultaneously provide excellent healthcare.


Author(s):  
Shiyan Yang ◽  
Kyle M. Wilson ◽  
Trey Roady ◽  
Jonny Kuo ◽  
Michael G. Lenné

Objective This study aimed to investigate the impacts of feature selection on driver cognitive distraction (CD) detection and validation in real-world nonautomated and Level 2 automated driving scenarios. Background Real-time driver state monitoring is critical to promote road user safety. Method Twenty-four participants were recruited to drive a Tesla Model S in manual and Autopilot modes on the highway while engaging in the N-back task. In each driving mode, CD was classified by the random forest algorithm built on three “hand-crafted” glance features (i.e., percent road center [PRC], the standard deviation of gaze pitch, and yaw angles), or through a large number of features that were transformed from the output of a driver monitoring system (DMS) and other sensing systems. Results In manual driving, the small set of glance features was as effective as the large set of machine-generated features in terms of classification accuracy. Whereas in Level 2 automated driving, both glance and vehicle features were less sensitive to CD. The glance features also revealed that the misclassified driver state was the result of the dynamic fluctuations and individual differences of cognitive loads under CD. Conclusion Glance metrics are critical for the detection and validation of CD in on-road driving. Applications The paper suggests the practical value of human factors domain knowledge in feature selection and ground truth validation for the development of driver monitoring technologies.


Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 233 ◽  
Author(s):  
Nadja Schömig ◽  
Katharina Wiedemann ◽  
Sebastian Hergeth ◽  
Yannick Forster ◽  
Jeffrey Muttart ◽  
...  

Within a workshop on evaluation methods for automated vehicles (AVs) at the Driving Assessment 2019 symposium in Santa Fe; New Mexico, a heuristic evaluation methodology that aims at supporting the development of human–machine interfaces (HMIs) for AVs was presented. The goal of the workshop was to bring together members of the human factors community to discuss the method and to further promote the development of HMI guidelines and assessment methods for the design of HMIs of automated driving systems (ADSs). The workshop included hands-on experience of rented series production partially automated vehicles, the application of the heuristic assessment method using a checklist, and intensive discussions about possible revisions of the checklist and the method itself. The aim of the paper is to summarize the results of the workshop, which will be used to further improve the checklist method and make the process available to the scientific community. The participants all had previous experience in HMI design of driver assistance systems, as well as development and evaluation methods. They brought valuable ideas into the discussion with regard to the overall value of the tool against the background of the intended application, concrete improvements of the checklist (e.g., categorization of items; checklist items that are currently perceived as missing or redundant in the checklist), when in the design process the tool should be applied, and improvements for the usability of the checklist.


Author(s):  
Betty Belanus

This chapter uses the 2008 Bhutan program to examine visitor experience and the role of curators in crafting these experiences. Using the suggestive possibilities of a portable Buddhist shrine featured in the program and basing the analysis on 20 years of experience working with the Festival and its evaluation, the author lays out a framework for analyzing four levels of Festival visitor engagement: Level 1—Sensory Cultural Enlightenment/Being Present; Level 2—Choosing a Route/Moving Through; Level 3—Viewing and Taking part in the Live Performance/Active Experiencing; and Level 4—Revealing the Deeper Layers/Discovering More. The chapter concludes with observations about visitor studies relevant to museums as well as festivals.


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