Intelligent driver profiling system for cars – a basic concept

Many industries have been transformed by the provision of service solutions characterised by personalisation and customisation - most dramatically the development of the iPhone. Personalisation and customisation stand to make an impact on cars and mobility in comparable ways. The automobile industry has a major role to play in this change, with moves towards electric vehicles, auton-omous cars, and car sharing as a service. These developments are likely to bring disruptive changes to the business of car manufacturers as well as to drivers. However, in the automobile industry, both the user's preferences and demands and also safety issues need to be confronted since the frequent use of different makes and models of cars, implied by car sharing, entails several risks due to variations in car controls depending on the manufacturer. Two constituencies, in particular, are likely to experience even more difficulties than they already do at present, namely older people and those with capability variations. To overcome these challenges, and as a means to empower a wide car user base, the paper here presents a basic concept of an intelligent driver profiling system for cars: the sys-tem would enable various car characteristics to be tailored according to individual driver-dependent profiles. It is intended that wherever possible the system will personalise the characteristics of individual car components; where this is not possible, however, an initial customisation will be performed

Supporting dynamic change detection: using the right tool for the task

Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness—the failure to notice visual changes—is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one’s own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142–153, 2011; J. Exp. Psychol. Appl. 19:403–419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition

Workload measures—recent trends in the driving context

This work is the follow-up of previous research where the authors postulated the need for the establishment of a standardized methodology for assessing the driver’s workload, given its importance in the driving context and the upcoming shift in the driving paradigm, namely the widespread use of conditional autonomous vehicles. Even though the early research devoted to this matter was somewhat scattered, a bottleneck in the scope of the dedicated literature seemed to begin to appear in the latter years. As such, the authors aimed to search for the trends in the use of workload measures within this scope, in a recent timeframe. Indeed, this convergence may unveil eventual best practices resulting from the researchers’ effort to cope with this recognized handicap in the decision on the best choice regarding workload measures. The results obtained are believed to be indicative of the best path for the standardisation of the method. A systematic literature review was conducted and it was found that there is a growing tendency to simultaneously apply all three workload measures (subjective, physiological and performance), as means to achieve redundant, comparable and more reliable results. Among the specific measures of workload, the most frequently used subjective measure is the NASA-TLX, whereas the HR-related measures are the most frequently used among the physiological measures and the most frequent performance measure is the primary driving task activity.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) Project nº 002797, Funding Reference: POCI-01-0247-FEDER-002797

Through the Google Glass: The impact of heads-up displays on visual attention

In five experiments, we evaluated how secondary information presented on a heads-up display (HUD) impacts performance of a concurrent visual attention task. To do so, we had participants complete a primary visual search task under a variety of secondary load conditions (a single word presented on Google Glass during each search trial). Processing of secondary information was measured through a recognition memory task. Other manipulations included relevance (Experiments 1–4) and temporal onset of secondary information relative to the primary task (Experiment 3). Secondary information was always disruptive to the visual search, regardless of temporal onset and even when participants were instructed to ignore it. These patterns were evident in search tasks reflective of both selective (Experiments 1–3) and preattentive (Experiment 4) attentional mechanisms, and were not a result of onset-offset attentional capture (Experiment 5). Recognition memory for secondary information was always above chance. Our findings suggest that HUD-based visual information is profoundly disruptive to attentional processes and largely immune to user-centric prioritization

Decision Making in Concurrent Multitasking:Do People Adapt to Task Interference?

<p>While multitasking has received a great deal of attention from researchers, we still know little about how well people adapt their behavior to multitasking demands. In three experiments, participants were presented with a multicolumn subtraction task, which required working memory in half of the trials. This primary task had to be combined with a secondary task requiring either working memory or visual attention, resulting in different types of interference. Before each trial, participants were asked to choose which secondary task they wanted to perform concurrently with the primary task. We predicted that if people seek to maximize performance or minimize effort required to perform the dual task, they choose task combinations that minimize interference. While performance data showed that the predicted optimal task combinations indeed resulted in minimal interference between tasks, the preferential choice data showed that a third of participants did not show any adaptation, and for the remainder it took a considerable number of trials before the optimal task combinations were chosen consistently. On the basis of these results we argue that, while in principle people are able to adapt their behavior according to multitasking demands, selection of the most efficient combination of strategies is not an automatic process.</p>