Response to automatic speed control in urban areas: A simulator study.

Speed affects both the likelihood and severity of an accident. Attempts to reduce speed have centred around road design and traffic calming, enforcement and feedback techniques and public awareness campaigns. However, although these techniques have met with some success, they can be both costly and context specific. No single measure has proved to be a generic countermeasure effective in reducing speed, leading to the suggestion that speed needs to be controlled at the source, i.e. within the vehicle. An experiment carried out on the University of Leeds Advanced Driving Simulator evaluated the effects of speed limiters on driver behavionr. Safety was measured using following behaviour, gap acceptance and traffic violations, whilst subjective mental workload was recorded using the NASA RTLX. It was found that although safety benefits were observed in terms of lower speeds, longer headways and fewer traffic light violations, drivers compensated for loss of time by exhibiting riskier gap acceptance behaviour and delayed braking behaviour. When speed limited, drivers' self-reports indicated that their driving performance improved and less physical effort was required, but that they also experienced increases in feelings of frustration and time pressure. It is discussed that there is a need for a total integrated assessment of the long term effects of speed limiters on safety, costs, energy, pollution, noise, in addition to investigation of issues of acceptability by users and car manufacturers

How to keep drivers engaged while supervising driving automation? A literature survey and categorization of six solution areas

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.submittedVersio

Measuring cognitive load and cognition: metrics for technology-enhanced learning

This critical and reflective literature review examines international research published over the last decade to summarise the different kinds of measures that have been used to explore cognitive load and critiques the strengths and limitations of those focussed on the development of direct empirical approaches. Over the last 40 years, cognitive load theory has become established as one of the most successful and influential theoretical explanations of cognitive processing during learning. Despite this success, attempts to obtain direct objective measures of the theory's central theoretical construct – cognitive load – have proved elusive. This obstacle represents the most significant outstanding challenge for successfully embedding the theoretical and experimental work on cognitive load in empirical data from authentic learning situations. Progress to date on the theoretical and practical approaches to cognitive load are discussed along with the influences of individual differences on cognitive load in order to assess the prospects for the development and application of direct empirical measures of cognitive load especially in technology-rich contexts

Automated driving: A literature review of the take over request in conditional automation

This article belongs to the Special Issue Autonomous Vehicles TechnologyIn conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver's state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions.This work was funded by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) Endowed Professorship for Sustainable Transport Logistics 4.0; the Spanish Ministry of Economy, Industry and Competitiveness under the TRA201563708-R and TRA2016-78886-C3-1-R project; open access funding by the Johannes Kepler University Linz

BENEFITS AND COST OF DUAL-TASKING IN A VIGILANCE TASK: A LABORATORY AND DRIVING SIMULATOR INVESTIGATION

It is believed that under certain conditions, the presence of a secondary task such as a cell phone conversation would minimize a decrease in vigilance. The current study investigates this assumption by using two different vigilance paradigms. Further investigations were done by applying the same secondary task conditions to a monotonous driving scenario in a simulator. Results from the vigilance studies showed robust effects of dual task interference, and improvement in task performance for participants engaged in dual task from beginning to end. It was noted that the benefit of an improvement in task performance did not outweigh its cost as the reported improvement only reached a level similar to that of an individual who was low in vigilance. Results from the driving simulator indicated a possible driving improvement with the presence of a secondary task during later stages of the driving task as indicated by smaller lane keeping variability. The perceived improvement was questioned as there was a significantly poorer recall memory under dual task conditions. In general, it might be suggested that a secondary task may improve task performance under vigilance conditions, but the reported benefit may not outweigh its costs

Drivers’ behaviour modelling for virtual worlds

In this paper we present a study that looks at modelling drivers’ behaviour with a view to contribute to the problem of road rage. The approach we adopt is based on agent technology, particularly multi-agent systems. Each driver is represented by a software agent. A virtual environment is used to simulate drivers’ behaviour, thus enabling us to observe the conditions leading to road rage. The simulated model is then used to suggest possible ways of alleviating this societal problem. Our agents are equipped with an emotional module which will make their behaviours more human-like. For this, we propose a computational emotion model based on the OCC model and probabilistic cognitive maps. The key influencing factors that are included in the model are personality, emotions and some social/personal attributes