Gender Effects on Lane Change Test (LCT) Performance

There are various easy-to-implement, low-cost methodologies for evaluating driver performance under distraction caused by in-vehicle tasks. One of them is the Lane Change Test (LCT), which is currently under consideration for becoming an ISO-standardized procedure. This paper investigates the effect of gender on LCT performance. Although a common procedure in psychological research, balancing for gender is not a requirement made by the ISO draft. However, using data from three LCT experiments, we found gender differences in LCT as well as secondary task performance. We conclude that subject samples balanced for gender are necessary to assure comparability of LCT results

More Than Storage of Information: What Working Memory Contributes to Visual Abductive Reasoning

Abductive reasoning is the process of finding the best explanation for a set of observations. As the number of possible observations and corresponding explanations may be very high, it is commonly accepted that working memory capacity is closely related to successful abductive reasoning. However, the precise relationship between abductive reasoning and working memory capacity remains largely opaque. In a reanalysis of two experiments (N = 59), we first investigated whether reasoning performance is associated with differences in working memory capacity. Second, using eye tracking, we explored the relationship between the facets of working memory and the process of visuospatial reasoning. We used working memory tests of both components (verbal-numerical/spatial) as well as an intelligence measure. Results showed a clear relationship between reasoning accuracy and spatial components as well as intelligence. Process measures suggested that working memory seems to be a limiting factor to reasoning and that looking-back to previously relevant areas is compensating for poor mental models rather than being a sign of a particularly elaborate one. Following, high working memory ability might lead to the use of strategies to optimize the content and complexity of the mental representation on which abductive reasoning is based

The Development of a Cognitive Skills Training to Support Driver Education: Experimental Validation of Theoretical Underpinnings

Crash numbers of novice drivers are, despite best efforts of all involved institutions, alarmingly high. One central explanation refers to deficits in cognitive skills such as hazard perception, which have a tremendous influence on accident involvement of younger drivers. Conventional forms of driver training have largely failed to build up skills that go beyond a rather descriptive knowledge of how to drive. Computer based trainings (CBTs) are assumed to provide new ways of tackling this problem. There are already CBTs available that address relevant issues and are presumed to be effective. However, their evaluations lack evidence for the superiority of the specific features of multimedia based interventions over other forms of training. This shortcoming, in addition to the fact that all available relevant CBTs have been developed within contexts that differs significantly from European conditions in terms of the “average” driving environment as well as the respective educational schemes, has prompted us to develop a new CBT that is intended to complement the existing driver training program by addressing critical cognitive skills. In a first step, we tested the CBTs theoretical validity by comparing the performance in the training itself between learner drivers and experienced drivers. The results show that experienced drivers achieve higher scores in the CBT. We conclude that our application does indeed address relevant cognitive skills that are associated with driving experience

Effects of Take-Over Requests and Cultural Background on Automation Trust in Highly Automated Driving

Appropriate automation trust is a prerequisite for safe, comfortable andefficient use of highly automated driving systems (HADS). Earlier researchindicates that a drivers’ nationality and Take-Over Requests (TOR) due toimperfect system reliability might affect trust, but this has never been investigatedin the context of highly automated driving. A driving simulator study (N = 80)showed that TORs only temporarily lowered trust in HADSs, and revealedsimilarities in trust formation between German and Chinese drivers. Trust wassignificantly higher after experiencing the system than before, both for German andChinese participants. However, Chinese drivers reported significantly higherautomation mistrust than German drivers. Self-report measures of automation trustwere not connected to behavioral measures. The results support a distinctionbetween automation trust and mistrust as separate constructs, short- and long-termeffects of TORs on automation trust, and cultural differences in automation trust

The Development of a Cognitive Skills Training to Support Driver Education – Comparing Performance of Experienced and Trained Learner Drivers

Deficits in cognitive skills such as hazard perception are considered one of the major factors explaining the high numbers of crashes for novice drivers. Computer based trainings (CBTs) have been identified as a potential measure to improve such skills. Several CBTs have been developed since. Some of them have been evaluated, however, only by comparing a treatment group and a control group. While results show that the evaluated CBTs are somewhat effective, it is unclear how an experienced driver would have performed in the test scenarios. We developed our own CBT, and in a first step, evaluated it following the same known strategy (treatment and control group, adding a “paper based training group). Results provided evidence for the assumption that the CBT had a positive effect on learner drivers’ glance behaviour in simulated driving (Petzoldt et al., 2013). However, after we confirmed the effectiveness, we tested a group of experienced drivers on exactly the same simulator scenarios. The comparison between treatment, control and experienced driver group is presented in this paper. Results show comparable patterns of glance behaviour for the treatment group and the experienced drivers, superior to that of the control group. Driving performance rated by experts was mostly appropriate for all groups, with notable exceptions for some scenarios

Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

As technological advances lead to rapid progress in driving automation, human-machine interaction (HMI) issues such as comfort in automated driving gain increasing attention. The research project KomfoPilot at Chemnitz University of Technology aims to assess discomfort in automated driving using physiological parameters from commercially available smartbands, pupillometry and body motion. Detected discomfort should subsequently be used to adapt driving parameters as well as information presentation and prevent potentially safety-critical take-over situations. In an empirical driving simulator study, 40 participants from 25 years to 84 years old experienced two highly automated drives with three potentially critical and discomfort-inducing approaching situations in each trip. The ego car drove in a highly automated mode at 100 km/h and approached a truck driving ahead with a constant speed of 80 km/h. Automated braking started very late at a distance of 9 m, reaching a minimum of 4.2 m. Perceived discomfort was assessed continuously using a handset control. Physiological parameters were measured by the smartband Microsoft Band 2 and included heart rate (HR), heart rate variability (HRV) and skin conductance level (SCL). Eye tracking glasses recorded pupil diameter and eye blink frequency; body motion was captured by a motion tracking system and a seat pressure mat. Trends of all parameters were analyzed 10 s before, during and 10 s after reported discomfort to check for overall parameter relevance, direction and strength of effects; timings of increase/decrease; variability as well as filtering, standardization and artifact removal strategies to increase the signal-to-noise ratio. Results showed a reduced eye blink rate during discomfort as well as pupil dilation, also after correcting for ambient light influence. Contrary to expectations, HR decreased significantly during discomfort periods, whereas HRV diminished as expected. No effects could be observed for SCL. Body motion showed the expected pushback movement during the close approach situation. Overall, besides SCL, all other parameters showed changes associated with discomfort indicated by the handset control. The results serve as a basis for designing and configuring a real-time discomfort detection algorithm that will be implemented in the driving simulator and validated in subsequent studies

Validation of a VR cycling simulation in terms of perceived criticality and experience of presence

Cycling offers many benefits, such as reducing traffic congestion, Iower emissions and health benefits. To further promote cycling, the cyclists' perceived safety needs to be addressed. In this context, automated vehicles offer high potential for designing safe and comfortable interactions with cyclists in the future. A key parameter in these interactions constitutes the proximity of vehicles passing cyclists to avoid causing discomfort. To evaluate specific scenarios with varying proximity, cycling simulators provide a safe and standardized environment for traffic safety research. Therefore, there are numerous efforts to implement cycling simulators for use in research. However, it is important to verify the simulator validity to ensure the generalizability of results. In this work, an implementation of a virtual reality (VR) cycling simulation is presented and it is aimed to investigate the simulator validity in terms of perceived criticality in traffic conflict scenarios as well as the participants' experience of presence within the VR cycling simulation. [from Introduction

Memory-related cognitive load effects in an interrupted learning task:A model-based explanation

Background: The Cognitive Load Theory provides a well-established framework for investigating aspects of learning situations that demand learners' working memory resources. However, the interplay of these aspects at the cognitive and neural level is still not fully understood. Method: We developed four computational models in the cognitive architecture ACT-R to clarify underlying memory-related strategies and mechanisms. Our models account for human data of an experiment that required participants to perform a symbol sequence learning task with embedded interruptions. We explored the inclusion of subsymbolic mechanisms to explain these data and used our final model to generate fMRI predictions. Results: The final model indicates a reasonable fit for reaction times and accuracy and links the fMRI predictions to the Cognitive Load Theory. Conclusions: Our work emphasizes the influence of task characteristics and supports a process-related view on cognitive load in instructional scenarios. It further contributes to the discussion of underlying mechanisms at a neural level