1,681 research outputs found
Measuring working memory load effects on electrophysiological markers of attention orienting during a simulated drive
Intersection accidents result in a significant proportion of road fatalities, and attention allocation likely plays a role. Attention allocation may depend on (limited) working memory (WM) capacity. Driving is often combined with tasks increasing WM load, consequently impairing attention orienting. This study (n = 22) investigated WM load effects on event-related potentials (ERPs) related to attention orienting. A simulated driving environment allowed continuous lane-keeping measurement. Participants were asked to orient attention covertly towards the side indicated by an arrow, and to respond only to moving cars appearing on the attended side by pressing a button. WM load was manipulated using a concurrent memory task. ERPs showed typical attentional modulation (cue: contralateral negativity, LDAP; car: N1, P1, SN and P3) under low and high load conditions. With increased WM load, lane-keeping performance improved, while dual task performance degraded (memory task: increased error rate; orienting task: increased false alarms, smaller P3).
Practitioner Summary: Intersection driver-support systems aim to improve traffic safety and flow. However, in-vehicle systems induce WM load, increasing the tendency to yield. Traffic flow reduces if drivers stop at inappropriate times, reducing the effectiveness of systems. Consequently, driver-support systems could include WM load measurement during driving in the development phase
A multidisciplinary research approach for experimental applications in road-driver interaction analysis
This doctoral dissertation represents a cluster of the research activities conducted at the DICAM Department of the University of Bologna during a three years Ph.D. course. In relation to the broader research topic of “road safety”, the presented research focuses on the investigation of the interaction between the road and the drivers according to human factor principles and supported by the following strategies: 1) The multidisciplinary structure of the research team covering the following academic disciplines: Civil Engineering, Psychology, Neuroscience and Computer Science Engineering. 2) The development of several experimental real driving tests aimed to provide investigators with knowledge and insights on the relation between the driver and the surrounding road environment by focusing on the behaviour of drivers. 3) The use of innovative technologies for the experimental studies, capable to collect data of the vehicle and on the user: a GPS data recorder, for recording the kinematic parameters of the vehicle; an eye tracking device, for monitoring the drivers’ visual behaviour; a neural helmet, for the detection of drivers’ cerebral activity (electroencephalography, EEG). 4) The use of mathematical-computational methodologies (deep learning) for data analyses from experimental studies.
The outcomes of this work consist of new knowledge on the casualties between drivers’ behaviour and road environment to be considered for infrastructure design. In particular, the ground-breaking results are represented by:
- the reliability and effectiveness of the methodology based on human EEG signals to objectively measure driver’s mental workload with respect to different road factors;
- the successful approach for extracting latent features from multidimensional driving behaviour data using a deep learning technique, obtaining driving colour maps which represent an immediate visualization with potential impacts on road safety
Defining, measuring, and modeling passenger's in-vehicle experience and acceptance of automated vehicles
Automated vehicle acceptance (AVA) has been measured mostly subjectively by
questionnaires and interviews, with a main focus on drivers inside automated
vehicles (AVs). To ensure that AVs are widely accepted by the public, ensuring
the acceptance by both drivers and passengers is key. The in-vehicle experience
of passengers will determine the extent to which AVs will be accepted by
passengers. A comprehensive understanding of potential assessment methods to
measure the passenger experience in AVs is needed to improve the in-vehicle
experience of passengers and thereby the acceptance. The present work provides
an overview of assessment methods that were used to measure a driver's
behavior, and cognitive and emotional states during (automated) driving. The
results of the review have shown that these assessment methods can be
classified by type of data-collection method (e.g., questionnaires, interviews,
direct input devices, sensors), object of their measurement (i.e., perception,
behavior, state), time of measurement, and degree of objectivity of the data
collected. A conceptual model synthesizes the results of the literature review,
formulating relationships between the factors constituting the in-vehicle
experience and AVA acceptance. It is theorized that the in-vehicle experience
influences the intention to use, with intention to use serving as predictor of
actual use. The model also formulates relationships between actual use and
well-being. A combined approach of using both subjective and objective
assessment methods is needed to provide more accurate estimates for AVA, and
advance the uptake and use of AVs.Comment: 22 pages, 1 figur
Non-visual Effects of Road Lighting CCT on Driver's Mood, Alertness, Fatigue and Reaction Time: A Comprehensive Neuroergonomic Evaluation Study
Good nighttime road lighting is critical for driving safety. To improve the
quality of nighttime road lighting, this study used the triangulation method by
fusing "EEG evaluation + subjective evaluation + behavioral evaluation" to
qualitatively and quantitatively investigate the response characteristics of
different correlated color temperature (CCT) (3500K, 4500K, 5500K, 6500K) on
drivers' non-visual indicators (mood, alertness, fatigue and reaction time)
under specific driving conditions (monotonous driving; waiting for red light
and traffic jam; car-following task). The results showed that the CCT and Task
interaction effect is mainly related to individual alertness and reaction time.
Individual subjective emotional experience, subjective visual comfort and
psychological security are more responsive to changes in CCT than individual
mental fatigue and visual fatigue. The subjective and objective evaluation
results demonstrated that the EEG evaluation indices used in this study could
objectively reflect the response characteristics of various non-visual
indicators. The findings also revealed that moderate CCT (4500K) appears to be
the most beneficial to drivers in maintaining an ideal state of mind and body
during nighttime driving, which is manifested as: good mood experience; it
helps drivers maintain a relatively stable level of alterness and to respond
quickly to external stimuli; both mental and visual fatigue were relatively
low. This study extends nighttime road lighting design research from the
perspective of non-visual effects by using comprehensive neuroergonomic
evaluation methods, and it provides a theoretical and empirical basis for the
future development of a humanized urban road lighting design evaluation system.Comment: 38 pages, 15 figures, 103 conference
Applications of brain imaging methods in driving behaviour research
Applications of neuroimaging methods have substantially contributed to the
scientific understanding of human factors during driving by providing a deeper
insight into the neuro-cognitive aspects of driver brain. This has been
achieved by conducting simulated (and occasionally, field) driving experiments
while collecting driver brain signals of certain types. Here, this sector of
studies is comprehensively reviewed at both macro and micro scales. Different
themes of neuroimaging driving behaviour research are identified and the
findings within each theme are synthesised. The surveyed literature has
reported on applications of four major brain imaging methods. These include
Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG),
Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG),
with the first two being the most common methods in this domain. While
collecting driver fMRI signal has been particularly instrumental in studying
neural correlates of intoxicated driving (e.g. alcohol or cannabis) or
distracted driving, the EEG method has been predominantly utilised in relation
to the efforts aiming at development of automatic fatigue/drowsiness detection
systems, a topic to which the literature on neuro-ergonomics of driving
particularly has shown a spike of interest within the last few years. The
survey also reveals that topics such as driver brain activity in semi-automated
settings or the brain activity of drivers with brain injuries or chronic
neurological conditions have by contrast been investigated to a very limited
extent. Further, potential topics in relation to driving behaviour are
identified that could benefit from the adoption of neuroimaging methods in
future studies
Effects of Mobile Phone Use on Driving Performance: An Experimental Study of Workload and Traffic Violations
ABSTRACT: The use of communication technologies, e.g., mobile phones, has increased dramatically in recent years, and their use among drivers has become a great risk to traffic safety. The present study assessed the workload and road ordinary violations, utilizing driving data collected from 39 young participants who underwent a dual-task while driving a simulator, i.e., respond to a call, text on WhatsApp, and check Instagram. Findings confirmed that there are significant differences in the driving performance of young drivers in terms of vehicle control (i.e., lateral distance and hard shoulder line violations) between distracted and non-distracted drivers. Furthermore, the overall workload score of young drivers increases with the use of their mobile phones while driving. The obtained results contribute to a better understanding of the driving performance of distracted young drivers and thus they could be useful for further improvements to traffic safety strategies
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