184,507 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
Analysis of Disengagements in Semi-Autonomous Vehicles: Driversā Takeover Performance and Operational Implications
This report analyzes the reactions of human drivers placed in simulated Autonomous Technology disengagement scenarios. The study was executed in a human-in-the-loop setting, within a high-fidelity integrated car simulator capable of handling both manual and autonomous driving. A population of 40 individuals was tested, with metrics for control takeover quantification given by: i) response times (considering inputs of steering, throttle, and braking); ii) vehicle drift from the lane centerline after takeover as well as overall (integral) drift over an S-turn curve compared to a baseline obtained in manual driving; and iii) accuracy metrics to quantify human factors associated with the simulation experiment. Independent variables considered for the study were the age of the driver, the speed at the time of disengagement, and the time at which the disengagement occurred (i.e., how long automation was engaged for). The study shows that changes in the vehicle speed significantly affect all the variables investigated, pointing to the importance of setting up thresholds for maximum operational speed of vehicles driven in autonomous mode when the human driver serves as back-up. The results shows that the establishment of an operational threshold could reduce the maximum drift and lead to better control during takeover, perhaps warranting a lower speed limit than conventional vehicles. With regards to the age variable, neither the response times analysis nor the drift analysis provide support for any claim to limit the age of drivers of semi-autonomous vehicles
Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving
In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving-related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence driversā takeover performance. This study, therefore, examined the effects of emotional valence and arousal on driversā takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not
Automotive automation: Investigating the impact on drivers' mental workload
Recent advances in technology have meant that an increasing number of vehicle driving
tasks are becoming automated. Such automation poses new problems for the ergonomist.
Of particular concern in this paper are the twofold effects of automation on mental
workload - novel technologies could increase attentional demand and workload,
alternatively one could argue that fewer driving tasks will lead to the problem of reduced
attentional demand and driver underload. A brief review of previous research is
presented, followed by an overview of current research taking place in the Southampton
Driving Simulator. Early results suggest that automation does reduce workload, and that
underload is indeed a problem, with a significant proportion of drivers unable to
effectively reclaim control of the vehicle in an automation failure scenario. Ultimately,
this research and a subsequent program of studies will be interpreted within the
framework of a recently proposed theory of action, with a view to maximizing both
theoretical and applied benefits of this domain
Integrating driving and traffic simulators for the study of railway level crossing safety interventions: a methodology
Safety at Railway Level Crossings (RLXs) is an important issue within the Australian transport system. Crashes at RLXs involving road vehicles in Australia are estimated to cost $10 million each year. Such crashes are mainly due to human factors; unintentional errors contribute to 46% of all fatal collisions and are far more common than deliberate violations. This suggests that innovative intervention targeting drivers are particularly promising to improve RLX safety. In recent years there has been a rapid development of a variety of affordable technologies which can be used to increase driverās risk awareness around crossings. To date, no research has evaluated the potential effects of such technologies at RLXs in terms of safety, traffic and acceptance of the technology. Integrating driving and traffic simulations is a safe and affordable approach for evaluating these effects. This methodology will be implemented in a driving simulator, where we recreated realistic driving scenario with typical road environments and realistic traffic. This paper presents a methodology for evaluating comprehensively potential benefits and negative effects of such interventions: this methodology evaluates driver awareness at RLXs , driver distraction and workload when using the technology . Subjective assessment on perceived usefulness and ease of use of the technology is obtained from standard questionnaires. Driving simulation will provide a model of driving behaviour at RLXs which will be used to estimate the effects of such new technology on a road network featuring RLX for different market penetrations using a traffic simulation. This methodology can assist in evaluating future safety interventions at RLXs
End-to-end Learning of Driving Models from Large-scale Video Datasets
Robust perception-action models should be learned from training data with
diverse visual appearances and realistic behaviors, yet current approaches to
deep visuomotor policy learning have been generally limited to in-situ models
learned from a single vehicle or a simulation environment. We advocate learning
a generic vehicle motion model from large scale crowd-sourced video data, and
develop an end-to-end trainable architecture for learning to predict a
distribution over future vehicle egomotion from instantaneous monocular camera
observations and previous vehicle state. Our model incorporates a novel
FCN-LSTM architecture, which can be learned from large-scale crowd-sourced
vehicle action data, and leverages available scene segmentation side tasks to
improve performance under a privileged learning paradigm.Comment: camera ready for CVPR201
A First Step toward the Understanding of Implicit Learning of Hazard Anticipation in Inexperienced Road Users Through a Moped-Riding Simulator
Hazard perception is considered one of the most important abilities in road safety.
Several efforts have been devoted to investigating how it improves with experience
and can be trained. Recently, research has focused on the implicit aspects of hazard
detection, reaction, and anticipation. In the present study, we attempted to understand
how the ability to anticipate hazards develops during training with a moped-riding
simulator: the Honda Riding Trainer (HRT). Several studies have already validated the
HRT as a tool to enhance adolescents\u2019 hazard perception and riding abilities. In the
present study, as an index of hazard anticipation, we used skin conductance response
(SCR), which has been demonstrated to be linked to affective/implicit appraisal of risk.
We administered to a group of inexperienced road users five road courses two times a
week apart. In each course, participants had to deal with eight hazard scenes (except
one course that included only seven hazard scenes). Participants had to ride along
the HRT courses, facing the potentially hazardous situations, following traffic rules, and
trying to avoid accidents. During the task, we measured SCR and monitored driving
performance. The main results show that learning to ride the simulator leads to both a
reduction in the number of accidents and anticipation of the somatic response related
to hazard detection, as proven by the reduction of SCR onset recorded in the second
session. The finding that the SCR signaling the impending hazard appears earlier when
the already encountered hazard situations are faced anew suggests that training with
the simulator acts on the somatic activation associated with the experience of risky
situations, improving its effectiveness in detecting hazards in advance so as to avoid
accidents. This represents the starting point for future investigations into the process of
generalization of learning acquired in new virtual situations and in real-road situations
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Development and validation of the Spanish hazard perception test
Objective: The aim of the current study is to develop and obtain validity evidence for a Hazard Perception test suitable for the Spanish driving population. To obtain validity evidence to support the use of the test, the effect of hazardous and quasi-hazardous situations on the participantsā Hazard Prediction is analysed and the pattern of results of drivers of different driving experience: learner, novice and expert drivers and re-offender vs. non-offender drivers, is compared. Potentially hazardous situations are those that develop without involving any real hazard (i.e., the driver didnāt actually have to decelerate or make any evasive manoeuvre to avoid a potential collision). The current study analysed multiple offender drivers attending compulsory re-education programmes as a result of reaching the maximum number of penalty points on their driving licence, due to repeated violations of traffic laws. Method: A new video-based hazard perception test was developed, using a total of 20 hazardous situation videos plus 8 quasi-hazardous situation videos. They were selected from 167 recordings of natural hazards in real Spanish driving settings
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