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

Understanding Driver Response Patterns to Mental Workload Increase in Typical Driving Scenarios

As vehicles become more complex and traffic increases, the associated mental workload of driving should increase, potentially compromising driving safety. As mental workload increases (as measured by the detection response time task), does how people drive (as assessed by driving performance and eye fixations) change? How does driving experience impact on such response patterns? To address those questions, data were collected in a motion-based driving simulator. Two driving scenarios were examined, a stop-controlled intersection (high workload — 16 participants, 320 trials) and speed-limited highway (low workload — 11 participants, 264 trials). In each scenario, in half of the trials, the participants were required to complete or not to complete a distracting secondary task. Hierarchical cluster analysis was used to identify driver response patterns. For highway driving, they are: (1) increased eye fixation variability and unchanged driving performance, and (2) unchanged fixation variability and increased mean speed. For intersection driving, they are: (1) increased and (2) decreased fixation variability both with decreased speed (mean and variance), and (3) increased fixation variability with increased speed. Eye fixation variability was more strongly associated with increased mental workload than other driving performance statistics. Furthermore, in contrast to prior research, changes in driving performance and eye fixations were not necessarily correlated with each other as mental workload increased. Novice drivers exhibit higher gaze variability, and they are more prone to maintain vehicle control than experienced drivers

The Impact of Distraction on an Intersection Crossing Assist System

The current study examines the impact of drivers’ use of an in-vehicle intersection crossing assist system under demanding cognitive load conditions. The use and adherence to the assist system is examined through intersection crossing driving performance measures. Furthermore, the impact of distraction is examined for younger and older drivers. The results suggest a more conservative approach to the crossing of rural intersections when using the assist system, a finding which was not altered by cognitive load

Cognitive Distraction Impairs Drivers\u27 Anticipatory Glances: An On-Road Study

This study assessed the impact of cognitive distraction on drivers’ anticipatory glances. Participants drove an instrumented vehicle and executed a number of secondary tasks associated with increasing levels of mental workload including: listening to the radio or audiobook, talking on a handheld or hands-free cellphone, interacting with a voice-based e-mail/text system, and executing a highly demanding task (Operational Span task; OSPAN). Drivers’ visual scanning behavior was recorded by four different high definition cameras and coded offline frame-by-frame. Visual scanning behavior at road intersections with crosswalks was targeted because distraction is one of the major causes of accidents at these locations (NHTSA, 2010a). Despite the familiarity of the locations, results showed that as the secondary-task became more cognitively demanding drivers reduced the amount of anticipatory glances to potential hazards locations. For example, while interacting with a high fidelity voice-based email/text system, the probability of executing a complete scan of the intersection was reduced by 11% compared to the no-distraction control condition. These results document the effects of cognitive distraction on drivers’ visual scanning for potential hazards and highlight the detrimental role of voice based systems on driving behavior

Smart driving aids and their effects on driving performance and driver distraction

In-vehicle information systems have been shown to increase driver workload and cause distraction; both of which are causal factors for accidents. This simulator study evaluates the impact that two designs for a smart driving aid, and scenario complexity have on workload, distraction and driving performance. Results showed that real-time delivery of smart driving information did not increase driver workload or adversely effect driver distraction, while having the effect of decreasing mean driving speed in both the simple and complex driving scenarios. Subjective workload was shown to increase with task difficulty, as well as revealing important differences between the two interface designs

Characteristics and Contributing Factors of Emergency Vehicle Crashes

The purpose of this study is to determine the contributing factors and characteristics associated with emergency vehicle(EV) crashes in order to generate insights about the emergency crashes. This dissertation consists of three approaches to address the purpose. In the first analysis a binary logistic regression model was used to identify the critical factors associated with EV crashes that resulted in fatality compared to those that did not. In the second analysis, an ordered regression model was used to identify critical factors that contributed to the severity of injuries that EV occupants experience in crashes as well as the effect of driver distraction and driver fatigue on the severity of injury in EV crashes. The third analysis employed a multinomial logit model to identify the disparities among types of EV (e.g., police, ambulance, and fire trucks) in terms of the types of crash. The results of this research have demonstrated several significant factors associated with the EV crashes in addition to what has been established in literature before

Identification of road user related risk factors, deliverable 4.1 of the H2020 project SafetyCube.

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the first deliverable (4.1) of work package 4 which is dedicated to identifying and assessing human related risk factors and corresponding countermeasures as well as their effect on road safety. The focus of deliverable 4.1 is on identification and assessment of risk factors and describes the corresponding operational procedure and corresponding outcomes. The following steps have been carried out: Identification of human related risk factors – creation of a taxonomy Consultation of relevant stakeholders and policy papers for identification of topic with high priority (‘hot topics’) Systematic literature search and selection of relevant studies on identified risk factors •Coding of studies •Analysis of risk factors on basis of coded studies •Synopses of risk factors, including accident scenarios The core output of this task are synopses of risk factors which will be available through the DSS. Within the synopses, each risk factor was analysed systematically on basis of scientific studies and is further assigned to one of four levels of risk (marked with a colour code). Essential information of the more than 180 included studies were coded and will also be available in the database of the DSS. Furthermore, the synopses contain theoretical background on the risk factor and are prepared in different sections with different levels of detail for an academic as well as a non-academic audience. These sections are readable independently. It is important to note that the relationship between road safety and road user related risk factors is a difficult task. For some risk factors the available studies focused more on conditions of the behaviour (in which situations the behaviour is shown or which groups are more likely to show this behaviour) rather than the risk factor itself. Therefore, it cannot be concluded that those risk factors that have not often been studied or have to rely more indirect and arguably weaker methodologies, e.g. self-reports , do not increase the chance of a crash occurring. The following analysed risk factors were assessed as ‘risky’, ‘probably risky’ or ‘unclear’. No risk factors were identified as ‘probably not risky’. Risky Probably risky Unclear • Influenced driving – alcohol • Influenced Driving – drugs (legal & illegal) • Speeding and inappropriate speed • Traffic rule violations – red light running • Distraction – cell phone use (hand held) • Distraction – cell phone use (hands free) • Distraction – cell phone use (texting) • Fatigue – sleep disorders – sleep apnea • Risk taking – overtaking • Risk taking – close following behaviour • Insufficient knowledge and skills • Functional impairment – cognitive impairment • Functional impairment – vision loss • Diseases and disorders – diabetes • Personal factors – sensation seeking • Personal factors – ADHD • Emotions – anger, aggression • Fatigue – Not enough sleep/driving while tired • Distraction – conversation with passengers • Distraction – outside of vehicle • Distraction – cognitive overload and inattention • Functional impairment – hearing loss (few studies) • Observation errors (few studies) • Distraction – music – entertainment systems (many studies, mixed results) • Distraction – operating devices (many studies, mixed results) The next step in SafetyCube’s WP4 is to identify and assess the effectiveness of measures and to establish a link to the identified risk factors. The work of this first task indicates a set of risk factors that should be centre of attention when identifying corresponding road safety measures (category ‘risky’)

Hum Factors

Objective:This study reports current status of knowledge and challenges associated with the emergency vehicle (police car, fire truck, and ambulance) crashes, with respect to the major contributing risk factors.Background:Emergency vehicle crashes are a serious nationwide problem, causing injury and death to emergency responders and citizens. Understanding the underlying causes of these crashes is critical for establishing effective strategies for reducing the occurrence of similar incidents.Method:We reviewed the broader literature associated with the contributing factors for emergency vehicle crashes: peer-reviewed journal papers; and reports, policies, and manuals published by government agencies, universities, and research institutes.Results:Major risk factors for emergency vehicle crashes identified in this study were organized into four categories: driver, task, vehicle, and environmental factors. Also, current countermeasures and interventions to mitigate the hazards of emergency vehicle crashes were discussed, and new ideas for future studies were suggested.Conclusion:Risk factors, control measures, and knowledge gaps relevant to emergency vehicle crashes were presented. Six research concepts are offered for the human factors community to address. Among the topics are emergency vehicle driver risky behavior carryover between emergency response and return from a call, distraction in emergency vehicle driving, in-vehicle driver assistance technologies, vehicle red light running, and pedestrian crash control.Application:This information is helpful for emergency vehicle drivers, safety practitioners, public safety agencies, and research communities to mitigate crash risks. It also offers ideas for researchers to advance technologies and strategies to further emergency vehicle safety on the road.CC999999/ImCDC/Intramural CDC HHS/United States2020-11-24T00:00:00Z29965790PMC76855298713vault:3620

Dream 3.0. Documentation of references supporting the links in the classification scheme

Both the Driving Reliability and Error Analysis Method (DREAM; Ljung, 2002) and the SafetyNet Accident Causation System (SNACS; Ljung, 2006) have been successfully used as tools for accident analysis in Sweden as well as in other European countries. While the drivervehicle/ traffic environment-organisation triad are used as frames of reference and the Contextual Control Model (COCOM; Hollnagel, 1998) is used to organise human cognition, the links in the classification schemes have not been established by referring to literature. The aim of this literature review is therefore to investigate the empirical support for the links in the classification scheme of DREAM 3.0 (an updated version of DREAM/SNACS)