Eye tracking use in researching driver distraction: A scientometric and qualitative literature review approach

Many factors affect road safety, but research constantly shows that drivers are the major cause of critical situations that could potentially lead to a traffic accident in road traffic. Visual information is a crucial part of input information into the driving process; therefore, distractions of overt visual attention can potentially have a large impact on driving safety. Modern eye tracking technology enables researchers to gain precise insight into the direction and movement of a driver’s gaze during various distractions. As this is an evolving and currently very relevant field of road safety research, the present paper sets out to analyse the current state of the research field and the most relevant publications that use eye tracking for research of distractions to a driver’s visual attention. With the use of scientometrics and a qualitative review of the 139 identified publications that fit the inclusion criteria, the results revealed a currently expanding research field. The narrow research field is interdisciplinary in its core, as evidenced by the dispersion of publication sources and research variables. The main research gaps identified were performing research in real conditions, including a wider array of distractions, a larger number of participants, and increasing interdisciplinarity of the field with more author cooperation outside of their primary co-authorship networks

EFFECTS OF AUTOMATION AND TAKEOVER TIME BUDGET ON YOUNG DRIVERS’ PERFORMANCE AND WORKLOAD

Young drivers are involved in higher number of crashes compared to other age groups. Highly automated vehicles are expected to improve traffic safety and reduce human errors. However, there are still concerns about the effects of automation and takeover time budget (TOTB) on driver performance and workload. The objective of this study was to assess the effects of unreliable automation, non-driving related tasks (NDRTs), and TOTB on young drivers’ takeover performance and workload when faced with critical incidents. Twenty-eight young drivers participated in a within-subject driving simulation study. Driver workload was measured using physiological measures including percentage change in pupil size and blink rate, subjective measurement of driver activity load index, and secondary task performance. Driver takeover performance was measured using maximum lateral acceleration, minimum time to collision, and takeover time. Results suggested that when faced with critical incidents, 8s of TOTB might be sufficient for young drivers to safely take over the control of the vehicle. However, providing longer TOTBs (i.e., 10s) can further reduce drivers’ mental workload. Performing a demanding NDRT significantly impaired drivers’ takeover performance and increased their workload. However, the results regarding the effect of automation on drivers’ mental workload and takeover performance were inconclusive, which might be due to short observation periods, and individual or recall biases. The findings of this study can provide guidelines for vehicle manufacturers to improve the design of highly automated vehicles, which can ultimately improve driver performance and reduce workload

Integrating automobile multiple intelligent warning systems : performance and policy implications

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2006.Includes bibliographical references (p. 160-167).Intelligent driver warning systems can be found in many high-end vehicles on the road today, which will likely rapidly increase as they become standard equipment. However, introducing multiple warning systems into vehicles could potentially add to the complexity of the driving task, and there are many critical human factors issues that should be considered, such as how the interaction between alarm alerting schemes, system reliabilities, and distractions combine to affect driving performance and situation awareness. In addition, there are also questions with respect to whether there should be any minimum safety standards set to ensure both functional and usage safety of these systems, and what these standards should be. An experiment was conducted to study how a single master alert versus multiple individual alerts of different reliabilities affected drivers' responses to different imminent collision situations while distracted. A master alert may have advantages since it reduces the total number of alerts, which could be advantageous especially with the proliferation of intelligent warning systems. However, a master alert may also confuse drivers, since it does not warn of a specific hazard, unlike a specific alert for each warning systems.(cont.) Auditory alerts were used to warn of imminent frontal and rear collisions, as well as unintentional left and right lane departures. Low and high warning reliabilities were also tested. The different warning systems and reliability factors produced significantly different reaction times and response accuracies. The warning systems with low reliability caused accuracy rates to fall more than 40% across the four warning systems. In addition, low reliability systems also induced negative emotions in participants. Thus, reliability is one of the most crucial determinants of driving performance and the safety outcome, and it is imperative that warning systems are reliable. For the master versus distinct alarms factor, drivers responded statistically no different to the various collision warnings for both reaction times and accuracy of responses. However, in a subjective post-experiment assessment, participants preferred distinct alarms for different driver warning systems, even though their objective performance showed no difference to the different alerting schemes. This study showed that it was essential to design robust and reliable intelligent warning systems. However, there are no existing safety standards today to ensure that these systems are safe before they are introduced into vehicles, even though such systems are already available in high-end cars.(cont.) Even though there are tradeoffs in having standards, such as increased time-to-market and possible loss of innovation, I recommend that safety standards be set nonetheless, since standards will ensure the safety performance of warning systems, to an extent. In terms of functional safety, safety standards should be performance-based, and should specify a minimum level of reliability. In terms of usage safety, the standards should also be performance-based, where driving performance can be indicated by measures such as reaction time, lane position, heading distance and accuracy of responses. In addition, multiple threat scenarios should also be tested. In terms of design guidelines, the various human factors guidelines from different countries should be harmonized internationally to ensure that manufacturers have access to a consistent set of guidelines. Finally, it is also important that these standards, especially for usage safety, specify tests with not just the average driver, but also with peripheral driving populations including novice and elderly drivers.by Angela Wei Ling Ho.S.M

Prediction of drivers’ performance in highly automated vehicles

Purpose: The aim of this research was to assess the predictability of driver’s response to critical hazards during the transition from automated to manual driving in highly automated vehicles using their physiological data.Method: A driving simulator experiment was conducted to collect drivers’ physiological data before, during and after the transition from automated to manual driving. A total of 33 participants between 20 and 30 years old were recruited. Participants went through a driving scenario under the influence of different non-driving related tasks. The repeated measures approach was used to assess the effect of repeatability on the driver’s physiological data. Statistical and machine learning methods were used to assess the predictability of drivers’ response quality based on their physiological data collected before responding to a critical hazard. Findings: - The results showed that the observed physiological data that was gathered before the transition formed strong indicators of the drivers’ ability to respond successfully to a potential hazard after the transition. In addition, physiological behaviour was influenced by driver’s secondary tasks engagement and correlated with the driver’s subjective measures to the difficulty of the task. The study proposes new quality measures to assess the driver’s response to critical hazards in highly automated driving. Machine learning results showed that response time is predictable using regression methods. In addition, the classification methods were able to classify drivers into low, medium and high-risk groups based on their quality measures values. Research Implications: Proposed models help increase the safety of automated driving systems by providing insights into the drivers’ ability to respond to future critical hazards. More research is required to find the influence of age, drivers’ experience of the automated vehicles and traffic density on the stability of the proposed models. Originality: The main contribution to knowledge of this study is the feasibility of predicting drivers’ ability to respond to critical hazards using the physiological behavioural data collected before the transition from automated to manual driving. With the findings, automation systems could change the transition time based on the driver’s physiological state to allow for the safest transition possible. In addition, it provides an insight into driver’s readiness and therefore, allows the automated system to adopt the correct driving strategy and plan to enhance drivers experience and make the transition phase safer for everyone.</div

Task time and glance measures of the use of telematics: a tabular summary of the literature

Delphi Electronics & Safetyhttp://deepblue.lib.umich.edu/bitstream/2027.42/92350/1/102882.pd

Modeling driver distraction mechanism and its safety impact in automated vehicle environment.

Automated Vehicle (AV) technology expects to enhance driving safety by eliminating human errors. However, driver distraction still exists under automated driving. The Society of Automotive Engineers (SAE) has defined six levels of driving automation from Level 0~5. Until achieving Level 5, human drivers are still needed. Therefore, the Human-Vehicle Interaction (HVI) necessarily diverts a driver’s attention away from driving. Existing research mainly focused on quantifying distraction in human-operated vehicles rather than in the AV environment. It causes a lack of knowledge on how AV distraction can be detected, quantified, and understood. Moreover, existing research in exploring AV distraction has mainly pre-defined distraction as a binary outcome and investigated the patterns that contribute to distraction from multiple perspectives. However, the magnitude of AV distraction is not accurately quantified. Moreover, past studies in quantifying distraction have mainly used wearable sensors’ data. In reality, it is not realistic for drivers to wear these sensors whenever they drive. Hence, a research motivation is to develop a surrogate model that can replace the wearable device-based data to predict AV distraction. From the safety perspective, there lacks a comprehensive understanding of how AV distraction impacts safety. Furthermore, a solution is needed for safely offsetting the impact of distracted driving. In this context, this research aims to (1) improve the existing methods in quantifying Human-Vehicle Interaction-induced (HVI-induced) driver distraction under automated driving; (2) develop a surrogate driver distraction prediction model without using wearable sensor data; (3) quantitatively reveal the dynamic nature of safety benefits and collision hazards of HVI-induced visual and cognitive distractions under automated driving by mathematically formulating the interrelationships among contributing factors; and (4) propose a conceptual prototype of an AI-driven, Ultra-advanced Collision Avoidance System (AUCAS-L3) targeting HVI-induced driver distraction under automated driving without eye-tracking and video-recording. Fixation and pupil dilation data from the eye tracking device are used to model driver distraction, focusing on visual and cognitive distraction, respectively. In order to validate the proposed methods for measuring and modeling driver distraction, a data collection was conducted by inviting drivers to try out automated driving under Level 3 automation on a simulator. Each driver went through a jaywalker scenario twice, receiving a takeover request under two types of HVI, namely “visual only” and “visual and audible”. Each driver was required to wear an eye-tracker so that the fixation and pupil dilation data could be collected when driving, along with driving performance data being recorded by the simulator. In addition, drivers’ demographical information was collected by a pre-experiment survey. As a result, the magnitude of visual and cognitive distraction was quantified, exploring the dynamic changes over time. Drivers are more concentrated and maintain a higher level of takeover readiness under the “visual and audible” warning, compared to “visual only” warning. The change of visual distraction was mathematically formulated as a function of time. In addition, the change of visual distraction magnitude over time is explained from the driving psychology perspective. Moreover, the visual distraction was also measured by direction in this research, and hotspots of visual distraction were identified with regard to driving safety. When discussing the cognitive distraction magnitude, the driver’s age was identified as a contributing factor. HVI warning type contributes to the significant difference in cognitive distraction acceleration rate. After drivers reach the maximum visual distraction, cognitive distraction tends to increase continuously. Also, this research contributes to quantitatively revealing how visual and cognitive distraction impacts the collision hazards, respectively. Moreover, this research contributes to the literature by developing deep learning-based models in predicting a driver’s visual and cognitive distraction intensity, focusing on demographics, HVI warning types, and driving performance. As a solution to safety issues caused by driver distraction, the AUCAS-L3 has been proposed. The AUCAS-L3 is validated with high accuracies in predicting (a) whether a driver is distracted and does not perform takeover actions and (b) whether crashes happen or not if taken over. After predicting the presence of driver distraction or a crash, AUCAS-L3 automatically applies the brake pedal for drivers as effective and efficient protection to driver distraction under automated driving. And finally, a conceptual prototype in predicting AV distraction and traffic conflict was proposed, which can predict the collision hazards in advance of 0.82 seconds on average

Age Differences in the Situation Awareness and Takeover Performance in a Semi-Autonomous Vehicle Simulator

Research on young and elderly drivers indicates a high crash risk amongst these drivers in comparison to other age groups of drivers. Young drivers have a greater propensity to adopt a risky driving style and behaviors associated with poor road safety. On the other hand, age-related declines can negatively impact the performance of older drivers on the road leading to crashes and risky maneuvers. Thus, autonomous vehicles have been suggested to improve the road safety and mobility of younger and older drivers. However, the difficulty of manually taking over control from semi-autonomous vehicles might vary in different driving conditions, particularly in those that are more challenging. Hence, the present study aims to examine the effect of road geometry and scenario, by investigating young, middle-aged and older drivers' situation awareness (SA) and takeover performance when driving a semi-autonomous vehicle simulator on a straight versus a curved road on a highway and an urban non-highway road when engaged in a secondary distracting task. Due to the impact of COVID-19, data from only the young (n=24) and middle-aged (n=24) adults were collected and analyzed. Participants drove a Level 3 semi-autonomous simulator vehicle and performed a secondary non-driving related task in the distracted conditions. The results indicated that the participants had significantly longer hazard perception times on the curved roads and autopilot drives, but there was no significant effect of driver age and road type. Their Situation Awareness Global Assessment Technique (SAGAT) scores were higher in the highway scenarios, on the straight roads, and in the manual drive compared to the autopilot with distraction drive. Young drivers were also found to have significantly higher SAGAT scores than middle-aged drivers. While there was a significant interaction effect between road type and road geometry on takeover time, there was no significant main effect of road geometry, drive type and driver’s age. For the takeover quality metrics, road geometry and drive type had an effect on takeover performance. The resulting acceleration was higher for the straight road and in the autopilot drives, and the lane deviation was higher on the curved road and autopilot only drive compared to the autopilot with distraction drive. There was no significant main effect of road type and driver’s age on resulting acceleration and lane deviation. Overall, while there were age differences in some aspects of SA, young and middle-aged drivers did not differ in their takeover performance. The participants' SA was impacted by the road type and geometry and their takeover quality varied according to the road geometry and drive type. The outcomes of this research will aid vehicle manufacturing companies that are developing Level 3 semi-autonomous vehicles with appropriately designing the lead time of the takeover request to meet the driving style and abilities of younger and middle-aged drivers. This will also help to improve road safety by reducing the crash rate of younger drivers

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’)