The effects of cognitive distraction on behavioural, oculomotor and electrophysiological metrics during a driving hazard perception task

Effect of expressway exit deceleration markings on distracted drivers in China

Expressway exit areas experience traffic diversion and complex road conditions, making them accident-prone areas. In this study, transverse and fishbone visual illusion deceleration markings were selected to optimize the induction facilities at expressway exits. The research aims to investigate the impact of these markings on the driving behavior, cognitive load, and physiological characteristics of drivers in various distracted scenarios at expressway exit areas. Furthermore, a comprehensive evaluation of each experimental scheme is conducted using the Matter-Element Extension Model. The study found that the implementation of deceleration markings can effectively enhance driver alertness and lane change awareness, enabling drivers to reduce their speed to near the speed limit in exit areas without compromising driving comfort. Compared to the situation without markings, drivers begin to decelerate approximately 600 m earlier and exit the ramp when markings are present. Fishbone deceleration markings, in contrast to transverse markings, result in lower vehicle speeds, smoother deceleration, and more effectively stimulate drivers' intention to change lanes, guiding them to make the final lane change earlier. Based on the comprehensive evaluation results, it is recommended that transverse or fishbone deceleration markings be considered in engineering practice. These markings have not produced significant effects on driver visual fatigue and driving load, with fishbone markings demonstrating superior comprehensive evaluation outcomes. These research findings can provide valuable insights for future expressway exit area marking design schemes, further enhancing driver safety.

Investigating the influence of eye-catching effect on mental workload in highway tunnel entrances: a comprehensive analysis of eye blink behavior

OBJECTIVE

The objective of this study was to investigate the relationship between eye-catching effects and mental workload at highway tunnel entrances. Specifically, the study aimed to analyze drivers' eye blink behavior to gain a comprehensive understanding of how visual attraction at tunnel entrances affects cognitive workload.

METHODS

50 participants were recruited for the naturalistic driving experiment. Four different visually attractive driving scenarios (baseline, landscape-style architecture, tip slogan, and billboard) were selected. Eye-tracking technology was utilized to record and analyze the eye blink behavior of participating drivers. Various metrics, including blink frequency, blink duration, inter-blink interval, and pupil diameter after a blink, were measured and compared across different scenarios.

RESULTS

The results of the study demonstrated significant differences in drivers' eye blink behavior across the different experimental scenarios, indicating the influence of visual attraction conditions on mental workload. The presence of eye-catching stimuli (landscape-style architecture, tip slogan, and billboard scenarios) at tunnel entrances resulted in decreased blink frequency, shorter blink duration, longer inter-blink intervals, and larger pupil diameter after a blink compared to when no specific eye-catching stimuli were present (baseline condition). These findings suggest that visual attractions capture drivers' attention, leading to increased cognitive workload and attentional demands.

CONCLUSIONS

The findings of this study contribute to the existing literature on driver attention and mental workload, particularly in relation to eye-catching effect in tunnel environments. The presence of eye-catching stimuli at tunnel entrances can distract drivers and increase their mental workload, potentially compromising driving performance and safety. It is crucial for transportation authorities and designers to carefully consider the design and placement of visual attractions in tunnel entrances to minimize distraction and cognitive workload. By doing so, driving safety and performance can be enhanced in tunnel entrances.

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network

In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain-computer interface in autonomous driving.

Modeling distracted driving behavior considering cognitive processes

The modeling of distracted driving behavior has been studied for many years, however, there remain many distraction phenomena that can not be fully modeled. This study proposes a new method that establishes the model using the queuing network model human processor (QN-MHP) framework. Unlike previous models that only consider distracted-driving-related human factors from a mathematical perspective, the proposed method reflects the information processing in the human brain, and simulates the distracted driver's cognitive processes based on a model structure supported by physiological and cognitive research evidence. Firstly, a cumulative activation effect model for external stimuli is adopted to mimic the phenomenon that a driver responds only to stimuli above a certain threshold. Then, dual-task queuing and switching mechanisms are modeled to reflect the cognitive resource allocation under distraction. Finally, the driver's action is modeled by the Intelligent Driver Model (IDM). The model is developed for visual distraction auditory distraction separately. 773 distracted car-following events from the Shanghai Naturalistic Driving Study data were used to calibrate and verify the model. Results show that the model parameters are more uniform and reasonable. Meanwhile, the model accuracy has improved by 57% and 66% compared to the two baseline models respectively. Moreover, the model demonstrates its ability to generate critical pre-crash scenarios and estimate the crash rate of distracted driving. The proposed model is expected to contribute to safety research regarding new vehicle technologies and traffic safety analysis.

Effects of lane-change scenarios on lane-change decision and eye movement

Improper lane-change manoeuvre can cause traffic safety issues and even lead to serious traffic collisions. Quantifying the decision behaviour and eye movements can provide a deeper understanding of lane-change manoeuvre in vehicle interaction environment. The purpose of this study was to investigate the effect of lane-change scenarios defined by gaps on lane-change decision and eye movements. Twenty-eight participants were recruited to complete a naturalistic driving experiment. Eye movements and lane-change decision duration (LDD) were recorded and analysed. Results suggested that the scanning frequency (SF) and saccade duration (SD) were the sensitive parameters to respond to lane-change scenarios. LDD was significantly affected by the scenario, SF, and SD. The increase in LDD was related to the high difficulty gap and high frequency scanning of multiple regions. These findings evaluated the driver's decision performance in response to different lane-change environments and provided valuable information for measuring the driver's scenario perception ability.Practitioner summary: A naturalistic driving experiment was conducted to evaluate the interaction of lane-change decision, eye movement, and lane changing gap in a lane-change task. The results reveal the sensitive eye movement parameters to lane-change scenario, which provide guidelines for driver's perception ability test and professional driver assessment.

Input modality matters: A comparison of touch, speech, and gesture based in-vehicle interaction

Innovative input devices are being available for in-vehicle information systems (IVISs). While they have the potential to provide enjoyable driving by enabling drivers to perform non-driving related tasks (NDRTs) in more natural ways, the associated distracting effects should be paid with more attention. The purpose of this exploratory study was to compare the effects of three novel input modalities, i.e., touchscreen-based interaction (TBI), speech-based interaction (SBI), and gesture-based interaction (GBI), on driving performance and driver visual behaviors. Moreover, we examined if the influence of different modalities would be moderated by the difficulty level of NDRTs. A total of 36 participants were invited to a simulated driving experiment where they were randomly assigned to one of the four groups (TBI, GBI, SBI or baseline) and completed three driving trials. The results showed that TBI led to the worse driving performance, as indicated by the significantly prolonged reaction time, reduced minimum time-to-collision, and increased variations in both longitudinal and lateral vehicle control. The deteriorated driving performance could be attributed, at least partially, to the intense visual demand induced by looking towards the touchscreen, as indicated by more and longer off-the-road glances. The adverse impacts of GBI were relatively smaller, but it still posed great crash risk by leading to a shorter minimum time-to-collision and less stable vehicle control compared to the baseline. SBI, although not completely equivalent to the baseline group, showed the minimum influence on driving and visual performance. Only very few interaction effects were found, suggesting that the effects of modality were quite robust across different NDRTs. It was concluded that SBI and GBI provided safer alternatives to in-vehicle interaction than TBI.

Distraction "Hangover": Characterization of the Delayed Return to Baseline Driving Risk After Distracting Behaviors

OBJECTIVE

We measured how long distraction by a smartphone affects simulated driving behaviors after the tasks are completed (i.e., the distraction hangover).

BACKGROUND

Most drivers know that smartphones distract. Trying to limit distraction, drivers can use hands-free devices, where they only briefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating and back to driving adds an underappreciated, potentially long period to the total distraction time.

METHOD

Ninety-seven 21- to 78-year-old individuals who self-identified as active drivers and smartphone users engaged in a simulated driving scenario that included smartphone distractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eye tracking.

RESULTS

The participants' lateral speed was larger than baseline for 15 s after the end of a voice distraction and for up to 25 s after a text distraction. Correct identification of peripheral cues dropped about 5% per decade of age, and participants from the 71+ age group missed seeing about 50% of peripheral cues within 4 s of the distraction. During distraction, coherence with the lead car in a following task dropped from 0.54 to 0.045, and seven participants rear-ended the lead car. Breadth of scanning contracted by 50% after distraction.

CONCLUSION

Simulated driving performance drops dramatically after smartphone distraction for all ages and for both voice and texting.

APPLICATION

Public education should include the dangers of any smartphone use during driving, including hands-free.

Predicting collision cases at unsignalized intersections using EEG metrics and driving simulator platform

Unsignalized intersection collision has been one of the most dangerous accidents in the world. How to identify road hazards and predict the potential intersection collision ahead are challenging problems in traffic safety. This paper studies the feasibility of EEG metrics to forecast road hazards and presents an improved neural network model to predict intersection collision based on EEG metrics and driving behavior. It is demonstrated that EEG metrics show significant differences between collision and non-collision cases. It indicates that EEG metrics can serve as effective indicators to predict the collision probability. The drivers with higher relative power in fast frequency band (alpha and beta), lower relative power in slow frequency band (delta and theta) are more likely to have conflicts. The prediction using three machine learning models (Multi-layer perceptron (MLP), Logistic regression (LR) and Random forest (RF)) based on three input datasets (only EEG metrics, only driving behavior and combined EEG metrics with driving behavior) are compared. The results show that for single time point prediction, MLP model has the highest accuracy among three machine learning models. The model solely based on EEG metrics datasets has higher accuracy than driving behavior as well as combined datasets. However, for multi-time point prediction, the accuracy of MLP is only 73.9%, worse than LR and RF. We improved the MLP model by adding attention mechanism layer and using random forest model to select important features. As a consequence, the accuracy is greatly improved and reaches 88%. This study demonstrates the importance and feasibility of EEG signals to identify unsafe drivers ahead. The improved neural network model can be helpful to reduce intersection accidents and improve traffic safety.

The Application of Electroencephalogram in Driving Safety: Current Status and Future Prospects

The driver is one of the most important factors in the safety of the transportation system. The driver's perceptual characteristics are closely related to driving behavior, while electroencephalogram (EEG) as the gold standard for evaluating human perception is non-deceptive. It is essential to study driving characteristics by analyzing the driver's brain activity pattern, effectively acquiring driver perceptual characteristics, creating a direct connection between the driver's brain and external devices, and realizing information interchange. This paper first introduces the theories related to EEG, then reviews the applications of EEG in scenarios such as fatigue driving, distracted driving, and emotional driving. The limitations of existing research have been identified and the prospect of EEG application in future brain-computer interface automotive assisted driving systems have been proposed. This review provides guidance for researchers to use EEG to improve driving safety. It also offers valuable suggestions for future research.

A framework for rigorous evaluation of human performance in human and machine learning comparison studies

Rigorous comparisons of human and machine learning algorithm performance on the same task help to support accurate claims about algorithm success rates and advances understanding of their performance relative to that of human performers. In turn, these comparisons are critical for supporting advances in artificial intelligence. However, the machine learning community has lacked a standardized, consensus framework for performing the evaluations of human performance necessary for comparison. We demonstrate common pitfalls in a designing the human performance evaluation and propose a framework for the evaluation of human performance, illustrating guiding principles for a successful comparison. These principles are first, to design the human evaluation with an understanding of the differences between human and algorithm cognition; second, to match trials between human participants and the algorithm evaluation, and third, to employ best practices for psychology research studies, such as the collection and analysis of supplementary and subjective data and adhering to ethical review protocols. We demonstrate our framework’s utility for designing a study to evaluate human performance on a one-shot learning task. Adoption of this common framework may provide a standard approach to evaluate algorithm performance and aid in the reproducibility of comparisons between human and machine learning algorithm performance.

Multiple levels of mental attentional demand modulate peak saccade velocity and blink rate

Every day we mentally process new information that needs to be attended, encoded and retrieved. Processing demands depend on the amount of information and the mental attentional capacity of the individual. Research shows that eye movement indices such as peak saccade velocity and blink rate are related to processes of attentional control, however it is still unclear how eye movements are affected by graded changes in task demand. We examine for the first time relations of eye movements to mental attentional tasks with six levels of task demand and two interference conditions. We report data on 57 adults who completed two versions of the color matching task and provided subjective self rating for each mental attentional demand level. Results show that peak saccade velocity and blink rate decrease as a function of mental attentional demand and correlate negatively with self rating of mental effort. Theoretically, new findings related to mental attentional demand and eye movements inform models of visual processing and cognition. Practically, results point to directions for further research to better understand complex relations among eye movements and mental attentional demand in pediatric populations and individuals with cognitive deficits.

Determining the risk of driver-at-fault events associated with common distraction types using naturalistic driving data

INTRODUCTION

Studies thus far have focused on automobile accidents that involve driver distraction. However, it is hard to discern whether distraction played a role if fault designation is missing because an accident could be caused by an unexpected external event over which the driver has no control. This study seeks to determine the effect of distraction in driver-at-fault events.

METHOD

Two generalized linear mixed models, one with at-fault safety critical events (SCE) and the other with all-cause SCEs as the outcomes, were developed to compare the odds associated with common distraction types using data from the SHRP2 naturalistic driving study.

RESULTS

Adjusting for environment and driver variation, 6 of 10 common distraction types significantly increased the risk of at-fault SCEs by 20-1330%. The three most hazardous sources of distraction were handling in-cabin objects (OR = 14.3), mobile device use (OR = 2.4), and external distraction (OR = 1.8). Mobile device use and external distraction were also among the most commonly occurring distraction types (10.1% and 11.0%, respectively).

CONCLUSIONS

Focusing on at-fault events improves our understanding of the role of distraction in potentially avoidable automobile accidents. The in-cabin distraction that requires eye-hand coordination presents the most danger to drivers' ability in maintaining fault-free, safe driving. Practical Applications: The high risk of at-fault SCEs associated with in-cabin distraction should motivate the smart design of the interior and in-vehicle information system that requires less visual attention and manual effort.

A cross-cultural comparison of visual search strategies and response times in road hazard perception testing

Road hazard perception is considered the most prominent higher-order cognitive skill related to traffic-accident involvement. Regional cultures and social rules that govern acceptable behavior may influence drivers' interpretation of a traffic situation and, consequently, the correct identification of potentially hazardous situations. Here, we aimed to compare hazard perception skills among four European countries that differ in their traffic culture, policies to reduce traffic risks, and fatal crashes: Ukraine, Italy, Spain, and Sweden. We developed a static hazard perception test in which driving scenes with different levels of braking affordance were presented while drivers' gaze was recorded. The test required drivers to indicate the action they would undertake: to brake vs. to keep driving. We assessed 218 young adult drivers. Multilevel models revealed that the scenes' levels of braking affordance (i.e., road hazard) modulated drivers' behavior. As the levels of braking affordance increased, drivers' responses became faster and their gaze entropy decreased (i.e., visual search strategy became less erratic). The country of origin influenced these effects. Ukrainian drivers were the fastest and Swedish drivers were the slowest to respond. For all countries, the decrement in response times was less marked in the case of experienced drivers. Also, Spanish drivers showed the most structured (least erratic) visual search strategy, whereas the Italians had the most rigid (most constant) one. These results suggest that road hazard perception can be defined cross-culturally, with cultural factors (e.g., traffic climate, legislation) modulating response times and visual search strategies. Our results also support the idea that a multimodal assessment methodology is possible for mass testing of road hazard perception and its outcomes would be relevant to understand how different traffic cultures shape driving behavior.