Concurrent processing of vehicle lane keeping and speech comprehension tasks

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.

Exploring the Timing of Disengagement From Nondriving Related Tasks in Scheduled Takeovers With Pre-Alerts: An Analysis of Takeover-Related Measures

OBJECTIVES

This study aimed to investigate drivers' disengagement from nondriving related tasks (NDRT) during scheduled takeovers and to evaluate its impact on takeover performance.

BACKGROUND

During scheduled takeovers, drivers typically have sufficient time to prepare. However, inadequate disengagement from NDRTs can introduce safety risks.

METHOD

Participants experienced scheduled takeovers using a driving simulator, undergoing two conditions, with and without an NDRT. We assessed their takeover performance and monitored their NDRT disengagement from visual, cognitive, and physical perspectives.

RESULTS

The study examined three NDRT disengagement timings (DTs): DT1 (disengaged before the takeover request), DT2 (disengaged after the request but before taking over), and DT3 (not disengaged). The impact of NDRT on takeover performance varied depending on DTs. Specifically, DT1 demonstrated no adverse effects; DT2 impaired takeover time, while DT3 impaired both takeover time and quality. Additionally, participants who displayed DT1 exhibited longer eye-off-NDRT duration and a higher eye-off-NDRT count during the prewarning stage compared to those with DT2 and DT3.

CONCLUSION

Drivers can benefit from earlier disengagement from NDRTs, demonstrating resilience to the adverse effects of NDRTs on takeover performance. The disengagement of cognition is often delayed compared to that of eyes and hands, potentially leading to DT3. Moreover, visual disengagement from NDRTs during the prewarning stage could distinguish DT1 from the other two.

APPLICATION

Our study emphasizes considering NDRT disengagement in designing systems for scheduled takeovers. Measures should be taken to promote early disengagement, facilitate cognitive disengagement, and employ visual disengagement during the prewarning period as predictive indicators of DTs.

Effects of multitasking on interpreting a spearcon sequence display for monitoring multiple patients

Spearcons are time-compressed speech phrases. When arranged in a sequence representing vital signs of multiple patients, spearcons may be more informative than conventional auditory alarms. However, multiple resource theory suggests that certain timeshared tasks might interfere with listeners' ability to understand spearcons. We tested the relative interference with spearcon identification from the following ongoing tasks: (1) manual tracking, (2) linguistic detection of spoken target words, (3) arithmetic true-false judgments, or (4) an ignored background speech control. Participants were 80 non-clinicians. The linguistic task worsened spearcon identification more than the tracking task, p < .001, and more than ignored background speech, p = .012. The arithmetic task worsened spearcon identification more than the tracking task, p < .001. The linguistic task and arithmetic task both worsened performance, p = .674. However, no ongoing task affected participants' ability to detect which patient(s) in a sequence had abnormal vital signs. Future research could investigate whether timeshared tasks affect non-speech auditory alerts.

Lifestyle Matters: Effects of Habitual Physical Activity on Driving Skills in Older Age

Research on multitasking driving has suggested age-related deterioration in driving performance. It has been shown that physical and cognitive functioning, which are related to driving performance and decline with aging, are positively associated with physical activity behavior. This study aimed to explore whether driving performance decline becomes severe with advancing age and whether physical activity behavior modifies age-related deterioration in driving performance. A total of one hundred forty-one healthy adults were categorized into three groups based on their age; old-old (74.21 ± 2.33 years), young-old (66.53 ± 1.50 years), and young adults (23.25 ± 2.82 years). Participants completed a realistic multitasking driving task. Physical activity and cardiorespiratory fitness levels were evaluated. Older groups drove more slowly and laterally than young adults, and old-old adults drove slower than young-old ones across the whole driving course. Physical activity level did not interact with the aging effect on driving performance, whereas cardiovascular fitness interacted. Higher-fitness young-old and young adults drove faster than higher-fitness old-old adults. Higher-fitness old adults drove more laterally than higher-fitness young adults. The present study demonstrated a gradual decline in driving performance in old adults, and cardiorespiratory fitness interacted with the aging effect on driving performance. Future research on the interaction of aging and physical activity behavior on driving performance in different age groups is of great value and may help deepen our knowledge.

The mere presence of a mobile phone: Does it influence driving performance?

The ubiquity of mobile phones has led to a rapid increase in its presence and use in vehicles, especially among young adults (up to 25 years), who are generally the least experienced group of drivers. The potential for phones to draw attention away from the main driving task has significant consequences for road safety. Previous studies have found that the mere presence of a mobile phone can be distracting by impairing attention in experimental non-driving contexts. However, the effect of phone presence, independent to usage, has not yet been examined in the context of driving. As such, the present study examined whether the mere presence of a mobile phone, its proximity to the driver, and power status (on/off) influenced the driving performance of young drivers. Additionally, this study assessed whether the effects of phone presence and proximity were moderated by an individual's level of dependence on, or emotional attachment to, their phone. A sample of 127 undergraduate psychology students (M = 19.76, SD = 1.63) were randomly assigned to one of four conditions: (1) phone absent (control), (2) phone on, in holder, (3) phone off, in holder, and (4) phone on, in pocket. They all completed the same simulated drive, and were measured for degree of phone dependence and phone emotional attachment. Overall, drivers in all the phone present conditions made significantly more driving errors (speeding and collision) compared to those in the phone absent (control) condition, irrespective of proximity to the phone and whether it was on or off. Phone dependence, but not phone emotional attachment, moderated the effect of phone presence on speeding behaviour. These findings suggest that the mere presence of a phone is distracting for drivers, especially so for those who are highly dependent on their phone, which may place them at a greater risk of a distraction induced crash.

Investigating On-Road Lane Maintenance and Speed Regulation in Post-Stroke Driving: A Pilot Case-Control Study

Stroke can adversely affect the coordination and judgement of drivers due to executive dysfunction, which is relatively common in the post-stroke population but often undetected. Quantitatively examining vehicle control performance in post-stroke driving becomes essential to inspect whether and where post-stroke older drivers are risky. To date, it is unclear as to which indicators, such as lane keeping or speed control, can differentiate the driving performance of post-stroke older drivers from that of normal (neurotypical) older drivers. By employing a case-control design using advanced vehicle movement tracking and analysis technology, this pilot study aimed to compare the variations in driving trajectory, lane keeping and speed control between the two groups of older drivers using spatial and statistical techniques. The results showed that the mean standard deviation of lane deviation (SDLD) in post-stroke participants was higher than that of normal participants in complex driving tasks (U-turn and left turn) but almost the same in simple driving tasks (straight line sections). No statistically significant differences were found in the speed control performance. The findings indicate that, although older drivers can still drive as they need to after a stroke, the decline in cognitive abilities still imposes a higher cognitive workload and more effort for post-stroke older drivers. Future studies can investigate post-stroke adults' driving behaviour at more challenging driving scenarios or design driving intervention programs to improve their executive function in driving.

Bidirectional Interference Between Simulated Driving and Speaking

Purpose This study relied on acoustic measures of connected speech and several indices of driving performance to quantify interference between speaking and simulated driving. Method Three groups of 20 younger (ages 20-30 years), middle-age (ages 40-50 years), and older (ages 60-71 years) adults produced monologues and completed a simulated driving task, which involved maintaining a constant speed and lane position on a freeway. Both tasks were completed separately and concurrently. Results There were significant divided attention effects, with a reduced speaking time ratio, and increases in vocal intensity, speed variability, and steering wheel adjustments. There was a significant between-subjects age effect for intensity and fundamental frequency as the younger group had less variation with these variables compared to the other age groups across conditions. There was a significant between-subjects age effect for lane position, steering wheel position, and speed as the younger group had less variation in lane position compared to the other 2 groups, and the older group had more variation in speed and steering wheel position compared to the other 2 groups across the experimental conditions. Conclusion These findings reveal that divided attention conditions can impact both speech and simulated driving performance. The results also shed some light on the effects of age on speech and driving tasks, although the degree of interference from divided attention did not differ by age.

Effect of Using Mobile Phones on Driver's Control Behavior Based on Naturalistic Driving Data

Distracted driving behaviors are closely related to crash risk, with the use of mobile phones during driving being one of the leading causes of accidents. This paper attempts to investigate the impact of cell phone use while driving on drivers' control behaviors. Given the limitation of driving simulators in an unnatural setting, a sample of 134 cases related to cell phone use during driving were extracted from Shanghai naturalistic driving study data, which provided massive unobtrusive data to observe actual driving process. The process of using mobile phones was categorized into five operations, including dialing, answering, talking and listening, hanging up, and viewing information. Based on the concept of moving time window, the variation of the intensity of control activity, the sensitivity of control operation, and the stability of control state in each operation were analyzed. The empirical results show strong correlation between distracted operations and driving control behavior. The findings contribute to a better understanding of drivers' natural behavior changes with using mobiles, and can provide useful information for transport safety management.

Driving behaviour while self-regulating mobile phone interactions: A human-machine system approach

Mobile phone distracted driving is a recurrent issue in road safety worldwide. Recent research on driving behaviour of distracted drivers suggests that in certain circumstances drivers seem to assume safer behaviours while using a mobile phone. Despite a high volume of research on this topic, self-regulation by mobile phone distracted drivers is not well understood as many driving simulator experiments are designed to impose an equal level of distraction to participants being tested for their driving performance. The aim of this research was to investigate the relationship between self-regulatory secondary task performance and driving. By a driving simulator experiment in which participants were allowed to perform their secondary tasks whenever they feel appropriate, the driving performance of 35 drivers aged 18-29 years was observed under three phone conditions including non-distraction (no phone use), hands-free interactions and visual-manual interactions in the CARRS-Q advanced driving simulator. Drivers' longitudinal and lateral vehicle control observed across various road traffic conditions were then modelled by Generalized Estimation Equations (GEE) with exchangeable correlation structure accounting for heterogeneity resulting from multiple observations from the same driver. Results show that the extent of engagement in the secondary task influence both longitudinal and lateral control of vehicles. Drivers who engaged in a large number of hands-free interactions are found to select lower driving speed. In contrast, longer visual-manual interactions are found to result in higher driving speed among drivers self-regulating their secondary task. Among the road traffic conditions, drivers distracted by their self-regulated secondary tasks are found to select lower speeds along the s-curve compared to straight and motorway segments. In summary, the applied human-machine system approach suggests that road traffic demands play a vital role in both secondary task management and driving performance.

Driver's Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China

Complex traffic situations and high driving workload are the leading contributing factors to traffic crashes. There is a strong correlation between driving performance and driving workload, such as visual workload from traffic signs on highway off-ramps. This study aimed to evaluate traffic safety by analyzing drivers' behavior and performance under the cognitive workload in complex environment areas. First, the driving workload of drivers was tested based on traffic signs with different quantities of information. Forty-four drivers were recruited to conduct a traffic sign cognition experiment under static controlled environment conditions. Different complex traffic signs were used for applying the cognitive workload. The static experiment results reveal that workload is highly related to the amount of information on traffic signs and reaction time increases with the information grade, while driving experience and gender effect are not significant. This shows that the cognitive workload of subsequent driving experiments can be controlled by the amount of information on traffic signs. Second, driving characteristics and driving performance were analyzed under different secondary task driving workload levels using a driving simulator. Drivers were required to drive at the required speed on a designed highway off-ramp scene. The cognitive workload was controlled by reading traffic signs with different information, which were divided into four levels. Drivers had to make choices by pushing buttons after reading traffic signs. Meanwhile, the driving performance information was recorded. Questionnaires on objective workload were collected right after each driving task. The results show that speed maintenance and lane deviations are significantly different under different levels of cognitive workload, and the effects of driving experience and gender groups are significant. The research results can be used to analyze traffic safety in highway environments, while considering more drivers' cognitive and driving performance.

The effects of cognitively demanding dual-task driving condition on elderly people's driving performance; Real driving monitoring

BACKGROUND

Using in-vehicle audio technologies such as audio systems and voice messages is regarded as a common secondary task. Such tasks, known as the sources of non-visual distraction, affect the driving performance. Given the elderly drivers' cognitive limitations, driving can be even more challenging to drivers. The current study examined how listening to economic news, as a cognitively demanding secondary task, affects elderly subjects' driving performance and whether their comprehension accuracy is associated with these effects.

METHODS

Participants of the study (N=22) drove in a real condition with and without listening to economic news. Measurements included driving performance (speed control, forward crash risk, and lateral lane position) and task performance (comprehension accuracy).

RESULTS

The mean driving speed, duration of driving in unsafe zones and numbers of overtaking decreased significantly when drivers were engaged in the dual-task condition. Moreover, the cognitive secondary task led to a higher speed variability. Our results demonstrate that there was not a significant relationship between the lane changes and the activity of listening to economic news. However, a meaningful difference was observed between general comprehension and deep comprehension on the one hand and driving performance on the other. Another aspect of our study concerning the drivers' ages and their comprehension revealed a significant relationship between age above 75 and comprehension level. Drivers aging 75 and older showed a lower level of deep comprehension.

CONCLUSION

Our study demonstrates that elderly drivers compensated driving performance with safety margin adoption while they were cognitively engaged. In this condition, however, maintaining speed proved more demanding for drivers aging 75 and older.

Texting while driving using Google Glass™: Promising but not distraction-free

Texting while driving is risky but common. This study evaluated how texting using a Head-Mounted Display, Google Glass, impacts driving performance. Experienced drivers performed a classic car-following task while using three different interfaces to text: fully manual interaction with a head-down smartphone, vocal interaction with a smartphone, and vocal interaction with Google Glass. Fully manual interaction produced worse driving performance than either of the other interaction methods, leading to more lane excursions and variable vehicle control, and higher workload. Compared to texting vocally with a smartphone, texting using Google Glass produced fewer lane excursions, more braking responses, and lower workload. All forms of texting impaired driving performance compared to undistracted driving. These results imply that the use of Google Glass for texting impairs driving, but its Head-Mounted Display configuration and speech recognition technology may be safer than texting using a smartphone.

Texting while driving: is speech-based text entry less risky than handheld text entry?

Research indicates that using a cell phone to talk or text while maneuvering a vehicle impairs driving performance. However, few published studies directly compare the distracting effects of texting using a hands-free (i.e., speech-based interface) versus handheld cell phone, which is an important issue for legislation, automotive interface design and driving safety training. This study compared the effect of speech-based versus handheld text entries on simulated driving performance by asking participants to perform a car following task while controlling the duration of a secondary text-entry task. Results showed that both speech-based and handheld text entries impaired driving performance relative to the drive-only condition by causing more variation in speed and lane position. Handheld text entry also increased the brake response time and increased variation in headway distance. Text entry using a speech-based cell phone was less detrimental to driving performance than handheld text entry. Nevertheless, the speech-based text entry task still significantly impaired driving compared to the drive-only condition. These results suggest that speech-based text entry disrupts driving, but reduces the level of performance interference compared to text entry with a handheld device. In addition, the difference in the distraction effect caused by speech-based and handheld text entry is not simply due to the difference in task duration.

Modeling Driving and Sentence Comprehension Dual-task Performance in Queueing Network-ACTR

Modeling driving performance in multi-task scenarios is important for both the examination of human performance modeling theories and the evaluation of in-vehicle interfaces. Previous driving performance models mainly focused on driving tasks with perceptual-motor components. The current study focuses on modeling a dual-task driving scenario containing a sentence comprehension component that involves complex cognitive processes. The model was built in Queueing Network-ACTR (QN-ACTR) cognitive architecture implementing a QN filtering discipline that has been previously proposed and tested for scheduling multiple task demands. A comparison of empirical and modeling results demonstrated that this filtering discipline is necessary for modeling the dual-task of lane keeping and sentence comprehension.

Adolescence, attention allocation, and driving safety

Motor vehicle crashes are the leading source of morbidity and mortality in adolescents in the United States and the developed world. Inadequate allocation of attention to the driving task and to driving hazards are important sources of adolescent crashes. We review major explanations for these attention failures with particular focus on the roles that brain immaturity and lack of driving experience play in causing attention problems. The review suggests that the potential for overcoming inexperience and immaturity with training to improve attention to both the driving task and hazards is substantial. Nevertheless, there are large individual differences in both attentional abilities and risky driving tendencies that pose challenges to novice driver policies. Research that can provide evidence-based direction for such policies is urgently needed.