Search

Abstract Background Concerns persist regarding the potential reduction in driving performance due to taking second-generation antihistamines or performing hands-free calling. Previous studies have indicated a potential risk to driving performance under an emergency event when these two factors are combined, whereas a non-emergency event was operated effectively. Currently, there is a lack of a discriminative index capable of detecting the potential risks of driving performance impairment. This study aims to investigate the relationship between driving performance and eye movements under combined conditions of taking second-generation antihistamines and a calling task, and to assess the usefulness of eye movement measurements as a discriminative index for detecting potential risks of driving performance impairment. Methods Participants engaged in a simulated driving task, which included a calling task, both under taking or not taking second-generation antihistamines. Driving performance and eye movements were monitored during both emergency and non-emergency events, assessing their correlation between driving performance and eye movements. The study further evaluated the usefulness of eye movement as a discriminative index for potential driving impairment risk through receiver operating characteristic (ROC) analysis. Results In the case of a non-emergency event, no correlation was observed between driving performance and eye movement under the combined conditions. Conversely, a correlation was observed during an emergency event. The ROC analysis, conducted to assess the discriminative index capability of eye movements in detecting the potential risk of driving performance impairment, demonstrated a high discriminative power, with an area under the curve of 0.833. Conclusions The findings of this study show the correlation between driving performance and eye movements under the concurrent influence of second-generation antihistamines and a calling task, suggesting the usefulness of eye movement measurement as a discriminant index for detecting potential risks of driving performance impairment.

Abstract In practical deep-learning applications, such as medical image analysis, autonomous driving, and traffic simulation, the uncertainty of a classification model’s output is critical. Evidential deep learning (EDL) can output this uncertainty for the prediction; however, its accuracy depends on a user-defined threshold, and it cannot handle training data with unknown classes that are unexpectedly contaminated or deliberately mixed for better classification of unknown class. To address these limitations, I propose a classification method called modified-EDL that extends classical EDL such that it outputs a prediction, i.e. an input belongs to a collective unknown class along with a probability. Although other methods handle unknown classes by creating new unknown classes and attempting to learn each class efficiently, the proposed m-EDL outputs, in a natural way, the “uncertainty of the prediction” of classical EDL and uses the output as the probability of an unknown class. Although classical EDL can also classify both known and unknown classes, experiments on three datasets from different domains demonstrated that m-EDL outperformed EDL on known classes when there were instances of unknown classes. Moreover, extensive experiments under different conditions established that m-EDL can predict unknown classes even when the unknown classes in the training and test data have different properties. If unknown class data are to be mixed intentionally during training to increase the discrimination accuracy of unknown classes, it is necessary to mix such data that the characteristics of the mixed data are as close as possible to those of known class data. This ability extends the range of practical applications that can benefit from deep learning-based classification and prediction models.

There has been an increase in the number of on-road vehicles of all types, especially in some developing countries. The rise of traffic heterogeneity causes larger mixed traffic congestion. This study examines the impact of next-nearest leading vehicles on the driving of following drivers in mixed traffic. Although previous studies reported that traffic stability can be improved with the introduction of followers' anticipatory driving that refers to multiple leaders, the effect of anticipatory driving on mixed traffic has not yet been examined. Using data collected from experiments conducted with groups of two and three vehicles, we found that operational delay, maximum acceleration and deceleration of the followers were affected by the presence of next-nearest leaders. In addition, we developed regression models of the affected followers' behaviours with respect to the next-nearest leaders and identified the factors affecting these behaviours. For example, the followers’ deceleration is directly affected by the height of the next-nearest leading vehicles. Hence, the model parameters for determining the deceleration of following vehicles should take the height of the next-nearest leading vehicle into consideration. Finally, based on the regression models, we estimated values of parameters in the intelligent driver model when the type of the next-nearest leader was changed. Stability analysis based on these estimated parameters implied that a tall or short next-nearest leader with a large engine power would stabilise traffic when anticipatory driving of followers is possible. Keywords: Transportation engineering, Mixed traffic, Car-following behaviour, Next-nearest leader, Traffic stability, Intelligent driver model

Objective: Although second-generation antihistamines have reduced sedation-related side effects compared to first-generation antihistamines, sedation may still impair motor vehicle driving performance. Moreover, receiving/making phone calls using a hands-free function can negatively affect driving performance. Therefore, herein, driving performance was evaluated using a driving simulator to gain insights into the hazards of driving by combining second-generation antihistamines and a calling task, i.e., simulated calls using a hands-free function. Methods: In this study, 20 subjects drove in a driving simulator in the absence or presence of a calling task while taking or not taking second-generation antihistamines. Driving performances for nonemergency and emergency events were determined, and a comparative analysis of intra-individual variability when taking and not taking second-generation antihistamines was conducted. Results: First, when nonemergency and emergency were examined in the absence of a calling task, no significant difference in driving performance was observed between taking and not taking second-generation antihistamines. Next, when the nonemergency event was examined in the presence of a calling task, no significant difference in driving performance was observed between taking and not taking second-generation antihistamines. However, when the emergency event was examined in the presence of a calling task, a significant difference in driving performance was observed between taking and not taking second-generation antihistamines, thus resulting in reduced driving performance. Conclusions: The new system with added calling tasks allowed the extraction of the potential risks of driving performance of second-generation antihistamines that may have been previously overlooked. This study suggests that pharmacists and other healthcare professionals may need to instruct people taking any second-generation antihistamine to focus on driving and not on subtasks that require cognitive load such as talking while driving. [ABSTRACT FROM AUTHOR]

Background: Concerns persist regarding the potential reduction in driving performance due to taking second-generation antihistamines or performing hands-free calling. Previous studies have indicated a potential risk to driving performance under an emergency event when these two factors are combined, whereas a non-emergency event was operated effectively. Currently, there is a lack of a discriminative index capable of detecting the potential risks of driving performance impairment. This study aims to investigate the relationship between driving performance and eye movements under combined conditions of taking second-generation antihistamines and a calling task, and to assess the usefulness of eye movement measurements as a discriminative index for detecting potential risks of driving performance impairment. Methods: Participants engaged in a simulated driving task, which included a calling task, both under taking or not taking second-generation antihistamines. Driving performance and eye movements were monitored during both emergency and non-emergency events, assessing their correlation between driving performance and eye movements. The study further evaluated the usefulness of eye movement as a discriminative index for potential driving impairment risk through receiver operating characteristic (ROC) analysis. Results: In the case of a non-emergency event, no correlation was observed between driving performance and eye movement under the combined conditions. Conversely, a correlation was observed during an emergency event. The ROC analysis, conducted to assess the discriminative index capability of eye movements in detecting the potential risk of driving performance impairment, demonstrated a high discriminative power, with an area under the curve of 0.833. Conclusions: The findings of this study show the correlation between driving performance and eye movements under the concurrent influence of second-generation antihistamines and a calling task, suggesting the usefulness of eye movement measurement as a discriminant index for detecting potential risks of driving performance impairment. [ABSTRACT FROM AUTHOR]

In practical deep-learning applications, such as medical image analysis, autonomous driving, and traffic simulation, the uncertainty of a classification model's output is critical. Evidential deep learning (EDL) can output this uncertainty for the prediction; however, its accuracy depends on a user-defined threshold, and it cannot handle training data with unknown classes that are unexpectedly contaminated or deliberately mixed for better classification of unknown class. To address these limitations, I propose a classification method called modified-EDL that extends classical EDL such that it outputs a prediction, i.e. an input belongs to a collective unknown class along with a probability. Although other methods handle unknown classes by creating new unknown classes and attempting to learn each class efficiently, the proposed m-EDL outputs, in a natural way, the "uncertainty of the prediction" of classical EDL and uses the output as the probability of an unknown class. Although classical EDL can also classify both known and unknown classes, experiments on three datasets from different domains demonstrated that m-EDL outperformed EDL on known classes when there were instances of unknown classes. Moreover, extensive experiments under different conditions established that m-EDL can predict unknown classes even when the unknown classes in the training and test data have different properties. If unknown class data are to be mixed intentionally during training to increase the discrimination accuracy of unknown classes, it is necessary to mix such data that the characteristics of the mixed data are as close as possible to those of known class data. This ability extends the range of practical applications that can benefit from deep learning-based classification and prediction models. [ABSTRACT FROM AUTHOR]

Recently, with the active development of automated driving systems (ADSs) corresponding to SAE automated driving level 2, the comfort of ADSs has gained significant attention. In our previous research, it was proposed that the comfort of ADSs is affected by their maneuvers and the degree of information sharing between ADS and drivers. However, even if the drivers are well-informed of the recognized traffic environment and ADS-controlled trajectory, the comfort and trust of drivers in ADSs could be insufficient. This is because each driver has distinct preferred trajectories. Although some researchers have proposed ADSs that learn driver preferences, the maneuvers presented by these systems are not easily modified, because of off-line learning. In addition, a few quantitative investigations on the subjective evaluation of comfort have been conducted. This study proposes an on-demand learning collision avoidance ADS that learns the preferred maneuvers of drivers through driver intervention. In our system, the drivers teach their preferred trajectory to the system only if they are unsatisfied with the maneuver presented by the system. The system updates the parameters and shows the learned maneuver at the next avoidance. To realize the proposed system, we applied modified risk potential functions and gain-tuning method, as well as a cost function and learning method for gradual and stable maneuver learning. Driving simulator experiments demonstrated stable trajectory learning and smooth intervention. Furthermore, the drivers were satisfied with the learned maneuvers of the ADS, and their comfort and trust in the ADS improved when using the proposed ADS. [ABSTRACT FROM AUTHOR]