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In-vehicle traffic lights (IVTLs) have been identified as a potential means of eco-driving. However, the extent to which they influence driving characteristics in the event of obstructed on-road traffic lights (ORTLs) has yet to be fully examined. Firstly, the situation of partially deployed IVTLs in both vehicles was analyzed to identify the factors that affect driving characteristics. Through the following distance model, relative vehicle speed, acceleration and deceleration, and following distance were recognized as the contributing factors. The evaluation indicators for driving characteristics were thereby further established. Then, a hardware-in-the-loop simulation platform was built using PreScan 8.5-MATLAB/Simulink R2018b joint simulation software and a Logitech G29 device. IVTLs were implemented using modules in the joint simulation software. Finally, under the scenarios of obstructed ORTLs and various deployment conditions of IVTLs, the original data were collected from 50 experimental subjects with simulated driving. The subjects included 25 males and 25 females, all of whom were non-professional drivers, with ages ranging from 20 to 40 years old. The conclusion was reached that IVTLs could improve driving comfort by approximately 10% in sunny weather (p = 0.008 < 0.05, p = 0.023 < 0.05; p = 0.046 < 0.05, p = 0.001 < 0.05), driving maneuverability by approximately 30% in foggy weather (p = 0.033 < 0.05), and driving safety by approximately 50% in the ORTLs obstructed by a truck scenario (p = 0.019 < 0.05). In general, even if only one vehicle was equipped with IVTLs, certain gain effects on the driving characteristics of both vehicles could still be provided.

Investigations of the neuro-physiological correlates of mental loads, or states, have attracted significant attention recently, as it is particularly important to evaluate mental fatigue in drivers operating a motor vehicle. In this research, we collected multimodal EEG/ECG/EOG and fNIRS data simultaneously to develop algorithms to explore neuro-physiological correlates of drivers' mental states. Each subject performed simulated driving under two different conditions (well-rested and sleep-deprived) on different days. During the experiment, we used 68 electrodes for EEG/ECG/EOG and 8 channels for fNIRS recordings. We extracted the prominent features of each modality to distinguish between the well-rested and sleep-deprived conditions, and all multimodal features, except EOG, were combined to quantify mental fatigue during driving. Finally, a novel driving condition level (DCL) was proposed that distinguished clearly between the features of well-rested and sleep-deprived conditions. This proposed DCL measure may be applicable to real-time monitoring of the mental states of vehicle drivers. Further, the combination of methods based on each classifier yielded substantial improvements in the classification accuracy between these two conditions.

Rationale Zuranolone is an oral positive allosteric modulator of GABA A receptors. Due to its central nervous system (CNS) activity, zuranolone may impact activities requiring complex cognition, including driving. Objective Evaluate the effect of zuranolone on simulated driving performance. Methods In this randomized, double-blind, active- and placebo-controlled, four-period crossover study, treatments included once-nightly zuranolone 50 mg on days 1–7, zuranolone 50 mg on days 1–6 and zuranolone 100 mg on day 7, zopiclone 7.5 mg on days 1 and 7, and placebo on days 1–7. Driving was assessed using a validated simulator. Primary endpoint was standard deviation of lateral position (SDLP), evaluated 9 h post-dose on days 2 and 8. Secondary endpoints included additional driving assessments, cognitive tests, pharmacokinetics, and safety. Results Healthy adults ( N  = 67) enrolled and received ≥ 1 dose. Zuranolone 50 mg increased SDLP versus placebo on days 2 (least squares mean difference [LSMD]: 7.4 cm; p  < 0.0001) and 8 (LSMD: 4.6 cm; p  = 0.0106). Zuranolone 100 mg evoked a larger increase in SDLP versus placebo on day 8 (LSMD 18.9 cm; p  < 0.0001). Reduced performance in other driving assessments and cognition were observed with zuranolone 50 mg on day 2; many resolved by day 8. Despite the SDLP observations, most participants judged themselves capable of driving. Frequent adverse events (≥ 20%) were CNS-related; most were mild/moderate. Conclusion Zuranolone impaired simulated driving and reduced cognitive function versus placebo 9 h after administration. Although many impairments resolved after 7 days of dosing, driving remained impaired. These results may inform prescriber decision-making.

Driver fatigue and negative emotions are significant factors contributing to traffic accidents. In-vehicle fragrance, as a fatigue intervention strategy, can help improve drivers’ mental and emotional states, preventing accidents. However, there is a lack of systematic research on how different fragrance types and release methods affect drivers’ fatigue and emotional development. Forty healthy drivers (mean age: 31 years, gender balanced) participated in this study. Participants were randomly assigned to two groups: one group tested three different fragrance types (HINOKI, GRASSY, YUZU), and the other group tested three fragrance release methods (CR: continuous release, IR: intermittent release, and PR: pulse release). All participants completed a simulated driving task under specified in-vehicle fragrance management conditions. Subjective fatigue ratings and emotional self-assessments (POMS) were used to assess changes in fatigue and emotions, and heart rate variability (HRV) was measured to evaluate physiological changes. Compared to no intervention, fragrance intervention significantly reduced drivers’ subjective fatigue ratings, with the continuous release mode showing a more pronounced reduction in fatigue scores. Fragrance intervention effectively improved heart rate variability, with significant differences observed between release modes. The fragrance intervention also had a significant effect on emotional ratings, notably increasing vigor and reducing negative emotions such as tension and anxiety. The impact of fragrance type on fatigue scores, HRV, and emotional ratings was limited, suggesting that the effectiveness of fragrance intervention may depend more on the intensity and release mode of the intervention rather than the fragrance type. Fragrance intervention effectively reduces driver fatigue and improves emotional states, with the continuous release mode showing the most significant effects. The findings of this study can provide valuable insights for customizing in-vehicle fragrance release strategies to alleviate fatigue and improve emotional well-being in individuals engaged in long-duration driving tasks, with significant implications for the management of drivers’ mental and psychological health.

The drivers’ distraction plays a crucial role in road safety as it is one of the main impacting causes of road accidents. The phenomenon of distraction encompasses both psychological and environmental factors and, therefore, addressing the complex interplay contributing to human distraction in automotive is crucial for developing technologies and interventions for improving road safety. In scientific literature, different works were proposed for the distraction characterization in automotive, but there is still the lack of a univocal measure to assess the degree of distraction, nor a gold-standard tool that allows to “detect” eventual events, road traffic, and additional driving tasks that might contribute to the drivers’ distraction. Therefore, the present study aimed at developing an EEG-based “Distraction index” obtained by the combination of the driver’s mental workload and attention neurometrics and investigating and validating its reliability by analyzing together subjective and behavioral measures. A total of 25 licensed drivers were involved in this study, where they had to drive in two different scenarios, i.e., City and Highway, while different secondary tasks were alternatively proposed in addition to the main one to modulate the driver’s attentional demand. The statistical analysis demonstrated the reliability of the proposed EEG-based distraction index in identifying the drivers’ distraction when driving along different roads and traffic conditions (all p < 0.001). More importantly, the proposed index was demonstrated to be reliable in identifying which are the most impacting additional driving tasks on the drivers’ distraction (all p < 0.01).

Background Current research related to electroencephalogram (EEG)-based driver’s emergency braking intention detection focuses on recognizing emergency braking from normal driving, with little attention to differentiating emergency braking from normal braking. Moreover, the classification algorithms used are mainly traditional machine learning methods, and the inputs to the algorithms are manually extracted features. Methods To this end, a novel EEG-based driver’s emergency braking intention detection strategy is proposed in this paper. The experiment was conducted on a simulated driving platform with three different scenarios: normal driving, normal braking and emergency braking. We compared and analyzed the EEG feature maps of the two braking modes, and explored the use of traditional methods, Riemannian geometry-based methods, and deep learning-based methods to predict the emergency braking intention, all using the raw EEG signals rather than manually extracted features as input. Results We recruited 10 subjects for the experiment and used the area under the receiver operating characteristic curve (AUC) and F1 score as evaluation metrics. The results showed that both the Riemannian geometry-based method and the deep learning-based method outperform the traditional method. At 200 ms before the start of real braking, the AUC and F1 score of the deep learning-based EEGNet algorithm were 0.94 and 0.65 for emergency braking vs. normal driving, and 0.91 and 0.85 for emergency braking vs. normal braking, respectively. The EEG feature maps also showed a significant difference between emergency braking and normal braking. Overall, based on EEG signals, it was feasible to detect emergency braking from normal driving and normal braking. Conclusions The study provides a user-centered framework for human–vehicle co-driving. If the driver's intention to brake in an emergency can be accurately identified, the vehicle's automatic braking system can be activated hundreds of milliseconds earlier than the driver's real braking action, potentially avoiding some serious collisions.

Rationale Alcohol increases the tendency for risky driving in some individuals but not others. Little is known about the factors underlying this individual difference. Studies find that those who underestimate their blood alcohol concentration (BAC) following a dose of alcohol tend to be more impulsive and report greater willingness to drive after drinking than those who estimate their BACs to be greater than their actual BAC. BAC underestimation could contribute to risky driving behavior following alcohol as such drivers might perceive little impairment in their driving ability and thus no need for caution. Objectives This study was designed to test the relationship between drivers’ BAC estimations following a dose of alcohol or a placebo and the degree of risky driving they displayed during a simulated driving test. Methods Forty adult drivers performed a simulated driving test and estimated their blood alcohol concentration after receiving a dose of alcohol (0.65 g/kg for men and 0.56 g/kg for women) or a placebo. Results Alcohol increased risk-taking and impaired driving skill. Those who estimated their BAC to be lower were the riskiest drivers following both alcohol and placebo. Conclusions The tendency to estimate lower BACs could support a series of high-risk decisions, regardless of one’s actual BAC. This could include the decision to drive after drinking.

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers’ cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the “fatigued” spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery).

The human factor plays a key role in the automotive field since most accidents are due to drivers' unsafe and risky behaviors. The industry is now pursuing two main solutions to deal with this concern: in the short term, there is the development of systems monitoring drivers' psychophysical states, such as inattention and fatigue, and in the medium-long term, there is the development of fully autonomous driving. This second solution is promoted by recent technological progress in terms of Artificial Intelligence and sensing systems aimed at making vehicles more and more accurately aware of their “surroundings.” However, even with an autonomous vehicle, the driver should be able to take control of the vehicle when needed, especially during the current transition from the lower (SAE < 3) to the highest level (SAE = 5) of autonomous driving. In this scenario, the vehicle has to be aware not only of its “surroundings” but also of the driver's psychophysical state, i.e., a user-centered Artificial Intelligence. The neurophysiological approach is one the most effective in detecting improper mental states. This is particularly true if considering that the more automatic the driving will be, the less available the vehicular data related to the driver's driving style. The present study aimed at employing a holistic approach, considering simultaneously several neurophysiological parameters, in particular, electroencephalographic, electrooculographic, photopletismographic, and electrodermal activity data to assess the driver's mental fatigue in real time and to detect the onset of fatigue increasing. This would ideally work as an information/trigger channel for the vehicle AI. In all, 26 professional drivers were engaged in a 45-min-lasting realistic driving task in simulated conditions, during which the previously listed biosignals were recorded. Behavioral (reaction times) and subjective measures were also collected to validate the experimental design and to support the neurophysiological results discussion. Results showed that the most sensitive and timely parameters were those related to brain activity. To a lesser extent, those related to ocular parameters were also sensitive to the onset of mental fatigue, but with a delayed effect. The other investigated parameters did not significantly change during the experimental session.

The driver’s attentional state is a significant human factor in traffic safety. The executive control process is a crucial sub-function of attention. To explore the relationship between the driver’s driving performance and executive control function, a total of 35 healthy subjects were invited to take part in a simulated driving experiment and a task-cuing experiment. The subjects were divided into three groups according to their driving performance (aberrant driving behaviors, including lapses and errors) by the clustering method. Then the performance efficiency and electroencephalogram (EEG) data acquired in the task-cuing experiment were compared among the three groups. The effect of group, task transition types and cue-stimulus intervals (CSIs) were statistically analyzed by using the repeated measures analysis of variance (ANOVA) and the post hoc simple effect analysis. The subjects with lower driving error rates had better executive control efficiency as indicated by the reaction time (RT) and error rate in the task-cuing experiment, which was related with their better capability to allocate the available attentional resources, to express the external stimuli and to process the information in the nervous system, especially the fronto-parietal network. The activation degree of the frontal area fluctuated, and of the parietal area gradually increased along with the increase of CSI, which implied the role of the frontal area in task setting reconstruction and working memory maintaining, and of the parietal area in stimulus–Response (S–R) mapping expression. This research presented evidence of the close relationship between executive control functions and driving performance.