Designing an Intelligent System to Detect Stress Levels During Driving

In addition to the devastating effects of anxiety and stress on the development and exacerbation of the cardiovascular disease, lack of stress control increases drivers' risk of accidents. This paper aims to identify the stress of drivers in various driving situations to warn the driver to control the tense conditions during driving. In order to detect stress while driving, we used the heart signals in the Physionet database. To analyze the conditions of the electrocardiogram (ECG) under various driving situations, linear and non-linear features were used. The characteristics of the RRIs are the only able to identify driver stress in different driving modes relative to rest periods, while the return mapping features, in addition to identifying driver stress while resting, have the ability to identify stress between different driving positions also brought. The results showed that driver's stress level during driving in city 1 and highway 1 with a P-value of 0.028 and also in city 3 and highway 2 with a P-value of 0.041 can be distinguished. The accuracy obtained from the proposed detection method is 98±2% for 100 iterations. The result indicated an efficiency of our proposed method and enhanced the reliability.

Sensitivity of Physiological Measures of Acute Driver Stress: A Meta-Analytic Review

Background: The link between driving performance impairment and driver stress is well-established. Identifying and understanding driver stress is therefore of major interest in terms of safety. Although many studies have examined various physiological measures to identify driver stress, none of these has as yet been definitively confirmed as offering definitive all-round validity in practice.Aims: Based on the data available in the literature, our main goal was to provide a quantitative assessment of the sensitivity of the physiological measures used to identify driver stress. The secondary goal was to assess the influence of individual factors (i.e., characteristics of the driver) and ambient factors (i.e., characteristics of the context) on driver stress. Age and gender were investigated as individual factors. Ambient factors were considered through the experimental apparatus (real-road vs. driving simulator), automation driving (manual driving vs. fully autonomous driving) and stressor exposure duration (short vs. long-term).Method: Nine meta-analyses were conducted to quantify the changes in each physiological measure during high-stress vs. low-stress driving. Meta-regressions and subgroup analyses were performed to assess the moderating effect of individual and ambient factors on driver stress.Results: Changes in stress responses suggest that several measures are sensitive to levels of driver stress, including heart rate, R-R intervals (RRI) and pupil diameter. No influence of individual and ambient factors was observed for heart rate.Applications and Perspective: These results provide an initial guide to researchers and practitioners when selecting physiological measures for quantifying driver stress. Based on the results, it is recommended that future research and practice use (i) multiple physiological measures, (ii) a triangulation-based methodology (combination of measurement modalities), and (iii) a multifactorial approach (analysis of the interaction of stressors and moderators).

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers’ future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

Competitive-Blockchain-Based Parking System with Fairness Constraints

Parking slot detection is one of the most popular applications of Vehicular ad Hoc Network re-search field. Proposing smart algorithms for fast parking is crucial not only to facilitate drivers, but also to reduce traffic congestion, pollution, and vehicle energy consumption. Typically, an urban area has several competitive car parks and, in order to make the parking process automatic, a mechanism to ensure a fair competition among them is needed. Among all the methods able to guarantee transparency and equity in a system, blockchain is a robust technology. It has been success- fully applied in many different research fields, from financial to health. In this work, we provide an automaticparking system in which vehicles are allocated among several competitive parking areas (called competitors), through a blockchain-based approach, by applying a consensus mechanism to manage the system modifications. To this aim, two classes of fairness constraints are defined, according to which any new operation on the parking consortium must be approved by the members. Such an approach brings benefits for different reasons, starting from traffic condition improvement, up to driver stress and pollution decrease.

Predictive variables for sleep quality in professional drivers

Antecedentes: Los conductores profesionales suelen padecer problemas para dormir o descansar correctamente. Esto puede deberse a diversos factores tanto personales como específicos de las condiciones laborales. En el presente trabajo nos hemos planteado desarrollar un modelo predictivo sobre la calidad del sueño en conductores profesionales utilizando los indicadores siguientes: Edad, Género, Confort del asiento, suspensión del asiento, Soporte lumbar ajustable del asiento del conductor, Horas de conducción, Problemas musculoesqueléticos, Drivers Stress, Irritación, Personalidad resistente, Burnout, conductas de seguridad e Impulsividad. Método: Los participantes han sido 369 conductores profesionales, de distintos sectores del transporte, obtenidos mediante un muestreo no probabilístico. Se han utilizado el programa SPSS 25.0. Resultados: Se determina la capacidad predictiva de algunas variables que afectan a los conductores sobre la calidad del sueño. Conclusiones: La calidad del sueño se puede predecir a través de determinadas variables, siendo la mejor predictora Exhaustion (Burnout). Esta investigación contribuye a un mayor conocimiento de la calidad del sueño y a la mejora de la salud de los conductores profesionales. Background: Professional drivers often have problems sleeping or resting properly. This may be due to various factors, both personal and specific to their working conditions. In this study, we set out to develop a predictive model for the quality of sleep in professional drivers using the following indicators: Age, Gender, Seat Comfort, Seat Suspension, Adjustable Lumbar Support of the Driver’s Seat, Driving Hours, Musculoskeletal Problems, Driver Stress, Irritation, Resistant Personality, Burnout, Safety Behaviors and Impulsivity. Method: The participants were 369 professional drivers from different transport sectors, obtained through non-probabilistic sampling. The SPSS 25.0 program was used for statistical analysis. Results: The predictive capacity of certain variables that affect drivers’ sleep quality is determined. Conclusions: Sleep quality can be predicted by means of certain variables, the best predictor of which is Exhaustion (Burnout). This research contributes to the body of knowledge on sleep quality and on improving the health of professional drivers.