On-Road Detection of Driver Fatigue and Drowsiness during Medium-Distance Journeys

Salvati, Luca; D’Amore, Matteo; Fiorentino, Anita; Pellegrino, Arcangelo; Sena, Pasquale; Villecco, Francesco

doi:10.3390/e23020135

Cited by 37 publications

(35 citation statements)

References 50 publications

(53 reference statements)

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“…With the approach developed in this paper, we can measure the fuzzy cross-efficiency scores for cases of variable returns to scale with fuzzy observation and apply to different fields of engineering applications (e.g., [42][43][44][45][46][47]) for decision-making. In this paper, the inputs and outputs are treated as triangular fuzzy numbers.…”

Section: Discussionmentioning

confidence: 99%

A Fuzzy Approach to Support Evaluation of Fuzzy Cross Efficiency

Liu

2021

Symmetry

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Cross-efficiency evaluation effectively distinguishes a set of decision-making units (DMUs) via self- and peer-evaluations. In constant returns to scale, this evaluation technique is usually applied for data envelopment analysis (DEA) models because negative efficiencies will not occur in this case. For situations of variable returns to scale, the negative cross-efficiencies may occur in this evaluation method. In the real world, the observations could be uncertain and difficult to measure precisely. The existing fuzzy cross-evaluation methods are restricted to production technologies with constant returns to scale. Generally, symmetry is a fundamental characteristic of binary relations used when modeling optimization problems. Additionally, the notion of symmetry appeared in many studies about uncertain theories employed in DEA problems, and this approach can be considered an engineering tool for supporting decision-making. This paper proposes a fuzzy cross-efficiency evaluation model with fuzzy observations under variable returns to scale. Since all possible weights of all DMUs are considered, a choice of weights is not required. Most importantly, negative cross-efficiencies are not produced. An example shows that this paper’s fuzzy cross-efficiency evaluation method has discriminative power in ranking the DMUs when observations are fuzzy numbers.

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Section: Discussionmentioning

confidence: 99%

A Fuzzy Approach to Support Evaluation of Fuzzy Cross Efficiency

Liu

2021

Symmetry

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“…Intensive research has been pointed in this direction. Luca Salvati et al [1] propose an algorithm to detect the sleepiness state of a driver based on the measurement of pulse rate variability generated by heartbeat. Then, the proposed method is validated via an objective indicator (time-mediated percentage of eye closure-named PERCLOS) [2].…”

Section: Literature Survey Related Workmentioning

confidence: 99%

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Minea

Dumitrescu

Costea

2021

Sensors

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Background: The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution. Methods: This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram—EEG, electrocardiogram—ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine. Results: The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance. Conclusion: The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient’s life, with the support of the e-Call system.

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“…In the behavioral analysis industry, HAR is used to monitor and identify family behavior [ 8 ]. In the field of traffic safety [ 9 ], the detection of driver fatigue is helpful to prevent traffic accidents caused by drowsiness during driving.…”

Section: Introductionmentioning

confidence: 99%

Human Behavior Recognition Model Based on Feature and Classifier Selection

Gao

Huan

et al. 2021

Sensors

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With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.

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On-Road Detection of Driver Fatigue and Drowsiness during Medium-Distance Journeys

Cited by 37 publications

References 50 publications

A Fuzzy Approach to Support Evaluation of Fuzzy Cross Efficiency

A Fuzzy Approach to Support Evaluation of Fuzzy Cross Efficiency

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Human Behavior Recognition Model Based on Feature and Classifier Selection

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