A fatigue driving detection method based on Frequency Modulated Continuous Wave radar

Dong, Zhening; Zhang, Meiyan; Sun, Jinwei; Cao, Tianao; Liu, Runqiao; Wang, Qisong; Danliu,

doi:10.1109/iccece51280.2021.9342080

Cited by 11 publications

(6 citation statements)

References 2 publications

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“…Driver attention monitoring studies have focused on radarbased vital sign recognition [24], [25], [26], monitoring of driver head movements [27], eye blinking [28], [29], [30], [31], fatigue, concentration and drowsiness [32], [33], [34], [35], and health indicators, such as blood pressure [36] and blood glucose levels [37]. The presence of body movements and respiration effects both the measurement of heart rate as well as that of eye blinking frequency and duration.…”

Section: B Automotive-internal Perceptionmentioning

confidence: 99%

Sensing and Machine Learning for Automotive Perception: A Review

et al. 2023

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Automotive perception involves understanding the external driving environment and the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This article provides an overview of different sensor modalities, such as cameras, radars, and light detection and ranging (LiDAR) used commonly for perception, along with the associated data processing techniques. Critical aspects of perception are considered, such as architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches, given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.

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Section: B Automotive-internal Perceptionmentioning

confidence: 99%

Sensing and Machine Learning for Automotive Perception: A Review

et al. 2023

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“…The ease of signal acquisition with this one depends greatly on the type of vehicle, the driver's skill, and the road conditions. Measurements of visual information including those related to the eyes, yawning detection [10], and facial expression are appropriate for practical applications since they are automated and also don't distract the driver.…”

Section: Introductionmentioning

confidence: 99%

Review on Neural Network based Detection System for Intoxicated Driving

Hari¹,

Thomas²

2023

2023 International Conference on Control, Communication and Computing (ICCC)

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Driving situations include a lot of safety considerations. One of the major factors in road accidents is drunk driving by drivers of vehicles. Due to advancements, notably in the area of biometrics, it is now possible to determine a person's state of drunkenness on alcohol. Therefore, creating an intelligent system to address this issue is important. These systems generally employ artificial vision or sensor networks. For the purpose of evaluating the collected data, we additionally utilize feature selection and supervised classification methods. Every technique and algorithm has been created to use the least amount of analytical resources feasible because the whole acquisition and analysis process will be completed within an embedded device.This paper provides an overview of current developments in the field of neural network-based driver intoxication detection.Different methods for detecting drunkenness have been examined, and potential areas for improvement have been identified.

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“…An apparent disadvantage for the eyes and mouth state detection is the lower accuracy of detection under face occlusions such as wearing a mask or eyeglasses. Moreover, such measures have lower accuracy during the night, which is the most critical time to detect a driver's tiredness where this system depends on the machine vision technology (Dong et al 2021) as it is affected by the surrounding brightness (Ren et al 2021).…”

Section: Literature Review Of Drivers' Psychophysiological State Dete...mentioning

confidence: 99%

“…According to Dong et al (2021), respiration and heartbeat signals can be used as aid detection measures to avoid some of the eye detection measures' failures. Respiration and heartbeat signals can be measured by millimeter-wave radar placed in front of the driver by 50 to 80 cm that transmits a signal reflected on the drivers' body to produce an echo signal that is received back again by the radar front end.…”

Section: Literature Review Of Drivers' Psychophysiological State Dete...mentioning

confidence: 99%

“…Logistic Regression (LR) is considered one of the simplest machine learning algorithms that determine the best boundary between the data (Bamidele et al 2019). It is used to classify drivers' states using respiration and heartbeat signals (Dong et al 2021) in addition to sEMG (Lu et al 2021).…”

Section: Analysis Of Drivers' Psychophysiological State Detection Mea...mentioning

confidence: 99%

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Development of Drivers’ Psychophysiological State Detection Measures and Relevant Machine Learning Methods

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Driver impairment is yet another human factor that is likely to cause dangerous driving behavior (aka distracted driving), leading to safety hazards on the roads. Assuring acceptable drivers' cognitive state can improve safe driving performance. Behavioral and self-report measures are used to assess drivers' cognitive states, but psychophysiological measures can add more considerable value in the real-world driving context. Hence, monitoring, detecting, and responding to drivers' psychophysiological state compose a safety countermeasure that can reduce the risk of distracted

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A fatigue driving detection method based on Frequency Modulated Continuous Wave radar

Cited by 11 publications

References 2 publications

Sensing and Machine Learning for Automotive Perception: A Review

Sensing and Machine Learning for Automotive Perception: A Review

Review on Neural Network based Detection System for Intoxicated Driving

Development of Drivers’ Psychophysiological State Detection Measures and Relevant Machine Learning Methods

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