A driver assistance framework based on driver drowsiness detection

Tran, Duy; Tadesse, Eyosiyas; Sheng, Weihua; Sun, Yaqi; Liu, Meiqin; Zhang, Senlin

doi:10.1109/cyber.2016.7574817

Cited by 17 publications

(8 citation statements)

References 14 publications

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“…Otherwise, the driver's ability tends to diminish; (3) Accordingly, if the driver can correct the vehicle to a safe condition even the driving risk increases, the control authority of vehicle still should be given to driver. Since the measurement of driver's state requires additional wearable equipments or is sensitive to the environment [23]~ [26], only the signals obtained by the onboard sensors are used to determine the switching time. To avoid unnecessary interventions when the driver has the ability to correct the vehicle state, a new index called "correction ability" is designed and integrated with the index of driving risk to determine when to assist the driver.…”

Section: Driving Capability-based Transition Strategymentioning

confidence: 99%

“…Another method is to switch among the automatic driving system and the human according to the state of driver [21] or driving risk [22]. Tran et al focused on the detection of fatigue, based on which a switching logic was designed to transmit the control authority from the driver to the machine pilot [23]. In that research, the fatigue is detected by combination of both steering operation and facial features of driver.…”

Section: Introductionmentioning

confidence: 99%

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Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

2020

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In open traffic environments, humans still have to remain in the control loop of vehicle due to the insufficient of the existing technologies and their high costs. For the realization of cooperation between the human and the automatic driving system, the determination of the time when automatic driving is necessary is very important. To avoid unnecessary intervention when the driver has the control authority of vehicle, a new driving capability-based transition strategy was proposed, which comprehensively considers the driver's correction ability and the driving risk. The transition time from the human driver to the automatic driving system is determined by an unreliable domain (UD), whose boundary is modeled according to the driving data recorded by a driving simulator and statistically described by a log-normal distribution. Furthermore, an adaptive algorithm is designed to update the parameters of UD boundary online to make this strategy suitable for different drivers. This UD-based transition strategy is validated by several tests on the driving simulator. The bench test results show that the individual driving characteristic can be identified by the adaptive algorithm in time, the transition time determined by UD is more accurate, and sufficient time is reserved for the correction carried out by the automatic driving system. INDEX TERMS Automatic driving, human-machine cooperation, driving capability, correction ability, driving risk.

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Section: Driving Capability-based Transition Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

2020

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“…This obtained image consists of some unique features which can be best expressed and described using using HSV format. [1] So the RGB image format is converted using HSV format. When the pixel color range is diversed, thresholding in HSV is very useful for isolating image features that cannot be achieved by RGB thresholding.…”

Section: B Analysismentioning

confidence: 99%

Driver Alertness Detection Using Opencv in Python

Kanade¹,

Patil²,

Bang³

et al. 2019

IJEAST

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One of the major technical challenges in the automotive industry is the development of safety features to prevent drunk and drowsy driving. Driving while drunk or drowsy, especially in modern age, is a major reason behind road accidents. Driving when drowsy can result in a higher risk of crash than in alert condition. Therefore, by using assistive systems to monitor driver's level of alertness can be of significant help in prevention of accidents. This paper aims towards the detection of driver's drowsiness using the visual features approach along with drunk detection using alcohol sensor. Driver drowsiness is based on real-time detection of the driver's head, face and mouth. The system will also have an alcohol detection sensor which will determine whether the driver is drunk or not, thus covering the major reasons behind road accidents.

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“…With the aforementioned measuring technologies of the driver state, several cooperative driving systems have been proposed. Tran et al designed a switching logic from manual driving to automatic mode by using the fatigue information of drivers [14]. In this study, both face features and steering operations are used.…”

Section: Introductionmentioning

confidence: 99%

Detection of Driving Capability Degradation for Human-Machine Cooperative Driving

Gao

2020

Sensors

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Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.

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A driver assistance framework based on driver drowsiness detection

Cited by 17 publications

References 14 publications

Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

Driver Alertness Detection Using Opencv in Python

Detection of Driving Capability Degradation for Human-Machine Cooperative Driving

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