Real-time detection of driver fatigue status is of great significance for road traffic safety.In this paper,a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the cl...Real-time detection of driver fatigue status is of great significance for road traffic safety.In this paper,a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock.The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard.The landmarks of the driver’s face were labeled and the eye-area was segmented.By calculating the aspect ratios of the eyes,the duration of eye closure,frequency of blinks and PERCLOS of both colored and infrared,fatigue can be detected.Based on the change of light intensity detected by a photosensitive device,the weight matrix of the colored features and the infrared features was adjusted adaptively to reduce the impact of lighting on fatigue detection.Video samples of the driver’s face were recorded in the test vehicle.After training the classification model,the results showed that our method has high accuracy on driver fatigue detection in both daytime and nighttime.展开更多
This research aims to quantify driver ride comfort due to changes in damper characteristics between comfort mode and sport mode,considering the vehicle’s inertial behavior.The comfort of riding in an automobile has b...This research aims to quantify driver ride comfort due to changes in damper characteristics between comfort mode and sport mode,considering the vehicle’s inertial behavior.The comfort of riding in an automobile has been evaluated in recent years on the basis of a subjective sensory evaluation given by the driver.However,reflecting driving sensations in design work to improve ride comfort is abstract in nature and difficult to express theoretically.Therefore,we evaluated the human body’s effects while driving scientifically by quantifying the driver’s behavior while operating the steering wheel and the behavior of the automobile while in motion using physical quantities.To this end,we collected driver and vehicle data using amotion capture system and vehicle CAN and IMU sensors.We also constructed a three-dimensional musculoskeletal mathematical model to simulate driver movements and calculate the power and amount of energy per unit of time used for driving the joints and muscles of the human body.Here,we used comfort mode and sport mode to compare damper characteristics in terms of hardness.In comfort mode,damper characteristics are soft and steering stability is mild,but vibration from the road is not easily transmitted to the driver making for a lighter load on the driver.In sport mode,on the other hand,damper characteristics are hard and steering stability is comparatively better.Still,vibration from the road is easily transmitted to the driver,whichmakes it easy for a load to be placed on the driver.As a result of this comparison,it was found that a load was most likely to be applied to the driver’s neck.This result in relation to the neck joint can therefore be treated as an objective measure for quantifying ride comfort.展开更多
In the past decades,there have been numerous advancements in the field of technology.This has led to many scientific breakthroughs in the field of medical sciences.In this,rapidly transforming world we are having a di...In the past decades,there have been numerous advancements in the field of technology.This has led to many scientific breakthroughs in the field of medical sciences.In this,rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent.So,this study aimed to understand what is fatigue,its repercussions,and techniques to detect it using machine learning(ML)approaches.This paper introduces,discusses methods and recent advancements in the field of fatigue detection.Further,we categorized the methods that can be used to detect fatigue into four diverse groups,that is,mathematical models,rule-based implementation,ML,and deep learning.This study presents,compares,and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue.Finally,the paper discusses the possible areas for improvement.展开更多
基金The work of this paper was supported by the National Natural Science Foundation of China under grant numbers 61572038 received by J.Z.in 2015.URL:https://isisn.nsfc.gov.cn/egrantindex/funcindex/prjsearch-list。
文摘Real-time detection of driver fatigue status is of great significance for road traffic safety.In this paper,a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock.The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard.The landmarks of the driver’s face were labeled and the eye-area was segmented.By calculating the aspect ratios of the eyes,the duration of eye closure,frequency of blinks and PERCLOS of both colored and infrared,fatigue can be detected.Based on the change of light intensity detected by a photosensitive device,the weight matrix of the colored features and the infrared features was adjusted adaptively to reduce the impact of lighting on fatigue detection.Video samples of the driver’s face were recorded in the test vehicle.After training the classification model,the results showed that our method has high accuracy on driver fatigue detection in both daytime and nighttime.
文摘This research aims to quantify driver ride comfort due to changes in damper characteristics between comfort mode and sport mode,considering the vehicle’s inertial behavior.The comfort of riding in an automobile has been evaluated in recent years on the basis of a subjective sensory evaluation given by the driver.However,reflecting driving sensations in design work to improve ride comfort is abstract in nature and difficult to express theoretically.Therefore,we evaluated the human body’s effects while driving scientifically by quantifying the driver’s behavior while operating the steering wheel and the behavior of the automobile while in motion using physical quantities.To this end,we collected driver and vehicle data using amotion capture system and vehicle CAN and IMU sensors.We also constructed a three-dimensional musculoskeletal mathematical model to simulate driver movements and calculate the power and amount of energy per unit of time used for driving the joints and muscles of the human body.Here,we used comfort mode and sport mode to compare damper characteristics in terms of hardness.In comfort mode,damper characteristics are soft and steering stability is mild,but vibration from the road is not easily transmitted to the driver making for a lighter load on the driver.In sport mode,on the other hand,damper characteristics are hard and steering stability is comparatively better.Still,vibration from the road is easily transmitted to the driver,whichmakes it easy for a load to be placed on the driver.As a result of this comparison,it was found that a load was most likely to be applied to the driver’s neck.This result in relation to the neck joint can therefore be treated as an objective measure for quantifying ride comfort.
文摘In the past decades,there have been numerous advancements in the field of technology.This has led to many scientific breakthroughs in the field of medical sciences.In this,rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent.So,this study aimed to understand what is fatigue,its repercussions,and techniques to detect it using machine learning(ML)approaches.This paper introduces,discusses methods and recent advancements in the field of fatigue detection.Further,we categorized the methods that can be used to detect fatigue into four diverse groups,that is,mathematical models,rule-based implementation,ML,and deep learning.This study presents,compares,and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue.Finally,the paper discusses the possible areas for improvement.
