Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly...Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.展开更多
As urban transportation increasingly impacts daily life,efficiently utilizing traffic resources and developing public transportation have become crucial for addressing issues such as congestion,frequent accidents,and ...As urban transportation increasingly impacts daily life,efficiently utilizing traffic resources and developing public transportation have become crucial for addressing issues such as congestion,frequent accidents,and noise pollution.The rapid advancement of intelligent autonomous driving technologies,particularly environmental perception technologies,offers new directions for solving these problems.This review discusses the application of multisensor information fusion technology in environmental perception for intelligent vehicles,analyzing the components and performance of various sensors and their specific applications in autonomous driving.Through multisensor information fusion,the accuracy of environmental perception is enhanced,optimizing decision support for autonomous driving systems and thereby improving vehicle safety and driving efficiency.This study also discusses the challenges faced by information fusion technology and future development trends,providing references for further research and application in intelligent transportation systems.展开更多
基金the Fundamental Research Funds for the Central Universities(GrantNo.IR2021222)received by J.Sthe Future Science and Technology Innovation Team Project of HIT(216506)received by Q.W.
文摘Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.
基金supported by the National Key R&D Program of China(Grant No.2023YFB4301804)the National Natural Science Foundation of China(Grant Nos.52220105001 and 52221005).
文摘As urban transportation increasingly impacts daily life,efficiently utilizing traffic resources and developing public transportation have become crucial for addressing issues such as congestion,frequent accidents,and noise pollution.The rapid advancement of intelligent autonomous driving technologies,particularly environmental perception technologies,offers new directions for solving these problems.This review discusses the application of multisensor information fusion technology in environmental perception for intelligent vehicles,analyzing the components and performance of various sensors and their specific applications in autonomous driving.Through multisensor information fusion,the accuracy of environmental perception is enhanced,optimizing decision support for autonomous driving systems and thereby improving vehicle safety and driving efficiency.This study also discusses the challenges faced by information fusion technology and future development trends,providing references for further research and application in intelligent transportation systems.
