为构建科学合理的城市道路自动驾驶测试场景,基于美国加利福尼亚州机动车管理局(California Department of Motor Vehicles, DMV)2021—2023年公开的280起自动驾驶汽车(Autonomous Vehicle, AV)碰撞事故报告,挖掘典型危险场景并完成测...为构建科学合理的城市道路自动驾驶测试场景,基于美国加利福尼亚州机动车管理局(California Department of Motor Vehicles, DMV)2021—2023年公开的280起自动驾驶汽车(Autonomous Vehicle, AV)碰撞事故报告,挖掘典型危险场景并完成测试场景转化。首先,通过多元Logistic回归分析提取人员受伤情况的显著影响因素。其次,引入独热编码(One-Hot Encoding)对分类变量进行二进制向量转换,消除传统标签编码的数值顺序偏差。然后,采用二阶聚类算法挖掘典型危险场景组,并进一步通过交叉表分析场景组与事故结果变量、道路环境变量间的关联性。最后,将危险场景转化设计为自动驾驶测试场景。结果显示,独热编码处理后的变量,聚类质量较传统方法提升50%;聚类分析共识别出12类典型危险场景,且交叉表分析表明场景组与事故结果及道路环境变量显著相关;进一步结合事故机理与测试需求,将这12类危险场景归纳为6类代表性测试场景,其中“AV停止或减速状态下被后方直行车辆追尾”的场景最为典型,在全部场景中占比46.1%。研究表明,独热编码方法显著提升了聚类分析的准确性,基于真实事故数据的场景聚类方法能识别AV在城市道路的事故模式,并为自动驾驶测试场景库的优先级划分与标准化设计提供数据驱动支撑。展开更多
In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiologi...In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.展开更多
文摘为构建科学合理的城市道路自动驾驶测试场景,基于美国加利福尼亚州机动车管理局(California Department of Motor Vehicles, DMV)2021—2023年公开的280起自动驾驶汽车(Autonomous Vehicle, AV)碰撞事故报告,挖掘典型危险场景并完成测试场景转化。首先,通过多元Logistic回归分析提取人员受伤情况的显著影响因素。其次,引入独热编码(One-Hot Encoding)对分类变量进行二进制向量转换,消除传统标签编码的数值顺序偏差。然后,采用二阶聚类算法挖掘典型危险场景组,并进一步通过交叉表分析场景组与事故结果变量、道路环境变量间的关联性。最后,将危险场景转化设计为自动驾驶测试场景。结果显示,独热编码处理后的变量,聚类质量较传统方法提升50%;聚类分析共识别出12类典型危险场景,且交叉表分析表明场景组与事故结果及道路环境变量显著相关;进一步结合事故机理与测试需求,将这12类危险场景归纳为6类代表性测试场景,其中“AV停止或减速状态下被后方直行车辆追尾”的场景最为典型,在全部场景中占比46.1%。研究表明,独热编码方法显著提升了聚类分析的准确性,基于真实事故数据的场景聚类方法能识别AV在城市道路的事故模式,并为自动驾驶测试场景库的优先级划分与标准化设计提供数据驱动支撑。
基金supported by the Science and Technology Bureau of Xi’an project(24KGDW0049)the Key Research and Development Programof Shaanxi(2023-YBGY-264)the Key Research and Development Program of Guangxi(GK-AB20159032).
文摘In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.
