The cut-ins(one kind of lane-changing behaviors)have result in severe safety issues,especially at the entrances and exits of urban expressways.Risk prediction and characteristics analysis of cut-ins are part of the es...The cut-ins(one kind of lane-changing behaviors)have result in severe safety issues,especially at the entrances and exits of urban expressways.Risk prediction and characteristics analysis of cut-ins are part of the essential research for advanced in-vehicle technologies which can reduce crash occurrences.This paper makes some efforts on these purposes.In this paper,twenty-four participants were recruited to conduct the experiments of multi-driver simulation for risky driving data collection.The surrogate measures,Time Exposure Time-to-Collision(TET)and Time Integrated Time-to-collision(TIT)were employed to quantify the risk of cut-ins,then k-means clustering was applied for risk classification of 3 levels.Multiple candidate variables of two kinds were extracted including 10 behavioral variables and 7 driver trait variables.Based on these variables,three prediction models including decision tree(DT),gradient boosting decision tree(GBDT)and long shortterm memory(LSTM)are used for predicting the risks of cut-ins.Results from data validity verification show that the data collected from multi-driver simulation experiments is valid compared with real-world data.From results of risk prediction models,the LSTM,with an overall accuracy of 87%,outperforms the GBDT(80.67%)and DT(76.9%).Despite this,this paper also concludes the merits of the DT over the GBDT and LSTM in variable explanation and the results of DT suggest that controlling the proper lane-changing gap and short duration of cut-ins can help reduce risks of cut-ins.展开更多
To explore the technical solution for independently-developed wireless synchronous control of locomotives based on 5G-R,this study investigates the service demands of such control and analyzes the insufficiency of the...To explore the technical solution for independently-developed wireless synchronous control of locomotives based on 5G-R,this study investigates the service demands of such control and analyzes the insufficiency of the existing communication system of China's heavy-haul railway.Giving full consideration of the high bandwidth,low delay,IP-based links,packet domain transmission,quality of service priority guarantee and other characteristics of the 5G-R network,an overall technical solution is proposed,focusing on the implementation of functions such as master-slave locomotive data transmission,controllable end-of-train data transmission,marshaling requests,and multi-driver calls.The findings contribute to enhancing the advancement of the independently-developed wireless synchronous control system of locomotives,ensuring its reliable operation in complex environments,providing valuable guidance for improving the safety and efficiency of heavy-haul railway transportation,and offering robust technical support for the modernization and intelligence development of heavy-haul railway.展开更多
基金Funding for this study was provided in part by the National Key R&D Program of China(2019YFB1600703)the Shanghai Sailing Program(20YF1451800).
文摘The cut-ins(one kind of lane-changing behaviors)have result in severe safety issues,especially at the entrances and exits of urban expressways.Risk prediction and characteristics analysis of cut-ins are part of the essential research for advanced in-vehicle technologies which can reduce crash occurrences.This paper makes some efforts on these purposes.In this paper,twenty-four participants were recruited to conduct the experiments of multi-driver simulation for risky driving data collection.The surrogate measures,Time Exposure Time-to-Collision(TET)and Time Integrated Time-to-collision(TIT)were employed to quantify the risk of cut-ins,then k-means clustering was applied for risk classification of 3 levels.Multiple candidate variables of two kinds were extracted including 10 behavioral variables and 7 driver trait variables.Based on these variables,three prediction models including decision tree(DT),gradient boosting decision tree(GBDT)and long shortterm memory(LSTM)are used for predicting the risks of cut-ins.Results from data validity verification show that the data collected from multi-driver simulation experiments is valid compared with real-world data.From results of risk prediction models,the LSTM,with an overall accuracy of 87%,outperforms the GBDT(80.67%)and DT(76.9%).Despite this,this paper also concludes the merits of the DT over the GBDT and LSTM in variable explanation and the results of DT suggest that controlling the proper lane-changing gap and short duration of cut-ins can help reduce risks of cut-ins.
文摘To explore the technical solution for independently-developed wireless synchronous control of locomotives based on 5G-R,this study investigates the service demands of such control and analyzes the insufficiency of the existing communication system of China's heavy-haul railway.Giving full consideration of the high bandwidth,low delay,IP-based links,packet domain transmission,quality of service priority guarantee and other characteristics of the 5G-R network,an overall technical solution is proposed,focusing on the implementation of functions such as master-slave locomotive data transmission,controllable end-of-train data transmission,marshaling requests,and multi-driver calls.The findings contribute to enhancing the advancement of the independently-developed wireless synchronous control system of locomotives,ensuring its reliable operation in complex environments,providing valuable guidance for improving the safety and efficiency of heavy-haul railway transportation,and offering robust technical support for the modernization and intelligence development of heavy-haul railway.
文摘识别非驾驶行为是提高驾驶安全性的重要手段之一。目前基于骨架序列和图像的融合识别方法具有计算量大和特征融合困难的问题。针对上述问题,本文提出一种基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别模型(skeleton-image based behavior recognition network,SIBBR-Net)。SIBBR-Net通过基于多尺度图的图卷积网络和基于局部视觉及注意力机制的卷积神经网络,充分提取运动和外观特征,较好地平衡了模型表征能力和计算量间的关系。基于手部运动的特征双向引导学习策略、自适应特征融合模块和静态特征空间上的辅助损失,使运动和外观特征间互相引导更新并实现自适应融合。最终在Drive&Act数据集进行算法测试,SIBBR-Net在动态标签和静态标签条件下的平均正确率分别为61.78%和80.42%,每秒浮点运算次数为25.92G,较最优方法降低了76.96%。
