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
SiC MOSFET具有低导通电阻、低开关损耗、高开关频率以及优异的反向恢复特性。器件过快的开关速度,会导致严重的开关过冲、振荡和串扰。此外其短路承受能力弱,需要保护电路具备更快的响应速度,但较高的开关变化率,又使得保护电路的快速...SiC MOSFET具有低导通电阻、低开关损耗、高开关频率以及优异的反向恢复特性。器件过快的开关速度,会导致严重的开关过冲、振荡和串扰。此外其短路承受能力弱,需要保护电路具备更快的响应速度,但较高的开关变化率,又使得保护电路的快速响应与抗噪声能力难以兼顾。为确保其安全可靠工作,该文提出基于多段式电平调控的驱动与保护方法。驱动方法解决开关过程多个目标的协同优化问题,在获得较快的开关速度和低损耗的同时,有效地抑制过冲和振荡;保护方法提出了增加补偿回路的导通压降检测电路,降低了温度和负载变化对检测精度的影响,同时提出了两段式降低栅压的关断方法,增大故障检测盲区时间以降低干扰噪声影响,并采用软关断技术,抑制关断过电压。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.
