Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.展开更多
To address the challenge of distinguishing subjective aggressive driving(initiated by drivers)from hazardous behaviors caused by external cyberattacks,this study proposes an innovative intent recognition framework nam...To address the challenge of distinguishing subjective aggressive driving(initiated by drivers)from hazardous behaviors caused by external cyberattacks,this study proposes an innovative intent recognition framework named Intent-Decipher.By integrating the information credibility outputted by an intrusion detection system(IDS)into a security-aware inverse reinforcement learning(SA-IRL)model,the framework infers the reward function behind vehicle behaviors and classifies three key driving intents:normal,aggressive,and malicious.Experiments were conducted on a semi-synthetic dataset containing 20000 trajectories.Results show that Intent-Decipher significantly outperforms baseline methods in classification accuracy,achieving a macro-average F1-score of 0.94.Notably,Intent-Decipher excels at differentiating subjective aggressive driving from attack-induced behaviors:its F1-score for identifying malicious attack-induced(MAI)intent reaches 0.90,an absolute improvement of 0.16 compared with the standard inverse reinforcement learning(IRL)model(which lacks security awareness and only achieves an F1-score of 0.74).展开更多
Electrification has become a major trend in the automotive industry.Compared with traditional fuel-powered vehicles,electric vehicles(EVs)exhibit stronger acceleration performance,which may impact traffic safety risks...Electrification has become a major trend in the automotive industry.Compared with traditional fuel-powered vehicles,electric vehicles(EVs)exhibit stronger acceleration performance,which may impact traffic safety risks.The pronounced acceleration capability of EVs is particularly evident at low speeds,with signalized intersections—key components of road networks—facing complex traffic flows and heightened safety risks.Therefore,investigating the impact of EVs’strong acceleration on traffic safety risks at signalized intersections is crucial for ensuring urban traffic safety.This study utilizes unmanned aerial vehicles(UAVs)and roadside cameras to construct a detailed trajectory dataset,including license plate types and vehicle dimensions.Based on time to collision(TTC)as a fundamental metric and incorporating the risk management quadrant principle,a two-dimensional integrated safety surrogate indicator is proposed,accounting for both collision probability and severity.The study also achieves automated extraction of safety surrogate indicators from trajectory data,quantifying the impact of EVs on traffic safety risks at signalized intersections.Case studies in urban and suburban areas provide a comparative analysis of driving behaviors and interactive safety risks between EVs and fuel-powered vehicles.At a 90%confidence level,the frequency of events where EVs have a higher likelihood of collisions but lower severity is 0.87%higher than that of fuel-powered vehicles,indicating that EVs are more likely to experience low-severity collisions.展开更多
Urban intersections contain severe blind zones where buildings and roadside obstacles block lineof-sight sensing,limiting the ability of autonomous vehicles to anticipate hidden hazards.This paper presents an urban-in...Urban intersections contain severe blind zones where buildings and roadside obstacles block lineof-sight sensing,limiting the ability of autonomous vehicles to anticipate hidden hazards.This paper presents an urban-intersection-oriented non-line-of-sight(NLOS)perception framework that exploits specular reflections from building surfaces using 77 GHz frequency-modulated continuous-wave(FMCW)automotive radar.All evaluations are conducted in a MATLAB-based simulation environment that models intersection geometry,building-induced occlusions,and specular reflection-assisted propagation,and generates 77-GHz FMCW radar echoes under controllable interference;real-world validation with measured radar data and richer multipath/material modeling is planned as future work.To improve robustness under noisy intersection interference,we propose a deep-learning-based mitigation module that restores corrupted radar echoes at the chirp level using a compact AlexNet-derived 1D regression backbone,with minimal architectural changes that insert a residual block after conv2 and apply batch normalization to enhance training stability and suppress interference while preserving informative echo characteristics.The restored echoes are then processed by conventional estimation steps to obtain range and azimuth-related angles.Under severe interference(Noise Factor=3.0),unmitigated measurements exhibit large errors(root-mean-square error(RMSE)=5.48 m/18.95°/10.77°for range/angle/azimuth deviation).Conventional AlexNet-based mitigation reduces these errors to 0.75 m/0.83°/0.93°,while the proposed improved AlexNet further reduces them to 0.56 m/0.46°/0.73°.The results demonstrate improved signal stability and measurement accuracy,supporting the potential practicality of low-cost NLOS perception in simulation for safety-critical autonomous driving at occluded urban intersections,subject to future real-world validation.展开更多
This paper aims to enhance highway traffic efficiency by integrating a project focused on signal timing optimization at highway intersections.Supported by intelligent technologies,a reasonable optimization system is c...This paper aims to enhance highway traffic efficiency by integrating a project focused on signal timing optimization at highway intersections.Supported by intelligent technologies,a reasonable optimization system is constructed.Practical application demonstrates that the signal timing optimization at highway intersections under this system yields significant results,substantially improving traffic efficiency.This provides a reference for subsequent signal timing optimization at highway intersections,aligning with the development needs of the intelligent transportation context.展开更多
The control of traffic flows on urban roads intersections through the traffic control signals is very important,not only does the jammed traffic statement valuably relieve and the ratio of the traffic availability inc...The control of traffic flows on urban roads intersections through the traffic control signals is very important,not only does the jammed traffic statement valuably relieve and the ratio of the traffic availability increase,but also the traffic accidents evidently decrease.In this paper,an adaptive algorithm of traffic control signals on the urban roads intersections is designed in,this new algorithm can actively adjust the concrete control times of the traffic control signals based on the perceiving information of the waiting vehicles in real time,then the dynamic balance between the traffic control signals and the traffic flows can be realized.Furthermore,through experiment testing and demonstrating,this adaptive algorithm expresses some fine performances,it also shows good application prospect in the field of smart city.展开更多
Three-way control combiner valves(TCCVs)are critical components used in nuclear power plants to regulate the concentration of boron acid for neutron absorption and reactor safety.However,current TCCV designs often suf...Three-way control combiner valves(TCCVs)are critical components used in nuclear power plants to regulate the concentration of boron acid for neutron absorption and reactor safety.However,current TCCV designs often suffer from suboptimal control performance and high flow resistance,leading to control deviations and reduced operational efficiency.In this paper,a numerical model based on the standard K–ωturbulence model is established and validated against experimental data to analyze the flow characteristics and local flow resistance of a TCCV.A parametric design method for the throttling windows is proposed,establishing relationships between shape parameters and performance indexes,including control performance and flow resistance.The adaptive non-dominated sorting genetic algorithm(ANSGA-II)is used to optimize the shape parameters of the throttling windows.The optimization results show an improvement in the performance indexes of the TCCV,with the adjustable operating range increasing by 31.0%and the maximum local resistance decreasing by 18.3%.We also introduce the concepts of effective and controllable domains to characterize the inlet backflow phenomena and regulation dead zones,which are crucial for ensuring the reliability and effectiveness of control valves.These findings provide insights for enhancing the design and performance of TCCVs in nuclear power plants.展开更多
This article explores the intersections of Buddhism,Daoism,and contemporary French literary practice in the study of the everyday(quotidien).Since the 1980s,French literature has increasingly shifted its focus from th...This article explores the intersections of Buddhism,Daoism,and contemporary French literary practice in the study of the everyday(quotidien).Since the 1980s,French literature has increasingly shifted its focus from the exotic to the mundane,engaging with theoretical frameworks developed by scholars such as Henri Lefebvre and Michel de Certeau.Drawing on Buddhist notions of emptiness and dependent arising,as well as Daoist principles of yin-yang interdependence,the article bridges Eastern and Western philosophies to demonstrate the everyday not as a static or trivial backdrop,but as a dynamic and transformative space.It further examines how representations of daily life in the works of Georges Perec and Jacques Roubaud employ the meticulous documentation of mundane details to uncover hidden patterns,rhythms,and structures of human experience.Through literary fieldwork,Perec and Roubaud challenge conventional perceptions of the everyday,unveiling its depth,complexity,and potential for reinvention.展开更多
为了解交叉口车路协同领域的研究现状及未来研究热点,以Web of Science和CNKI数据库作为数据源,检索了2000年1月至2024年9月的427篇核心期刊文献。通过文献计量分析,从发文趋势、文献来源和关键词共词分析等角度进行了统计和可视化。统...为了解交叉口车路协同领域的研究现状及未来研究热点,以Web of Science和CNKI数据库作为数据源,检索了2000年1月至2024年9月的427篇核心期刊文献。通过文献计量分析,从发文趋势、文献来源和关键词共词分析等角度进行了统计和可视化。统计结果显示,近年来此领域的发文数量呈增长趋势,且样本文献具有较强的代表性。关键词共现分析表明,此领域的研究方向主要包括交叉口管控、路径与速度引导、信号-车辆协同优化、建模与仿真、多车交互安全、单一车辆安全、弱势道路使用者安全、网络通信和路侧感知。对以上研究方向的发展脉络以及具体方法进行了梳理和总结,并基于此展望了交叉口车路协同领域的未来研究热点。展开更多
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
摘要Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
基金The National Key Research and Development Program of China(No.2022YFB4300304).
摘要To address the challenge of distinguishing subjective aggressive driving(initiated by drivers)from hazardous behaviors caused by external cyberattacks,this study proposes an innovative intent recognition framework named Intent-Decipher.By integrating the information credibility outputted by an intrusion detection system(IDS)into a security-aware inverse reinforcement learning(SA-IRL)model,the framework infers the reward function behind vehicle behaviors and classifies three key driving intents:normal,aggressive,and malicious.Experiments were conducted on a semi-synthetic dataset containing 20000 trajectories.Results show that Intent-Decipher significantly outperforms baseline methods in classification accuracy,achieving a macro-average F1-score of 0.94.Notably,Intent-Decipher excels at differentiating subjective aggressive driving from attack-induced behaviors:its F1-score for identifying malicious attack-induced(MAI)intent reaches 0.90,an absolute improvement of 0.16 compared with the standard inverse reinforcement learning(IRL)model(which lacks security awareness and only achieves an F1-score of 0.74).
基金supported by the National Natural Science Foundation of China(Nos.52172348 and 52372338)the Research Project of Shanghai Science and Technology Commission(No.21XD1424100)the Belt and Road Cooperation Program under the 2023 Shanghai Action Plan for Science,Technology and Innovation(No.23210750500).
摘要Electrification has become a major trend in the automotive industry.Compared with traditional fuel-powered vehicles,electric vehicles(EVs)exhibit stronger acceleration performance,which may impact traffic safety risks.The pronounced acceleration capability of EVs is particularly evident at low speeds,with signalized intersections—key components of road networks—facing complex traffic flows and heightened safety risks.Therefore,investigating the impact of EVs’strong acceleration on traffic safety risks at signalized intersections is crucial for ensuring urban traffic safety.This study utilizes unmanned aerial vehicles(UAVs)and roadside cameras to construct a detailed trajectory dataset,including license plate types and vehicle dimensions.Based on time to collision(TTC)as a fundamental metric and incorporating the risk management quadrant principle,a two-dimensional integrated safety surrogate indicator is proposed,accounting for both collision probability and severity.The study also achieves automated extraction of safety surrogate indicators from trajectory data,quantifying the impact of EVs on traffic safety risks at signalized intersections.Case studies in urban and suburban areas provide a comparative analysis of driving behaviors and interactive safety risks between EVs and fuel-powered vehicles.At a 90%confidence level,the frequency of events where EVs have a higher likelihood of collisions but lower severity is 0.87%higher than that of fuel-powered vehicles,indicating that EVs are more likely to experience low-severity collisions.
基金National Science and Technology Council,Taiwan,for financially supporting this research(grant No.NSTC 114-2221-E-018-003)the Ministry of Education’s Teaching Practice Research Program,Taiwan(PSK1142780).
摘要Urban intersections contain severe blind zones where buildings and roadside obstacles block lineof-sight sensing,limiting the ability of autonomous vehicles to anticipate hidden hazards.This paper presents an urban-intersection-oriented non-line-of-sight(NLOS)perception framework that exploits specular reflections from building surfaces using 77 GHz frequency-modulated continuous-wave(FMCW)automotive radar.All evaluations are conducted in a MATLAB-based simulation environment that models intersection geometry,building-induced occlusions,and specular reflection-assisted propagation,and generates 77-GHz FMCW radar echoes under controllable interference;real-world validation with measured radar data and richer multipath/material modeling is planned as future work.To improve robustness under noisy intersection interference,we propose a deep-learning-based mitigation module that restores corrupted radar echoes at the chirp level using a compact AlexNet-derived 1D regression backbone,with minimal architectural changes that insert a residual block after conv2 and apply batch normalization to enhance training stability and suppress interference while preserving informative echo characteristics.The restored echoes are then processed by conventional estimation steps to obtain range and azimuth-related angles.Under severe interference(Noise Factor=3.0),unmitigated measurements exhibit large errors(root-mean-square error(RMSE)=5.48 m/18.95°/10.77°for range/angle/azimuth deviation).Conventional AlexNet-based mitigation reduces these errors to 0.75 m/0.83°/0.93°,while the proposed improved AlexNet further reduces them to 0.56 m/0.46°/0.73°.The results demonstrate improved signal stability and measurement accuracy,supporting the potential practicality of low-cost NLOS perception in simulation for safety-critical autonomous driving at occluded urban intersections,subject to future real-world validation.
摘要This paper aims to enhance highway traffic efficiency by integrating a project focused on signal timing optimization at highway intersections.Supported by intelligent technologies,a reasonable optimization system is constructed.Practical application demonstrates that the signal timing optimization at highway intersections under this system yields significant results,substantially improving traffic efficiency.This provides a reference for subsequent signal timing optimization at highway intersections,aligning with the development needs of the intelligent transportation context.
摘要The control of traffic flows on urban roads intersections through the traffic control signals is very important,not only does the jammed traffic statement valuably relieve and the ratio of the traffic availability increase,but also the traffic accidents evidently decrease.In this paper,an adaptive algorithm of traffic control signals on the urban roads intersections is designed in,this new algorithm can actively adjust the concrete control times of the traffic control signals based on the perceiving information of the waiting vehicles in real time,then the dynamic balance between the traffic control signals and the traffic flows can be realized.Furthermore,through experiment testing and demonstrating,this adaptive algorithm expresses some fine performances,it also shows good application prospect in the field of smart city.
基金supported by the National Natural Science Foundation of China(No.52422506).
摘要Three-way control combiner valves(TCCVs)are critical components used in nuclear power plants to regulate the concentration of boron acid for neutron absorption and reactor safety.However,current TCCV designs often suffer from suboptimal control performance and high flow resistance,leading to control deviations and reduced operational efficiency.In this paper,a numerical model based on the standard K–ωturbulence model is established and validated against experimental data to analyze the flow characteristics and local flow resistance of a TCCV.A parametric design method for the throttling windows is proposed,establishing relationships between shape parameters and performance indexes,including control performance and flow resistance.The adaptive non-dominated sorting genetic algorithm(ANSGA-II)is used to optimize the shape parameters of the throttling windows.The optimization results show an improvement in the performance indexes of the TCCV,with the adjustable operating range increasing by 31.0%and the maximum local resistance decreasing by 18.3%.We also introduce the concepts of effective and controllable domains to characterize the inlet backflow phenomena and regulation dead zones,which are crucial for ensuring the reliability and effectiveness of control valves.These findings provide insights for enhancing the design and performance of TCCVs in nuclear power plants.
基金funded by Sichuan International Studies University within the framework of the research project“Oulipian Experimentalism and Spatial Structure in the Travel Narratives of Jacques Roubaud”(sisu202008).
摘要This article explores the intersections of Buddhism,Daoism,and contemporary French literary practice in the study of the everyday(quotidien).Since the 1980s,French literature has increasingly shifted its focus from the exotic to the mundane,engaging with theoretical frameworks developed by scholars such as Henri Lefebvre and Michel de Certeau.Drawing on Buddhist notions of emptiness and dependent arising,as well as Daoist principles of yin-yang interdependence,the article bridges Eastern and Western philosophies to demonstrate the everyday not as a static or trivial backdrop,but as a dynamic and transformative space.It further examines how representations of daily life in the works of Georges Perec and Jacques Roubaud employ the meticulous documentation of mundane details to uncover hidden patterns,rhythms,and structures of human experience.Through literary fieldwork,Perec and Roubaud challenge conventional perceptions of the everyday,unveiling its depth,complexity,and potential for reinvention.
摘要为了解交叉口车路协同领域的研究现状及未来研究热点,以Web of Science和CNKI数据库作为数据源,检索了2000年1月至2024年9月的427篇核心期刊文献。通过文献计量分析,从发文趋势、文献来源和关键词共词分析等角度进行了统计和可视化。统计结果显示,近年来此领域的发文数量呈增长趋势,且样本文献具有较强的代表性。关键词共现分析表明,此领域的研究方向主要包括交叉口管控、路径与速度引导、信号-车辆协同优化、建模与仿真、多车交互安全、单一车辆安全、弱势道路使用者安全、网络通信和路侧感知。对以上研究方向的发展脉络以及具体方法进行了梳理和总结,并基于此展望了交叉口车路协同领域的未来研究热点。