1 Authorities in Montgomery Township,Pennsylvania,have introduced wavy lane patterns on some streets in an effort to slow down traffic in the area.2 Driving along Grays Lane in Montgomery Township for the first time m...1 Authorities in Montgomery Township,Pennsylvania,have introduced wavy lane patterns on some streets in an effort to slow down traffic in the area.2 Driving along Grays Lane in Montgomery Township for the first time must be challenging.Thats because the regular lane patterns have been replaced by wavy and zig⁃zag lines that look like they were painted by a drunk.But they are wavy by design.According to Montgomery Township officials,the unusual patterns were thought as the best solution to discouraging speeding on some of the streets.Police sources told local media outlets that the“traffic⁃calming measures”were installed in response to numerous complaints about certain streets being used as“speedways”.展开更多
In the field of autonomous driving,the task of reliably and accurately detecting lane markings poses a significant and complex challenge.This study presents a lane recognition model that employs an encoder-decoder arc...In the field of autonomous driving,the task of reliably and accurately detecting lane markings poses a significant and complex challenge.This study presents a lane recognition model that employs an encoder-decoder architecture.In the encoder section,we develop a feature extraction framework that operates concurrently with attention mechanisms and convolutional layers.We propose a spatial axis attention framework that integrates spatial information transfer regulated by gating units.This architecture places a strong emphasis on long-range dependencies and the spatial distribution of images.Furthermore,we incorporate multi-scale convolutional layers to extract intricate features from the images.The two sets of feature maps are concatenated and subsequently transformed into an input sequence for the decoder,with the lane marking coordinates considered as a target sequence for coordinate generation.This decoder can directly segment multiple lane markings,eliminating the need for additional post-processing algorithms,thereby significantly streamlining the lane recognition process.The proposed method demonstrates a high degree of accuracy in recognizing lane markings and exhibits robust capabilities in differentiating between occlusions and objects resembling lanes.It shows exceptional performance on the TuSimple and CULane datasets.展开更多
This paper addresses the lane-keeping control problem for autonomous ground vehicles subject to input saturation and uncertain system parameters.An enhanced adaptive terminal sliding mode based prescribed performance ...This paper addresses the lane-keeping control problem for autonomous ground vehicles subject to input saturation and uncertain system parameters.An enhanced adaptive terminal sliding mode based prescribed performance control scheme is proposed,which enables the lateral position error of the vehicle to be kept within the prescribed performance boundaries all the time.This is achieved by firstly introducing an improved performance function into the controller design such that the stringent initial condition requirements can be relaxed,which further allows the global prescribed performance control result,and then,developing a multivariable adaptive terminal sliding mode based controller such that both input saturation and parameter uncertainties are handled effectively,which further ensures the robust lane-keeping control.Finally,the proposed control strategy is validated through numerical simulations,demonstrating its effectiveness.展开更多
The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection.As deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line dete...The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection.As deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field.However,owing to the simple appearance of lane lines and the lack of distinctive features,it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines.The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines.To address the aforementioned challenges,we propose a novel deep learning approach for lane line detection.This method leverages the Swin Transformer in conjunction with LaneNet(called ST-LaneNet).The experience results showed that the true positive detection rate can reach 97.53%for easy lanes and 96.83%for difficult lanes(such as scenes with severe occlusion and extreme lighting conditions),which can better accomplish the objective of detecting lane lines.In 1000 detection samples,the average detection accuracy can reach 97.83%,the average inference time per image can reach 17.8 ms,and the average number of frames per second can reach 64.8 Hz.The programming scripts and associated models for this project can be accessed openly at the following GitHub repository:https://github.com/Duane 711/Lane-line-detec tion-ST-LaneNet.展开更多
Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase...Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.展开更多
Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majori...Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world.In this study,a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed,and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed.Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation.A high tendency of following results in good lane formation.Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease.The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70%of his own.The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model.The presence of a small obstacle does not obstruct the walking of pedestrians;in contrast,it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.展开更多
When controling the signal of highway-rail level crossings in the port area,the multi-objective signal optimization model is not applicable due to the cross effect of roads and railways and the priority of incoming ve...When controling the signal of highway-rail level crossings in the port area,the multi-objective signal optimization model is not applicable due to the cross effect of roads and railways and the priority of incoming vehicles.Therefore,in order to ensure that the inbound truck fleet enters the port directly without being affected by the train when passing through the highway-rail level crossing in the port area,the queuing of vehicles in front of the port needs to be reduced,and the priority should be given to the inbound trucks.Based on the idea of priority on key lanes,this study relies on speed guidance information to guide the fleet to shift reasonably,postpone or early arrive at the railway gate.At the same time,the optimization goal is to minimize the delays at intersections,the number of stops,and the vehicle exhaust emissions.The measured data of road-rail level crossings in Dayaowan Port area of Dalian were selected,and it was re-developed under the VISSIM environment by serial interface to realize signal optimization control under vehicle speed guidance.The original timing plan,multi-objective timing optimization plan and key lanes priority are given to the optimization scheme for simulation experiments.The results show that the multi-objective optimization scheme and the optimization scheme under the priority of key lanes can generally improve the traffic capacity of road-rail level crossings.Compared with the original plan,the optimization plan under the priority of key lanes reduces the delay by 33.3%,the number of stops is reduced by 25%,and the vehicle exhaust emissions are reduced by 31.3%.It proves the effectiveness of the optimization scheme for highway-rail level crossings in the port area under the priority of key lanes,and it is more suitable for highway-rail level crossings.展开更多
Connected automated vehicles(CAVs)rely heavily on intelligent algorithms and remote sensors.If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication,...Connected automated vehicles(CAVs)rely heavily on intelligent algorithms and remote sensors.If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication,it can cause significant damage to CAVs or passengers.The primary objective of this study is to model cyberattacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment.Based on the analysis on environmental perception system and possible cyber-attacks on sensors,a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed.The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lanechanging process is then quantitatively analyzed.The evaluation indexes include spatio-temporal evolution of average speed,spatial distribution of selected lane-changing gaps,lane-changing rate distribution,lane-changing preparation search time,efficiency and safety.Finally,the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack.Also,when the traffic system is under cyber-attack,more unsafe back gaps are chosen for lane-changing,especially in the center lane.Therefore,more lane-changing maneuvers are concentrated on approaching the off-ramp,causing severe congestions and potential rear-end collisions.In addition,as the number of cyber-attacked vehicles and the severity of cyber-attacks increase,the road capacity and safety level will rapidly decrease.The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.展开更多
In this article,lane change models for mixed traffic flow under cooperative adaptive cruise control(CACC)platoon formation are established.The analysis begins by examining the impact of lane changes on traffic flow st...In this article,lane change models for mixed traffic flow under cooperative adaptive cruise control(CACC)platoon formation are established.The analysis begins by examining the impact of lane changes on traffic flow stability.The influences of various factors such as lane change locations,timing,and the current traffic state on stability are discussed.In this analysis,it is assumed that the lane change location and the entry position in the adjacent lane have already been selected,without considering the specific intention behind the lane change.The speeds of the involved vehicles are adjusted based on an existing lane change model,and various conditions are analyzed for traffic flow disturbances,including duration,shock amplitude,and driving delays.Numerical calculations are provided to illustrate these effects.Additionally,traffic flow stability is factored into the lane change decision-making process.By incorporating disturbances to the fleet into the lane change income model,both a lane change intention model and a lane change execution model are constructed.These models are then compared with a model that does not account for stability,leading to the corresponding conclusions.展开更多
Mandatory lane change(MLC)is likely to cause traffic oscillations,which have a negative impact on traffic efficiency and safety.There is a rapid increase in research on mandatory lane change decision(MLCD)prediction,w...Mandatory lane change(MLC)is likely to cause traffic oscillations,which have a negative impact on traffic efficiency and safety.There is a rapid increase in research on mandatory lane change decision(MLCD)prediction,which can be categorized into physics-based models and machine-learning models.Both types of models have their advantages and disadvantages.To obtain a more advanced MLCD prediction method,this study proposes a hybrid architecture,which combines the Evolutionary Game Theory(EGT)based model(considering data efficient and interpretable)and the Machine Learning(ML)based model(considering high prediction accuracy)to model the mandatory lane change decision of multi-style drivers(i.e.EGTML framework).Therefore,EGT is utilized to introduce physical information,which can describe the progressive cooperative interactions between drivers and predict the decision-making of multi-style drivers.The generalization of the EGTML method is further validated using four machine learning models:ANN,RF,LightGBM,and XGBoost.The superiority of EGTML is demonstrated using real-world data(i.e.,Next Generation SIMulation,NGSIM).The results of sensitivity analysis show that the EGTML model outperforms the general ML model,especially when the data is sparse.展开更多
Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we...Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model(GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform(PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.展开更多
A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approa...A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.展开更多
In this paper, the speed gradient （SG） model is extended to describe the traffic flow on two-lane freeways. Terms related to lane change are added into the continuity equations and velocity dynamic equations. The em...In this paper, the speed gradient （SG） model is extended to describe the traffic flow on two-lane freeways. Terms related to lane change are added into the continuity equations and velocity dynamic equations. The empirically observed two-lane phenomena, such as lane usage inversion and lane change rate versus density, are reproduced by extended SG model. The local cluster effect is also investigated by numerical simulations.展开更多
A multilane extension of the single-lane anisotropic continuum model （GK model） developed by Gupta and Katiyar for traffic flow is discussed with the consideration of the coupling effect between the vehicles of diff...A multilane extension of the single-lane anisotropic continuum model （GK model） developed by Gupta and Katiyar for traffic flow is discussed with the consideration of the coupling effect between the vehicles of different lanes in the instantaneous traffic situation and the lane-changing effect. The conditions for securing the linear stability of the new model are presented. The shock and the rarefaction waves, the local cluster effect and the phase transition are investigated through simulation experiments with the new model and are found to be consistent with the diverse nonlinear dynamical phenomena observed in a real traffic flow. The analysis also focuses on empirically observed two- lane phenomena, such as lane usage inversion and the density dependence of the number of lane changes. It is shown that single-lane dynamics can be extended to multilane cases without changing the basic properties of the single-lane model. The results show that the new multilane model is capable of explaining some particular traffic phenomena and is in accordance with real traffic flow.展开更多
To explore the potential capacity of dual-fight-turn lanes at signalized intersections under mixed traffic conditions, we defined two conflict zones between right turn vehicles and through bicycle corresponding to dif...To explore the potential capacity of dual-fight-turn lanes at signalized intersections under mixed traffic conditions, we defined two conflict zones between right turn vehicles and through bicycle corresponding to different right turn flows from dual-right-turn lanes. Relationships between the arrival rate of bicycle group at each conflict zone and the saturation flow rate of right turn movement were investigated. A model based on gap acceptance theory was adopted to estimate the capacity of dual-right-turn lanes under mixed traffic conditions. An analysis was carried out using the collected data from three four-leg signalized intersections in Beijing, China, where the dual-right-turn lanes were used. In addition, we also discussed the patterns of bicycle lane in the urban area of Beijing, and classified it based on its characteristics in use. It is concluded that the two lanes of dual-fight-turn lanes produce different capacities under mixed traffic conditions, and the analysis on scenarios of dual-right-turn movement traversing bicycle traffic plays a key role in explaining the different capacity performance of the two right turn lanes. Error analysis of the model indicated that the model was rational.展开更多
文摘1 Authorities in Montgomery Township,Pennsylvania,have introduced wavy lane patterns on some streets in an effort to slow down traffic in the area.2 Driving along Grays Lane in Montgomery Township for the first time must be challenging.Thats because the regular lane patterns have been replaced by wavy and zig⁃zag lines that look like they were painted by a drunk.But they are wavy by design.According to Montgomery Township officials,the unusual patterns were thought as the best solution to discouraging speeding on some of the streets.Police sources told local media outlets that the“traffic⁃calming measures”were installed in response to numerous complaints about certain streets being used as“speedways”.
基金support was provided by Ministry of Education Higher Education Industry University Research Innovation Fund Special Project.If there are other authors,they declare that they have no known competing financial interests or personal relationships that could have appeared to influence thework reported in this paper.
文摘In the field of autonomous driving,the task of reliably and accurately detecting lane markings poses a significant and complex challenge.This study presents a lane recognition model that employs an encoder-decoder architecture.In the encoder section,we develop a feature extraction framework that operates concurrently with attention mechanisms and convolutional layers.We propose a spatial axis attention framework that integrates spatial information transfer regulated by gating units.This architecture places a strong emphasis on long-range dependencies and the spatial distribution of images.Furthermore,we incorporate multi-scale convolutional layers to extract intricate features from the images.The two sets of feature maps are concatenated and subsequently transformed into an input sequence for the decoder,with the lane marking coordinates considered as a target sequence for coordinate generation.This decoder can directly segment multiple lane markings,eliminating the need for additional post-processing algorithms,thereby significantly streamlining the lane recognition process.The proposed method demonstrates a high degree of accuracy in recognizing lane markings and exhibits robust capabilities in differentiating between occlusions and objects resembling lanes.It shows exceptional performance on the TuSimple and CULane datasets.
基金supported in part by the National Key Research and Development Program of China under Grant 2023YFA1011803in part by Natural Science Foundation of Chongqing,China under Grant CSTB2023NSCQ-MSX0588+2 种基金in part by the Fundamental Research Funds for the Central Universities,China under Grant 2023CDJKYJH047in part by the National Natural Science Foundation of China under Grant 62273064,Grant 61991400,Grant 61991403,Grant 61933012,Grant 62250710167,Grant 62203078in part by Innovation Support Program for International Students Returning to China under Grant cx2022016.
文摘This paper addresses the lane-keeping control problem for autonomous ground vehicles subject to input saturation and uncertain system parameters.An enhanced adaptive terminal sliding mode based prescribed performance control scheme is proposed,which enables the lateral position error of the vehicle to be kept within the prescribed performance boundaries all the time.This is achieved by firstly introducing an improved performance function into the controller design such that the stringent initial condition requirements can be relaxed,which further allows the global prescribed performance control result,and then,developing a multivariable adaptive terminal sliding mode based controller such that both input saturation and parameter uncertainties are handled effectively,which further ensures the robust lane-keeping control.Finally,the proposed control strategy is validated through numerical simulations,demonstrating its effectiveness.
基金Supported by National Natural Science Foundation of China(Grant Nos.51605003,51575001)Natural Science Foundation of Anhui Higher Education Institutions of China(Grant No.KJ2020A0358)Young and Middle-Aged Top Talents Training Program of Anhui Polytechnic University of China.
文摘The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection.As deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field.However,owing to the simple appearance of lane lines and the lack of distinctive features,it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines.The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines.To address the aforementioned challenges,we propose a novel deep learning approach for lane line detection.This method leverages the Swin Transformer in conjunction with LaneNet(called ST-LaneNet).The experience results showed that the true positive detection rate can reach 97.53%for easy lanes and 96.83%for difficult lanes(such as scenes with severe occlusion and extreme lighting conditions),which can better accomplish the objective of detecting lane lines.In 1000 detection samples,the average detection accuracy can reach 97.83%,the average inference time per image can reach 17.8 ms,and the average number of frames per second can reach 64.8 Hz.The programming scripts and associated models for this project can be accessed openly at the following GitHub repository:https://github.com/Duane 711/Lane-line-detec tion-ST-LaneNet.
基金Supported by National Natural Science Foundation of China(Grant Nos.51605199,U20A20333,52225212)Six Talent Peak Funding Projects in Jiangsu Province of China(Grant No.2019-GDZB-084)Key Science and Technology Support Program in Taizhou City of China(Grant No.TG202307).
文摘Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.
基金Project supported by the National Natural Science Foundation of China(Grant No.71603146).
文摘Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world.In this study,a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed,and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed.Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation.A high tendency of following results in good lane formation.Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease.The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70%of his own.The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model.The presence of a small obstacle does not obstruct the walking of pedestrians;in contrast,it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.
基金the Research on Key Technologies and Equipment for Transportation Infrastructure Construction Safety(No.2017YFC0805309)。
文摘When controling the signal of highway-rail level crossings in the port area,the multi-objective signal optimization model is not applicable due to the cross effect of roads and railways and the priority of incoming vehicles.Therefore,in order to ensure that the inbound truck fleet enters the port directly without being affected by the train when passing through the highway-rail level crossing in the port area,the queuing of vehicles in front of the port needs to be reduced,and the priority should be given to the inbound trucks.Based on the idea of priority on key lanes,this study relies on speed guidance information to guide the fleet to shift reasonably,postpone or early arrive at the railway gate.At the same time,the optimization goal is to minimize the delays at intersections,the number of stops,and the vehicle exhaust emissions.The measured data of road-rail level crossings in Dayaowan Port area of Dalian were selected,and it was re-developed under the VISSIM environment by serial interface to realize signal optimization control under vehicle speed guidance.The original timing plan,multi-objective timing optimization plan and key lanes priority are given to the optimization scheme for simulation experiments.The results show that the multi-objective optimization scheme and the optimization scheme under the priority of key lanes can generally improve the traffic capacity of road-rail level crossings.Compared with the original plan,the optimization plan under the priority of key lanes reduces the delay by 33.3%,the number of stops is reduced by 25%,and the vehicle exhaust emissions are reduced by 31.3%.It proves the effectiveness of the optimization scheme for highway-rail level crossings in the port area under the priority of key lanes,and it is more suitable for highway-rail level crossings.
基金jointly supported by the National Key Research and Development Program of China(No.2022ZD0115600)National Natural Science Foundation of China(No.52072067)+3 种基金Natural Science Foundation of Jiangsu Province(No.BK20210249)China Postdoctoral Science Foundation(No.2020M681466)Jiangsu Planned Projects for Postdoctoral Research Funds(No.SBK2021041144)Jiangsu Planned Projects for Postdoctoral Research Funds(No.2021K094A)。
文摘Connected automated vehicles(CAVs)rely heavily on intelligent algorithms and remote sensors.If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication,it can cause significant damage to CAVs or passengers.The primary objective of this study is to model cyberattacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment.Based on the analysis on environmental perception system and possible cyber-attacks on sensors,a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed.The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lanechanging process is then quantitatively analyzed.The evaluation indexes include spatio-temporal evolution of average speed,spatial distribution of selected lane-changing gaps,lane-changing rate distribution,lane-changing preparation search time,efficiency and safety.Finally,the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack.Also,when the traffic system is under cyber-attack,more unsafe back gaps are chosen for lane-changing,especially in the center lane.Therefore,more lane-changing maneuvers are concentrated on approaching the off-ramp,causing severe congestions and potential rear-end collisions.In addition,as the number of cyber-attacked vehicles and the severity of cyber-attacks increase,the road capacity and safety level will rapidly decrease.The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.
文摘In this article,lane change models for mixed traffic flow under cooperative adaptive cruise control(CACC)platoon formation are established.The analysis begins by examining the impact of lane changes on traffic flow stability.The influences of various factors such as lane change locations,timing,and the current traffic state on stability are discussed.In this analysis,it is assumed that the lane change location and the entry position in the adjacent lane have already been selected,without considering the specific intention behind the lane change.The speeds of the involved vehicles are adjusted based on an existing lane change model,and various conditions are analyzed for traffic flow disturbances,including duration,shock amplitude,and driving delays.Numerical calculations are provided to illustrate these effects.Additionally,traffic flow stability is factored into the lane change decision-making process.By incorporating disturbances to the fleet into the lane change income model,both a lane change intention model and a lane change execution model are constructed.These models are then compared with a model that does not account for stability,leading to the corresponding conclusions.
基金supported by the National Key R&D Program of China(2023YFE0106800)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX24_0100).
文摘Mandatory lane change(MLC)is likely to cause traffic oscillations,which have a negative impact on traffic efficiency and safety.There is a rapid increase in research on mandatory lane change decision(MLCD)prediction,which can be categorized into physics-based models and machine-learning models.Both types of models have their advantages and disadvantages.To obtain a more advanced MLCD prediction method,this study proposes a hybrid architecture,which combines the Evolutionary Game Theory(EGT)based model(considering data efficient and interpretable)and the Machine Learning(ML)based model(considering high prediction accuracy)to model the mandatory lane change decision of multi-style drivers(i.e.EGTML framework).Therefore,EGT is utilized to introduce physical information,which can describe the progressive cooperative interactions between drivers and predict the decision-making of multi-style drivers.The generalization of the EGTML method is further validated using four machine learning models:ANN,RF,LightGBM,and XGBoost.The superiority of EGTML is demonstrated using real-world data(i.e.,Next Generation SIMulation,NGSIM).The results of sensitivity analysis show that the EGTML model outperforms the general ML model,especially when the data is sparse.
基金supported by the National Nature Science Foundation of China under Grant No.61502226the Jiangsu Provincial Transportation Science and Technology Project No.2017X04the Fundamental Research Funds for the Central Universities
文摘Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model(GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform(PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.
基金Project(90820302) supported by the National Natural Science Foundation of China
文摘A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.
文摘In this paper, the speed gradient （SG） model is extended to describe the traffic flow on two-lane freeways. Terms related to lane change are added into the continuity equations and velocity dynamic equations. The empirically observed two-lane phenomena, such as lane usage inversion and lane change rate versus density, are reproduced by extended SG model. The local cluster effect is also investigated by numerical simulations.
文摘A multilane extension of the single-lane anisotropic continuum model （GK model） developed by Gupta and Katiyar for traffic flow is discussed with the consideration of the coupling effect between the vehicles of different lanes in the instantaneous traffic situation and the lane-changing effect. The conditions for securing the linear stability of the new model are presented. The shock and the rarefaction waves, the local cluster effect and the phase transition are investigated through simulation experiments with the new model and are found to be consistent with the diverse nonlinear dynamical phenomena observed in a real traffic flow. The analysis also focuses on empirically observed two- lane phenomena, such as lane usage inversion and the density dependence of the number of lane changes. It is shown that single-lane dynamics can be extended to multilane cases without changing the basic properties of the single-lane model. The results show that the new multilane model is capable of explaining some particular traffic phenomena and is in accordance with real traffic flow.
文摘To explore the potential capacity of dual-fight-turn lanes at signalized intersections under mixed traffic conditions, we defined two conflict zones between right turn vehicles and through bicycle corresponding to different right turn flows from dual-right-turn lanes. Relationships between the arrival rate of bicycle group at each conflict zone and the saturation flow rate of right turn movement were investigated. A model based on gap acceptance theory was adopted to estimate the capacity of dual-right-turn lanes under mixed traffic conditions. An analysis was carried out using the collected data from three four-leg signalized intersections in Beijing, China, where the dual-right-turn lanes were used. In addition, we also discussed the patterns of bicycle lane in the urban area of Beijing, and classified it based on its characteristics in use. It is concluded that the two lanes of dual-fight-turn lanes produce different capacities under mixed traffic conditions, and the analysis on scenarios of dual-right-turn movement traversing bicycle traffic plays a key role in explaining the different capacity performance of the two right turn lanes. Error analysis of the model indicated that the model was rational.
