To track the vehicles under occlusion, a vehicle tracking algorithm based on blocks is proposed. The target vehicle is divided into several blocks of uniform size, in which the edge block can overlap its neighboring b...To track the vehicles under occlusion, a vehicle tracking algorithm based on blocks is proposed. The target vehicle is divided into several blocks of uniform size, in which the edge block can overlap its neighboring blocks. All the blocks＇ motion vectors are estimated, and the noise motion vectors are detected and adjusted to decrease the error of motion vector estimation. Then, by moving the blocks based on the adjusted motion vectors, the vehicle is tracked. Aiming at the occlusion between vehicles, a Markov random field is established to describe the relationship between the blocks in the blocked regions. The neighborhood of blocks is defined using the Euclidean distance. An energy function is defined based on the blocks＇ histograms and optimized by the simulated annealing algorithm to segment the occlusion region. Experimental results demonstrate that the proposed algorithm can track vehicles under occlusion accurately.展开更多
A real-time vehicle tracking method is proposed for trattlC monitoring system at roau mte^cc- tions, and the vehicle tracking module consists of an initialization stage and a tracking stage. Li- cense plate location b...A real-time vehicle tracking method is proposed for trattlC monitoring system at roau mte^cc- tions, and the vehicle tracking module consists of an initialization stage and a tracking stage. Li- cense plate location based on edge density and color analysis is used to detect the license plate re- gion for tracking initialization. In the tracking stage, covariance matching is employed to track the license plate. Genetic algorithm is used to reduce the computational cost. Real-time image tracking of multi-lane vehicles is achieved. In the experiment, test videos are recorded in advance by record- ers of actual E-police systems erage false detection rate and at several different city intersections. In the tracking module, the av- missed plates rate are 1.19%, and 1.72%, respectively.展开更多
Multi-object tracking is a vital problem as many applications require better tracking approaches.Although learning-based detectors are becoming extremely powerful,there are few tracking methods designed to work with t...Multi-object tracking is a vital problem as many applications require better tracking approaches.Although learning-based detectors are becoming extremely powerful,there are few tracking methods designed to work with them in real time.We explored an efficient flexible online vehicle tracking-by-detection framework suitable for real-virtual mapping systems,which combines a non-recursive temporal window search with delayed output and produces stable trajectories despite noisy detection responses.Its computation speed meets the real-time requirements,whereas its performance is comparable with that of state-of-the-art online trackers on the DETRAC dataset.The trajectories from our approach also contain the target class and color information important for virtual vehicle motion reconstruction.展开更多
The latest advances in Deep Learning based methods and computational capabilities provide new opportunities for vehicle tracking. In this study, YOLOv2 (You Only Look Once—version 2) is used as an open source Convolu...The latest advances in Deep Learning based methods and computational capabilities provide new opportunities for vehicle tracking. In this study, YOLOv2 (You Only Look Once—version 2) is used as an open source Convolutional Neural Network (CNN), to process high-resolution satellite images, in order to generate the spatio-temporal GIS (Geographic Information System) tracks of moving vehicles. At first step, YOLOv2 is trained with a set of images of 1024 × 1024 resolution from the VEDAI database. The model showed satisfactory results, with an accuracy of 91%, and then at second step, is used to process aerial images extracted from aerial video. The output vehicle bounding boxes have been processed and fed into the GIS based LinkTheDots algorithm, allowing vehicles identification and spatio-temporal tracks generation in GIS format.展开更多
Four-Wheel Independent Steering(4WIS)Vehicles can independently control the angle of each wheel,demonstrating superior trajectory tracking performance under normal conditions.However,on intermittent icy and snowy road...Four-Wheel Independent Steering(4WIS)Vehicles can independently control the angle of each wheel,demonstrating superior trajectory tracking performance under normal conditions.However,on intermittent icy and snowy roads,the presence of time-varying adhesion coefficients,time-varying cornering stiffness,and the irregularities due to ice and snow accumulation introduce multiple uncertainties into the steering system,significantly degrading the trajectory tracking performance of 4WIS vehicles.In response,this paper proposes a robust Tube Model Predictive Control(Tube-MPC)trajectory tracking control method for 4WIS.In this method,a Bi-directional Long Short-Term Memory neural network is established for online estimation of tire cornering stiffness under different road adhesion coefficients,providing accurate estimation of time-varying cornering stiffness for each wheel to mitigate the uncertainties of time-varying adhesion coefficients and cornering stiffness.Additionally,considering the road irregularities caused by snow accumulation on intermittent icy and snowy roads,a trajectory tracking controller that integrates Tube-MPC and robust Sliding Mode Control is proposed.The nominal MPC model,developed from the estimated tire cornering stiffness,utilizes the sliding surface and the optimal auxiliary control unit law for the tube is derived from the reaching law in Tube-MPC,aiming to minimize the trajectory tracking error while enhancing the controller’s robustness against road uncertainties.The experiments show that the proposed method outperforms the Tube-MPC algorithm in terms of trajectory accuracy and robustness.This method demonstrates excellent trajectory tracking accuracy under intermittent icy and snowy road conditions,and it lays a theoretical foundation for future studies on vehicle stability and trajectory tracking under such road conditions.展开更多
Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential...Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential and challenging task.In the previous studies,many vehicle detection methods have been presented.These proposed approaches mostly used either motion information or characteristic information to detect vehicles.Although these methods are effective in detecting vehicles,their detection accuracy still needs to be improved.Moreover,the headlights and windshields,which are used as the vehicle features for detection in these methods,are easily obscured in some traffic conditions.The paper aims to discuss these issues.Design/methodology/approach-First,each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model.Next,the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks.These feature blocks will be used to track the moving objects frame by frame.Findings-Using the proposed method,it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement(waving trees),which has to be deemed as background.In addition,the proposed method is able to deal with different vehicle shapes such as cars,vans,and motorcycles.Originality/value-This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.展开更多
An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehi...An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.展开更多
This paper presents algorithms for vision-based tracking and classification of vehicles in image sequences of traffic scenes recorded by a stationary camera. In the algorithms, the central moment and extended Kalman f...This paper presents algorithms for vision-based tracking and classification of vehicles in image sequences of traffic scenes recorded by a stationary camera. In the algorithms, the central moment and extended Kalman filter of tracking processes optimizes the amount of spent computational resources. Moreover, it robust to many difficult situations such as partial or full occlusions of vehicles. Vehicle classification performance is improved by Bayesian network, especially from incomplete data. The methods are test on a single Intel Pentium 4 processor 2.4 GHz and the frame rate is 25 frames/s. Experimental results from highway scenes are provided, which demonstrate the effectiveness and robust of the methods.展开更多
Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehic...Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.展开更多
Life Cycle Tracking(LCT)involves continuous monitoring and analy-sis of various activities associated with a vehicle.The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain ph...Life Cycle Tracking(LCT)involves continuous monitoring and analy-sis of various activities associated with a vehicle.The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders.Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties.Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions per-formed.Machine learning techniques when combined decentralized nature of blockchains can be used to develop a robust Vehicle LCT model.In the proposed work,Harmonic Optimized Gradient Descent andŁukasiewicz Fuzzy(HOGD-LF)Vehicle Life Cycle Tracking in Cloud Environment is proposed and it involves three stages.First,the Progressive Harmonic Optimized User Registra-tion and Authentication model is designed for computationally efficient registra-tion and authentication.Next,for the authentic user,the Gradient Descent Blockchain-based SVM Data Encryption model is designed with minimum CPU utilization.Finally,Łukasiewicz Fuzzy Smart Contract Verification is per-formed with encrypted data to ensure accurate and precise fraudulent activity deduction.The experimental analysis shows that the proposed method achieves significant performance in terms of life cycle’s prediction time,overhead,and accuracy for a different number of users.展开更多
This article proposes a linear parameter varying （LPV） switching tracking control scheme for a flexible air-breathing hypersonic vehicle （FAHV）. First, a polytopic LPV model is constructed to represent the complex...This article proposes a linear parameter varying （LPV） switching tracking control scheme for a flexible air-breathing hypersonic vehicle （FAHV）. First, a polytopic LPV model is constructed to represent the complex nonlinear longitudinal model of the FAHV by using Jacobian linearization and tensor-product （T-P） model transformation approach. Second, for less conservative controller design purpose, the flight envelope is divided into four sub-regions and a non-fragile LPV controller is designed for each parameter sub-region. These non-fragile LPV controllers are then switched in order to guarantee the closed-loop FAHV system to be asymptotically stable and satisfy a specified performance criterion. The desired non-fragile LPV switching controller is found by solving a convex constraint problem which can be efficiently solved using available linear matrix inequality （LMI） techniques, and robust stability analysis of the closed-loop FAHV system is verified based on multiple Lypapunov functions （MLFs）. Finally, numerical simulations have demonstrated the effectiveness of the proposed approach.展开更多
Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do no...Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.展开更多
To realize the stabilization and the tracking of flight control for an air-breathing hypersonic cruise vehicle, the linearization of the longitudinal model under trimmed cruise condition is processed firstly. Furtherm...To realize the stabilization and the tracking of flight control for an air-breathing hypersonic cruise vehicle, the linearization of the longitudinal model under trimmed cruise condition is processed firstly. Furthermore, the flight control problem is formulated as a robust model tracking control problem. And then, based on the robust parametric approach, eigenstructure assignment and reference model tracking theory, a parametric optimization method for robust controller design is presented. The simulation results show the effectiveness of the proposed approach.展开更多
When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,includin...When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,including high vulnerability to target occlusion and shape variations,as well as pronounced false alarms and missed detections in low signal-to-noise ratio(SNR)envi-ronments.To address these issues,this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network.The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data.Additionally,the key parameters of the algorithm are optimized through a process of selection and comparison,thereby improving the algorithm's precision.The experimental results show that the improved algo-rithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions,effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.展开更多
Traffic data collection is essential for performance assessment, safety improvement and road planning. While automated traffic data collection for highways is relatively mature, that for roundabouts is more challengin...Traffic data collection is essential for performance assessment, safety improvement and road planning. While automated traffic data collection for highways is relatively mature, that for roundabouts is more challenging due to more complex traffic scenes, data specifications and vehicle behavior. In this paper, the authors propose an automated traffic data collection system dedicated to roundabout scenes. The proposed system has mainly four steps of processing. First, camera calibration is performed for roundabout traffic scenes with a novel circle-based calibration algorithm. Second, the system uses enhanced Mixture of Gaussian algorithm with shaking removal for video segmentation, which can tolerate repeated camera displacements and background movements. Then, Kalman filtering, Kemel-based tracking and overlap-based opti- mization are employed to track vehicles while they are occluded and to derive the complete vehicle trajectories. The resulting vehicle trajectory of each individual vehicle gives the position, size, shape and speed of the vehicle at each time moment. Finally, a data mining algorithm is used to automatically extract the interested traffic data from the vehicle trajectories. The overall traffic data collection system has been implemented in software and runs on regular PC. The total processing time for a 3-hour video is currently 6 h. The automated traffic data collection system can significantly reduce cost and improve efficiency compared to manual data collection. The extracted traffic data have been compared to accurate manual measurements for 29 videos recorded on 29 different days, and an accuracy of more than 90% has been achieved.展开更多
This paper outlines research findings from an investigation into a range of options for generating vehicle data relevant to traffic management systems.Linking data from freight vehicles with traffic management systems...This paper outlines research findings from an investigation into a range of options for generating vehicle data relevant to traffic management systems.Linking data from freight vehicles with traffic management systems stands to provide a number of benefits.These include reducing congestion,improving safety,reducing freight vehicle trip times,informing alternative routing for freight vehicles,and informing transport planning and investment decisions.This paper will explore a number of different methods to detect,classify,and track vehicles,each having strengths and weaknesses,and each with different levels of accuracy and associated costs.In terms of freight management applications,the key feature is the capability to track in real time the position of the vehicle.This can be done using a range of technologies that either are located on the vehicle such as GPS(global positioning system)trackers and RFID(Radio Frequency Identification)Tags or are part of the network infrastructure such as CCTV(Closed Circuit Television)cameras,satellites,mobile phone towers,Wi-Fi receivers and RFID readers.Technology in this space is advancing quickly having started with a focus on infrastructure based sensors and communications devices and more recently shifting to GPS and mobile devices.The paper concludes with an overview of considerations for how data from freight vehicles may interact with traffic management systems for mutual benefit.This new area of research and practice seeks to balance the needs of traffic management systems in order to better manage traffic and prevent bottlenecks and congestion while delivering tangible benefits to freight companies stands to be of great interest in the coming decade.This research has been developed with funding and support provided by Australia’s SBEnrc(Sustainable Built Environment National Research Centre)and its partners.展开更多
The track model used in the dynamic analysis and design system software is investigated. A home made tank is taken as an example to illustrate the method for modeling an integral tracked vehicle and perform the dynam...The track model used in the dynamic analysis and design system software is investigated. A home made tank is taken as an example to illustrate the method for modeling an integral tracked vehicle and perform the dynamic simulation. The obtained results have demonstrated that the simulation method has the advantage of high efficiency, more convenience and more insight into the dynamical behavior of the system.展开更多
In order to achieve precise,robust autonomous guidance and control of a tracked vehicle,a kinematic model with longitudinal and lateral slip is established,Four different nonlinear filters are used to estimate both st...In order to achieve precise,robust autonomous guidance and control of a tracked vehicle,a kinematic model with longitudinal and lateral slip is established,Four different nonlinear filters are used to estimate both state vector and time-varying parameter vector of the created model jointly.The first filter is the well-known extended Kalman filter.The second filter is an unscented version of the Kalman filter.The third one is a particle filter using the unscented Kalman filter to generate the importance proposal distribution.The last one is a novel and guaranteed filter that uses a linear set-membership estimator and can give an ellipsoid set in which the true state lies.The four different approaches have different complexities,behavior and advantages that are surveyed and compared.展开更多
Based on main physical and mechanical properties of deep-sea sediment from C-C poly-metallic nodule mining area in the Pacific Ocean, the best sediment simulant was successfully prepared by mixing bentonite with a cer...Based on main physical and mechanical properties of deep-sea sediment from C-C poly-metallic nodule mining area in the Pacific Ocean, the best sediment simulant was successfully prepared by mixing bentonite with a certain content of water. Compression-shear coupling rheological constitutive model of the sediment simulant was established by endochronic theory and the coupling rheological parameters were obtained by compressive and compression-shear creep tests. A new calculation formula of turning traction force of the tracked mining vehicle was first derived based on the coupling rheological model and consideration of pushing resistance and sinkage of the tracked mining vehicle. Effects of the turning velocity, crawler spacing and contacting length of crawler with deep-sea sediment on the turning traction force were analyzed. Research results can provide theoretical foundation for operation safety and optimal design of the tracked mining vehicle.展开更多
The sinkage of a moving tracked mining vehicle is greatly af fected by the combined compression-shear rheological properties of soft deep-sea sediments. For test purposes, the best sediment simulant is prepared based ...The sinkage of a moving tracked mining vehicle is greatly af fected by the combined compression-shear rheological properties of soft deep-sea sediments. For test purposes, the best sediment simulant is prepared based on soft deep-sea sediment from a C-C poly-metallic nodule mining area in the Pacific Ocean. Compressive creep tests and shear creep tests are combined to obtain compressive and shear rheological parameters to establish a combined compressive-shear rheological constitutive model and a compression-sinkage rheological constitutive model. The combined compression-shear rheological sinkage of the tracked mining vehicle at dif ferent speeds is calculated using the Recur Dyn software with a selfprogrammed subroutine to implement the combined compression-shear rheological constitutive model. The model results are compared with shear rheological sinkage and ordinary sinkage(without consideration of rheological properties). These results show that the combined compression-shear rheological constitutive model must be taken into account when calculating the sinkage of a tracked mining vehicle. The combined compression-shear rheological sinkage decrease with vehicle speed and is the largest among the three types of sinkage. The developed subroutine in the Recur Dyn software can be used to study the performance and structural optimization of moving tracked mining vehicles.展开更多
基金The National Natural Science Foundation of China(No.60972001,61374194)
文摘To track the vehicles under occlusion, a vehicle tracking algorithm based on blocks is proposed. The target vehicle is divided into several blocks of uniform size, in which the edge block can overlap its neighboring blocks. All the blocks＇ motion vectors are estimated, and the noise motion vectors are detected and adjusted to decrease the error of motion vector estimation. Then, by moving the blocks based on the adjusted motion vectors, the vehicle is tracked. Aiming at the occlusion between vehicles, a Markov random field is established to describe the relationship between the blocks in the blocked regions. The neighborhood of blocks is defined using the Euclidean distance. An energy function is defined based on the blocks＇ histograms and optimized by the simulated annealing algorithm to segment the occlusion region. Experimental results demonstrate that the proposed algorithm can track vehicles under occlusion accurately.
基金Supported by the National Natural Science Foundation of China(No.61005034)China Postdoctoral Science Foundation and under Grant(No.2012M510768)the Science Foundation of Hebei Province under Grant(No.F2012203182)
文摘A real-time vehicle tracking method is proposed for trattlC monitoring system at roau mte^cc- tions, and the vehicle tracking module consists of an initialization stage and a tracking stage. Li- cense plate location based on edge density and color analysis is used to detect the license plate re- gion for tracking initialization. In the tracking stage, covariance matching is employed to track the license plate. Genetic algorithm is used to reduce the computational cost. Real-time image tracking of multi-lane vehicles is achieved. In the experiment, test videos are recorded in advance by record- ers of actual E-police systems erage false detection rate and at several different city intersections. In the tracking module, the av- missed plates rate are 1.19%, and 1.72%, respectively.
文摘Multi-object tracking is a vital problem as many applications require better tracking approaches.Although learning-based detectors are becoming extremely powerful,there are few tracking methods designed to work with them in real time.We explored an efficient flexible online vehicle tracking-by-detection framework suitable for real-virtual mapping systems,which combines a non-recursive temporal window search with delayed output and produces stable trajectories despite noisy detection responses.Its computation speed meets the real-time requirements,whereas its performance is comparable with that of state-of-the-art online trackers on the DETRAC dataset.The trajectories from our approach also contain the target class and color information important for virtual vehicle motion reconstruction.
文摘The latest advances in Deep Learning based methods and computational capabilities provide new opportunities for vehicle tracking. In this study, YOLOv2 (You Only Look Once—version 2) is used as an open source Convolutional Neural Network (CNN), to process high-resolution satellite images, in order to generate the spatio-temporal GIS (Geographic Information System) tracks of moving vehicles. At first step, YOLOv2 is trained with a set of images of 1024 × 1024 resolution from the VEDAI database. The model showed satisfactory results, with an accuracy of 91%, and then at second step, is used to process aerial images extracted from aerial video. The output vehicle bounding boxes have been processed and fed into the GIS based LinkTheDots algorithm, allowing vehicles identification and spatio-temporal tracks generation in GIS format.
基金Supported by National Natural Science Foundation of China(Grant Nos.52405112,U24A20199)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20240973).
文摘Four-Wheel Independent Steering(4WIS)Vehicles can independently control the angle of each wheel,demonstrating superior trajectory tracking performance under normal conditions.However,on intermittent icy and snowy roads,the presence of time-varying adhesion coefficients,time-varying cornering stiffness,and the irregularities due to ice and snow accumulation introduce multiple uncertainties into the steering system,significantly degrading the trajectory tracking performance of 4WIS vehicles.In response,this paper proposes a robust Tube Model Predictive Control(Tube-MPC)trajectory tracking control method for 4WIS.In this method,a Bi-directional Long Short-Term Memory neural network is established for online estimation of tire cornering stiffness under different road adhesion coefficients,providing accurate estimation of time-varying cornering stiffness for each wheel to mitigate the uncertainties of time-varying adhesion coefficients and cornering stiffness.Additionally,considering the road irregularities caused by snow accumulation on intermittent icy and snowy roads,a trajectory tracking controller that integrates Tube-MPC and robust Sliding Mode Control is proposed.The nominal MPC model,developed from the estimated tire cornering stiffness,utilizes the sliding surface and the optimal auxiliary control unit law for the tube is derived from the reaching law in Tube-MPC,aiming to minimize the trajectory tracking error while enhancing the controller’s robustness against road uncertainties.The experiments show that the proposed method outperforms the Tube-MPC algorithm in terms of trajectory accuracy and robustness.This method demonstrates excellent trajectory tracking accuracy under intermittent icy and snowy road conditions,and it lays a theoretical foundation for future studies on vehicle stability and trajectory tracking under such road conditions.
文摘Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential and challenging task.In the previous studies,many vehicle detection methods have been presented.These proposed approaches mostly used either motion information or characteristic information to detect vehicles.Although these methods are effective in detecting vehicles,their detection accuracy still needs to be improved.Moreover,the headlights and windshields,which are used as the vehicle features for detection in these methods,are easily obscured in some traffic conditions.The paper aims to discuss these issues.Design/methodology/approach-First,each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model.Next,the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks.These feature blocks will be used to track the moving objects frame by frame.Findings-Using the proposed method,it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement(waving trees),which has to be deemed as background.In addition,the proposed method is able to deal with different vehicle shapes such as cars,vans,and motorcycles.Originality/value-This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.
文摘An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.
文摘This paper presents algorithms for vision-based tracking and classification of vehicles in image sequences of traffic scenes recorded by a stationary camera. In the algorithms, the central moment and extended Kalman filter of tracking processes optimizes the amount of spent computational resources. Moreover, it robust to many difficult situations such as partial or full occlusions of vehicles. Vehicle classification performance is improved by Bayesian network, especially from incomplete data. The methods are test on a single Intel Pentium 4 processor 2.4 GHz and the frame rate is 25 frames/s. Experimental results from highway scenes are provided, which demonstrate the effectiveness and robust of the methods.
基金funded by Researchers Supporting Project Number(RSP2023R503),King Saud University,Riyadh,Saudi Arabia。
文摘Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.
基金The authors wish to express their sincere thanks to the Department of Science&Technology,New Delhi,India(Project ID:SR/FST/ETI-371/2014)express their sincere thanks to the INSPIRE fellowship(DST/INSPIRE Fellowship/2016/IF160837)for their financial support.The authors also thank SASTRA Deemed to be University,Thanjavur,India for extending the infrastructural support to carry out this work.
文摘Life Cycle Tracking(LCT)involves continuous monitoring and analy-sis of various activities associated with a vehicle.The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders.Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties.Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions per-formed.Machine learning techniques when combined decentralized nature of blockchains can be used to develop a robust Vehicle LCT model.In the proposed work,Harmonic Optimized Gradient Descent andŁukasiewicz Fuzzy(HOGD-LF)Vehicle Life Cycle Tracking in Cloud Environment is proposed and it involves three stages.First,the Progressive Harmonic Optimized User Registra-tion and Authentication model is designed for computationally efficient registra-tion and authentication.Next,for the authentic user,the Gradient Descent Blockchain-based SVM Data Encryption model is designed with minimum CPU utilization.Finally,Łukasiewicz Fuzzy Smart Contract Verification is per-formed with encrypted data to ensure accurate and precise fraudulent activity deduction.The experimental analysis shows that the proposed method achieves significant performance in terms of life cycle’s prediction time,overhead,and accuracy for a different number of users.
基金co-supported by National Outstanding Youth Science Foundation(No.61125306)National Natural Science Foundation of Major Research Plan(Nos.91016004,61034002)+1 种基金Research Fund for the Doctoral Program of Higher Education of China(No.20110092110020)the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1103)
文摘This article proposes a linear parameter varying （LPV） switching tracking control scheme for a flexible air-breathing hypersonic vehicle （FAHV）. First, a polytopic LPV model is constructed to represent the complex nonlinear longitudinal model of the FAHV by using Jacobian linearization and tensor-product （T-P） model transformation approach. Second, for less conservative controller design purpose, the flight envelope is divided into four sub-regions and a non-fragile LPV controller is designed for each parameter sub-region. These non-fragile LPV controllers are then switched in order to guarantee the closed-loop FAHV system to be asymptotically stable and satisfy a specified performance criterion. The desired non-fragile LPV switching controller is found by solving a convex constraint problem which can be efficiently solved using available linear matrix inequality （LMI） techniques, and robust stability analysis of the closed-loop FAHV system is verified based on multiple Lypapunov functions （MLFs）. Finally, numerical simulations have demonstrated the effectiveness of the proposed approach.
基金Project(2009AA11Z220)supported by the National High Technology Research and Development Program of China
文摘Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.
基金Sponsored by the Major Program of National Natural Science Foundation of China (Grant No.60710002)the Program for Changjiang Scholars and Innovative Research Team in University
文摘To realize the stabilization and the tracking of flight control for an air-breathing hypersonic cruise vehicle, the linearization of the longitudinal model under trimmed cruise condition is processed firstly. Furthermore, the flight control problem is formulated as a robust model tracking control problem. And then, based on the robust parametric approach, eigenstructure assignment and reference model tracking theory, a parametric optimization method for robust controller design is presented. The simulation results show the effectiveness of the proposed approach.
基金supported in part by National Natural Science Founda-tion of China(No.62372284)in part by Shanghai Nat-ural Science Foundation(No.24ZR1421800).
文摘When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,including high vulnerability to target occlusion and shape variations,as well as pronounced false alarms and missed detections in low signal-to-noise ratio(SNR)envi-ronments.To address these issues,this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network.The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data.Additionally,the key parameters of the algorithm are optimized through a process of selection and comparison,thereby improving the algorithm's precision.The experimental results show that the improved algo-rithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions,effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.
文摘Traffic data collection is essential for performance assessment, safety improvement and road planning. While automated traffic data collection for highways is relatively mature, that for roundabouts is more challenging due to more complex traffic scenes, data specifications and vehicle behavior. In this paper, the authors propose an automated traffic data collection system dedicated to roundabout scenes. The proposed system has mainly four steps of processing. First, camera calibration is performed for roundabout traffic scenes with a novel circle-based calibration algorithm. Second, the system uses enhanced Mixture of Gaussian algorithm with shaking removal for video segmentation, which can tolerate repeated camera displacements and background movements. Then, Kalman filtering, Kemel-based tracking and overlap-based opti- mization are employed to track vehicles while they are occluded and to derive the complete vehicle trajectories. The resulting vehicle trajectory of each individual vehicle gives the position, size, shape and speed of the vehicle at each time moment. Finally, a data mining algorithm is used to automatically extract the interested traffic data from the vehicle trajectories. The overall traffic data collection system has been implemented in software and runs on regular PC. The total processing time for a 3-hour video is currently 6 h. The automated traffic data collection system can significantly reduce cost and improve efficiency compared to manual data collection. The extracted traffic data have been compared to accurate manual measurements for 29 videos recorded on 29 different days, and an accuracy of more than 90% has been achieved.
基金funding and support provided by Australia’s SBEnrc(Sustainable Built Environment National Research Centre)and its partners.
文摘This paper outlines research findings from an investigation into a range of options for generating vehicle data relevant to traffic management systems.Linking data from freight vehicles with traffic management systems stands to provide a number of benefits.These include reducing congestion,improving safety,reducing freight vehicle trip times,informing alternative routing for freight vehicles,and informing transport planning and investment decisions.This paper will explore a number of different methods to detect,classify,and track vehicles,each having strengths and weaknesses,and each with different levels of accuracy and associated costs.In terms of freight management applications,the key feature is the capability to track in real time the position of the vehicle.This can be done using a range of technologies that either are located on the vehicle such as GPS(global positioning system)trackers and RFID(Radio Frequency Identification)Tags or are part of the network infrastructure such as CCTV(Closed Circuit Television)cameras,satellites,mobile phone towers,Wi-Fi receivers and RFID readers.Technology in this space is advancing quickly having started with a focus on infrastructure based sensors and communications devices and more recently shifting to GPS and mobile devices.The paper concludes with an overview of considerations for how data from freight vehicles may interact with traffic management systems for mutual benefit.This new area of research and practice seeks to balance the needs of traffic management systems in order to better manage traffic and prevent bottlenecks and congestion while delivering tangible benefits to freight companies stands to be of great interest in the coming decade.This research has been developed with funding and support provided by Australia’s SBEnrc(Sustainable Built Environment National Research Centre)and its partners.
文摘The track model used in the dynamic analysis and design system software is investigated. A home made tank is taken as an example to illustrate the method for modeling an integral tracked vehicle and perform the dynamic simulation. The obtained results have demonstrated that the simulation method has the advantage of high efficiency, more convenience and more insight into the dynamical behavior of the system.
基金This project is supported by National Hi-tech Research and Development Program of China(863 program,No.2006AA04Z215).
文摘In order to achieve precise,robust autonomous guidance and control of a tracked vehicle,a kinematic model with longitudinal and lateral slip is established,Four different nonlinear filters are used to estimate both state vector and time-varying parameter vector of the created model jointly.The first filter is the well-known extended Kalman filter.The second filter is an unscented version of the Kalman filter.The third one is a particle filter using the unscented Kalman filter to generate the importance proposal distribution.The last one is a novel and guaranteed filter that uses a linear set-membership estimator and can give an ellipsoid set in which the true state lies.The four different approaches have different complexities,behavior and advantages that are surveyed and compared.
基金Projects(51274251,11502226)supported by the National Natural Science Foundation of China
文摘Based on main physical and mechanical properties of deep-sea sediment from C-C poly-metallic nodule mining area in the Pacific Ocean, the best sediment simulant was successfully prepared by mixing bentonite with a certain content of water. Compression-shear coupling rheological constitutive model of the sediment simulant was established by endochronic theory and the coupling rheological parameters were obtained by compressive and compression-shear creep tests. A new calculation formula of turning traction force of the tracked mining vehicle was first derived based on the coupling rheological model and consideration of pushing resistance and sinkage of the tracked mining vehicle. Effects of the turning velocity, crawler spacing and contacting length of crawler with deep-sea sediment on the turning traction force were analyzed. Research results can provide theoretical foundation for operation safety and optimal design of the tracked mining vehicle.
基金Supported by the National Natural Science Foundation of China(Nos.51274251,11502226)
文摘The sinkage of a moving tracked mining vehicle is greatly af fected by the combined compression-shear rheological properties of soft deep-sea sediments. For test purposes, the best sediment simulant is prepared based on soft deep-sea sediment from a C-C poly-metallic nodule mining area in the Pacific Ocean. Compressive creep tests and shear creep tests are combined to obtain compressive and shear rheological parameters to establish a combined compressive-shear rheological constitutive model and a compression-sinkage rheological constitutive model. The combined compression-shear rheological sinkage of the tracked mining vehicle at dif ferent speeds is calculated using the Recur Dyn software with a selfprogrammed subroutine to implement the combined compression-shear rheological constitutive model. The model results are compared with shear rheological sinkage and ordinary sinkage(without consideration of rheological properties). These results show that the combined compression-shear rheological constitutive model must be taken into account when calculating the sinkage of a tracked mining vehicle. The combined compression-shear rheological sinkage decrease with vehicle speed and is the largest among the three types of sinkage. The developed subroutine in the Recur Dyn software can be used to study the performance and structural optimization of moving tracked mining vehicles.
