YOLOv2 Deep Learning Model and GIS Based Algorithms for Vehicle Tracking

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.

Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

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.

A hierarchical clustering of features approach for vehicle tracking in traffic environments

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.

Efficient Online Vehicle Tracking for Real-Virtual Mapping Systems

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.

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

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.

Tracking control for air-breathing hypersonic cruise vehicle based on tangent linearization approach

This paper is focused on developing a tracking controller for a hypersonic cruise vehicle using tangent linearization approach.The design of flight control systems for air-breathing hypersonic vehicles is a highly challenging task due to the unique characteristics of the vehicle dynamics.Motivated by recent results on tangent linearization control,the tracking control problem for the hypersonic cruise vehicle is reduced to that of a feedback stabilizing controller design for a linear time-varying system which can be accomplished by a standard design method of frozen-time control.Through a proper model transformation,it can be proven that the tracking error of the designed closed-loop system decays exponentially.Simulation studies are conducted for trimmed cruise conditions of 110000 ft and Mach 15 where the responses of the vehicle to step changes in altitude and velocity are evaluated.The effectiveness of the controller is demonstrated by simulation results.

Secured Vehicle Life Cycle Tracking Using Blockchain and Smart Contract

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.

Development of a tracking-based system for automated traffic data collection for roundabouts

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.

Compare and Contrast of Options to Collect Freight Vehicle Data in Order to Inform 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.

A Study on the Dynamic Analysis of A Tracked Vehicle for Ocean Mining on the Deep Seabed

A study is presented on the dynamic analysis of a tracked vehicle for mining on the deep seabed of very soft soil. Equations for the interaction between the track and extremely soft seabed are employed to develop a track/soil interaction module called TVAS. The vehicle is modeled as a multibody dynamic system by the use of a multibody dynamic analysis program. The module developed is cooperated with the multibody dynamic analysis program with a user-defined subroutine. The dynamic behavior and the conceptual design of the mining vehicle on the deep seabed are investigated.

Predicting the sinkage of a moving tracked mining vehicle using a new rheological formulation for soft deep-sea sediment

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.

Study on Steering Control Strategy for High-Speed Tracked Vehicle with Hydrostatic Drive

Steering control strategy for high-speed tracked vehicle with hydrostatic drive is designed based on analyzing the fundamental steering theories of the hydrostatic drive tracked vehicle. The strategy is completed by the cooperation between integrated steering control unit and pump ＆ motor displacement controller. The steering simulation is conducted by using Simulink of Matlab. It is indicated that this steering control strategy can reduce the average vehicle speed automatically to achieve the driver＇s expected steering radius exactly in the case of en- suring not exceeding the system pressure threshold and no sideslip.

NONLINEAR ESTIMATION METHODS FOR AUTONOMOUS TRACKED VEHICLE WITH SLIP

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.

Chassis tra fficability simulation and experiment of a LY1352JP forest tracked vehicle

Based on the analysis of complex terrains and current forest transportation equipment,a forest tracked vehicle prototype LY1352 JP was developed.The road model and the virtual prototype of the chassis were constructed using dynamic simulation software RecurDyn.The optimal tension of the vehicle as well as its capabilities for crossing trenches,climbing vertical walls,uphill and downhill slopes were simulated.The simulation results showed that the optimum tension force of the chassis of the vehicle was 63 kN(kilonewton),accounting for 45%of the total vehicle weight.The maximum trench crossing width and vertical obstacle climbing height were 1.35 m and 0.45 m,respectively.The maximum uphill and downhill angles were 50°and 45°,respectively.Tests on the prototype capacity for crossing trenches,and uphill and downhill driving were carried out.The test results were in agreement with the simulation results.A cross-country performance of a fire truck based on the tracked vehicle chassis was conducted in an old-growth forest.Tests verified that the vehicle has a strong forest trafficability performance and can meet the needs for forest transportation.

Slip-Control Strategy of Dual Independent Electric Drive Tracked Vehicle

In order to improve the brake performance of a dual independent electric drive tracked vehicle,a dynamic model for braking situation was established.Then,a sliding model controller(SMC)with an auxiliary system was designed to control the slip and its effectiveness was proved.A hardware-in-loop simulation through MATLAB/XPC was compared with the normal SMC and normal integral sliding mode controller(ISMC),the results show that SMC with the auxiliary system has a better performance:a smaller overshoot and steady state error.The disturbance is suppressed effectively.In the initial speed of 65.km/h,the brake distance was shortened by 3.4%and 6.8%compared with the other two methods,respectively.Finally,initial speeds of 30-36.km/h tests was carried out on a flat soil road.Compared with a no-control brake,the displacement was shortened by 1.8.m.It demonstrates the effectiveness of the slip-control strategy.In the same situation,the error between the simulation and test is 18.1%,which validates the accuracy of models.

Simulation study on starting of dual clutch transmission for tracked vehicles

In order to study the application of dual clutch transmission （DCT） on tracked vehicles, a simulation moclel and two control methods were performed for starting analysis. Based on a certain transmission of an existing tracked vehicle, a DCT structure was proposed. Matlab/Simulink was a dopted as a platform to develop the simulation model. The engine speed was controlled to follow the target speed as a launch strategy. Two control methods, a proportional integral derivative （ PID ） control method and a fuzzy control method, were proposed to control the engine throttle and oil pressure in order to track the target engine speed. Simulation results show that either the PID control or fuzzy control method can improve the starting performance compared with no loop control meth od. Fuzzy control method can lead a better starting quality compared with PID control method.

Track Tension Analysis of Four-Wheel Drive Tracked Vehicles

The distribution of track tension on track link is complex when the tracked vehicles run at a high speed.A multi-drive track link structure,which changes the traditional induction wheel into the driving wheel was proposed.The mathematical model of the system was established and the distribution of track tension was studied.The combined simulation model of RecurDyn and Simulink of the structure with multi-drive track was established.The simulation results show that our proposed structure has more uniform tension distribution than traditional structures,especially under the high speed condition.The maximum tension can be reduced by 28 kN-36 kN and the transmission efficiency can be improved by10%-16% under high speed condition with this new structure.

Nonlinear Derivative and Integral Sliding Control for Tracked Vehicle Steering with Hydrostatic Drive

In the steering process of tracked vehicle with hydrostatic drive,the motion and resistance states of the vehicle are always of uncertain and nonlinear characteristics,and these states may undergoe large-scale changes.Therefore,it is significant to enhance the steering stability of tracked vehicle with hydrostatic drive to meet the need of future battlefield.In this paper,a sliding mode control algorithm is proposed and applied to achieve desired yaw rates.The speed controller and the yaw rate controller are designed through the kinematics and dynamics analysis.In addition,the nonlinear derivative and integral sliding mode control algorithm is designed,which is supposed to efficiently reduce the integration saturation and the disturbances from the unsmooth road surfaces through a conditional integrator approach.Moreover,it improves the response speed of the system and reduces the chattering by the derivative controller.The hydrostatic tracked vehicle module is modeled with a multi-body dynamic software RecurDyn and the steering control strategy module is modeled by MATLAB/Simulink.The co-simulation results of the whole model show that the control strategy can improve the vehicle steering response speed and also ensure a smooth control output with small chattering and strong robustness.

Dynamic Characteristics of Tracked Vehicle Power Train Considering Road Random Torsional Excitation

The road random torsional excitation is one type of torque rooted from the road roughness and vehicle drive system. This paper aims to study how the road random torsional excitation affects the dynamic characteristics of vehicle power train. The method of simulating the random torsional excitation of tracked vehicle is explored at first. Secondly,the road random torsional excitations under different road roughness,vehicle speeds and pre-tensions are obtained. Thirdly,the dynamic analysis model of tracked vehicle power train is constructed with the consideration of the road random torsional excitation. Eventually,the influences of this excitation on output torque,bearing support force,vibration acceleration and dynamic shear stress of transmission shafts are intensively studied.The research conclusions are helpful to correct and refine the present virtual prototype of tracked vehicle power train.

Analyses and Simulation of Fuzzy Logic Control for Suspension System of a Track Vehicle

The vibration caused by terrible road excitation affects the ride quality and safety of track vehicles. The vibration control of suspension systems is a very important factor for modern track vehicles. A fuzzy logic control for suspension system of a track vehicle is presented. A mechanical model and a system of difft, rential equations of motion taking account of the mass of loading wheel are established. Then the fuzzy logic control is applied to control the vibration of suspension system of track vehicles for sine signal and random road surfaces. Numerical simulation shows that the maximum acceleration of suspension system can be reduced to 44 % of the original value for sine signal road surface, and the mean square root of acceleration of suspension system can be reduced to 21% for random road surface. Therefore, the proposed fuzzy logic control is an efficient method for the suspension systems of track vehicles.