Vehicle Tracking Research Articles

Vehicle Tracking System Approaches: A Systematic Literature Review

Vehicle Tracking System Approaches: A Systematic Literature Review

Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data.

Velocity-constrained distributed formation tracking of fixed-wing UAVs with multiple leaders

This paper addresses the safety-critical formation tracking of fixed-wing unmanned aerial vehicles, where both the velocity constraint and multiple leaders are taken into consideration. A hierarchical control strategy, consisting of a distributed observer and a formation controller, is elaborately constructed to achieve the desired configuration with the velocity in a safe range. First, a novel distributed observer is designed to estimate the formation error, which promises the practical fixed-time convergence of the estimation error despite unknown inputs of leaders and disturbances. Then, the velocity constraint is enforced through the forward invariance of a safety set, for which a robust control barrier function is carefully constructed considering the effect of disturbances. Further, a local formation controller is established by embedding the robust control barrier function into quadratic programming. Compared with existing results, the proposed controller is able to achieve the formation configuration while satisfying velocity safety, even in the presence of disturbances. Simulations are conducted to demonstrate the proposed method's capability in guaranteeing velocity constraint and stabilizing formation errors.

Trajectory Tracking and Docking Control Strategy for Unmanned Surface Vehicles in Water-Based Search and Rescue Missions

Trajectory Tracking and Docking Control Strategy for Unmanned Surface Vehicles in Water-Based Search and Rescue Missions

Trajectory Control of Quadrotors via Spiking Neural Networks

In this study, a novel control scheme based on spiking neural networks (SNNs) has been proposed to accomplish the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs). The update rules for the network parameters have been derived using the Lyapunov stability theorem. Three different trajectories have been utilized in the simulated and experimental studies to verify the efficacy of the proposed control scheme. The acquired results have been compared with the responses obtained for proportional–integral–derivative (PID) and traditional neural network controllers. Simulated and experimental studies demonstrate that the proposed SNN-based controller is capable of providing better tracking accuracy and robust system response in the presence of disturbing factors.

Retraction Note: An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm

Retraction Note: An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm

Dynamic Tracking Method Based on Improved DeepSORT for Electric Vehicle

The development of electric vehicles has facilitated intelligent transportation, which requires the swift and effective detection and tracking of moving vehicles. To satisfy this demand, this paper presents an enhanced DeepSORT algorithm. By selecting YOLO-SSFS as the front-end detector and incorporating a lightweight and high-precision feature training network called FasterNet, the proposed method effectively extracts vehicle appearance attributes. Besides this, the noise scale adaptive Kalman filter is implemented and the conventional cascade matching process is substituted with global join matching, thereby enhancing overall performance and tracking accuracy. Validation conducted on the VisDrone dataset demonstrates the superiority of this method compared to the original DeepSORT algorithm, exhibiting a 4.76% increase in tracking accuracy and a 3.10% improvement in tracking precision. The findings reveal the advantages of the algorithms in the domain of vehicle detection and tracking, allowing significant technological advancements in intelligent transportation systems.

Dynamic modelling and experimental validation of a dynamic track stabiliser vehicle–track spatially coupling dynamics system

A dynamic track stabiliser vehicle is an unconventional vehicle for maintaining the quality of ballasted tracks, and its dynamic performance has a significant impact on its working efficiency. For the first time, this paper proposes a comprehensive dynamic track stabiliser vehicle–track coupled vertical and lateral multi-body dynamics model based on classical vehicle–track coupling dynamics theory. In this model, detailed attention is given to the vertical, lateral, roll, yaw, and pitch motions of the components, including the vehicle body, bogies, wheelsets and stabilisers. The Shen–Hedrick–Elkins theory is employed to describe non-linear wheel–rail contact relationships between the bogie wheelsets and the rails, and three various methodologies are adopted to address complicated wheel–rail interaction problems between the stabiliser wheelsets and the rails. This comprehensive multi-body dynamics model enables a detailed investigation of the dynamic performance of the system under complex excitations, such as the lateral excitation of the stabilisers and vertical downward pressure exerted by hydraulic cylinders, especially the dynamic response of the stabilisers in the vibration environment of the complete vehicle. The dynamics model was fully validated by comparing its results with time- and frequency-domain data obtained from field tests. The results indicate that the model is capable of being used for analysis of the dynamic performance of dynamic track stabiliser vehicles.

Adaptive sliding mode control with nonlinear MPC-based obstacle avoidance using LiDAR for an autonomous surface vehicle under disturbances

This manuscript proposes a path-following and collision avoidance guidance and control system for an autonomous surface vehicle operating in cluttered environments under perturbations. A nonlinear model predictive control strategy handles the state and control constraints for obtaining a guidance law. The approach considers the vessel sideslip angle to provide the desired heading angle, satisfying path-following and obstacle avoidance. Additionally, to solve the nonlinear optimization problem for obtaining the guidance law in real-time, the acados software package is used. Furthermore, in order to guarantee that the vehicle tracks the guidance in presence of model uncertainties and external disturbances, a robust adaptive sliding mode low-level controller is adopted. This controller is finite time convergent, and dynamically adapts its control gain to use only the necessary control inputs. Furthermore, a low-memory and computationally-light obstacle detection strategy employing a LiDAR sensor is presented and proven to run in real-time. Simulation and experimental tests conducted using a customized prototype validate the advantages of the proposed strategy and demonstrate the effectiveness of the path following while evading multiple obstacles in the presence of external disturbances and model discrepancies.

Localized rail third-order bending mode causes high-order polygonization of high-speed train wheels

The formation mechanism of high-order (mainly 18 ∼ 23) polygonization of high-speed train wheels has been debated for over a decade. Currently, researchers believe that the mechanisms leading to the high-order polygonization of high-speed train wheels mainly include: (A) vibration of bogie components, (B) self-excited vibration of wheel-rail friction, (C) third-order bending (B3) modal vibration of a segment of localized rail constrained by a bogie, etc. Of these, mechanism C was previously proposed by our team and has been insisted on until now, although it is highly controversial. To further prove the plausibility of mechanism C, through rail modal tests, vehicle tracking tests, and dynamics simulation analyses, this paper systematically proves that (1) the rail B3 mode can induce resonance of the wheel-rail system, which is the fundamental factor causing the high-order polygonization of high-speed train wheels, and (2) there is no significant frequency shift between the vibration frequency of the excited rail B3 mode and the passing frequency of the formed polygonal wheel, with an extreme difference being less than the wheel rotation frequency.

A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning

In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles’ characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network’s ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements.

Barrier Lyapunov Function-based Backstepping Controller Design for Path Tracking of Autonomous Vehicles

This research proposes a novel BLF-based backstepping controller for path tracking of Autonomous Vehicles (AVs) with unknown dynamics and unmeasurable states. The proposed framework includes: (1) forming geometric-dynamic model of the vehicle by combining the dynamics of the vehicle with the kinematics of the visual measurement system, (2) designing a fixed-time Extended-State Observer (ESO) to estimate the unknown dynamics and unmeasurable states, and (3) introducing a BLF-based controller for faster response and more accurate path tracking compared to previous BLF-based controllers. Besides the novelty of the BLF-based controller, by transforming the closed-loop error dynamics into a unified proportional-derivative (PD)-type structure, an intuitive criterion is proposed to provide a systematic procedure for comparing BLF-based controllers. A combined BLF is further proposed based on this performance criterion to eliminate the sensitivity of BLF-based controllers to the magnitude of the constraint. The stability analysis is performed for the fixed-time ESO and the closed-loop control system. MATLAB/CarSim co-simulation is conducted to evaluate the performance of the proposed control system. The outcomes of the work show that the closed-loop control system is exponentially stable. In addition, it can provide a faster response and result in more accurate path tracking compared to previous BLF-based control systems.

Optical Detection of Underwater Propeller Wake Based on a Position-Sensitive Detector

The study of underwater vehicle wake detection is of significant importance within the field of target detection, localisation, and tracking of underwater vehicles. Given that propellers are the propellers of modern ships and underwater vehicles, the propeller wake field represents the principal target source for wake detection in underwater vehicles. The objective of this paper is to propose a method for measuring the wake of an underwater propeller based on a position-sensitive detector. A theoretical model of the relationship between the laser spot displacement and the change in the refractive index of the wake field is established on the basis of the principle of laser beam deflection. A prototype experimental setup for underwater propeller wake measurement was constructed based on the aforementioned optical measurement method. Furthermore, the simulation of the propeller wake flow field with strong density stratification and linear density stratification was conducted based on the experimental setup. Furthermore, experiments were conducted to detect the flow field of a propeller wake. The experimental results indicate that the wake dissipation times of the propeller in a strong density-stratified water environment are approximately 800 s and 750 s. Following the stabilisation of the wake field density, the laser spot position is observed to be stable at 0.341 mm and 0.441 mm, respectively, with a corresponding refractive index change of 2.99 × 10−6 RIU (refractive index unit) and 3.87 × 10−6 RIU, respectively. These experimental results are found to be in general agreement with the simulation results of the propeller wake field. A comparison of the experimental wake measurements based on the device with the wake measurements based on a CTD (conductivity–temperature–depth) device reveals a consistent trend. The realisation of this detection technique is of great significance for the advancement of research in the field of optical detection of underwater vehicle wake streams.

Distributed avoidance-accommodating flexible performance approach for adaptive formation tracking of underactuated surface vehicles against moving obstacles

A novel strategy for formation tracking of multiple uncertain underactuated surface vehicles is introduced in this study, to maintain the prescribed formation performance in the presence of moving obstacles with unknown velocities. Unlike the existing methods for prescribed formation performance control, our key contribution is the development of distributed avoidance-accommodating flexible performance functions (FPFs). These functions prevent deviation from the prescribed performance bounds while navigating around moving obstacles, considering input saturation and model uncertainties. We design local adaptive formation controllers by using distributed posture errors and avoidance-accommodating FPFs. Auxiliary signals that facilitate adjustment of the posture errors and performance functions during obstacle avoidance are derived using a recursive design that ensures Lyapunov stability. Additionally, command filters for virtual control laws are redesigned as saturation-compensating filters to solve the input saturation problem. Consequently, the proposed controller ensures the prescribed distributed formation performance, even in scenarios involving moving obstacles and input saturation, a capability lacking in conventional prescribed performance-based formation controllers, which can become unstable in such scenarios. The theoretical findings of this study are validated through comparative simulations.

Multi-Object Vehicle Detection and Tracking Algorithm Based on Improved YOLOv8 and ByteTrack

Multi-Object Vehicle Detection and Tracking Algorithm Based on Improved YOLOv8 and ByteTrack

Optimization and performance verification of track vehicle multi-friction pair liquid-cooled full-disc brake system

Abstract The brake system is a crucial component for enhancing the reliability and maneuverability potential of tracked armored vehicles, with a particular emphasis on improving braking performance and reliability. This paper proposes a novel multi-friction pair liquid-cooled full-disc brake system by extending the friction pair and introducing a liquid-cooling structure. Simulation and experimental results demonstrate that the proposed solution significantly accelerates the heat dissipation rate of the full-disc brake system compared to its initial version. Moreover, the presented approach increases the maximum average braking torque by 24.7% and reduces the braking time by 15.2% compared to the original version of the brake system.

Literature Review Application of YOLO Algorithm for Detection and Tracking

A vehicle tracking system is a computer program that utilizes devices to monitor the position, movement and condition of a vehicle or fleet of vehicles. Multi-vehicle tracking on highways has significant research interest and practical value in building intelligent transportation systems. Nevertheless, traffic road video frames consist of various complex backgrounds and objects. Detection and tracking are very challenging because foreground to background switching occurs frequently. One-stage algorithm approaches such as YOLO and its various variants have been proven to be accurate for detecting vehicles. Meanwhile, the SORT, DeepSORT, ByteTrack and other algorithms can be combined in YOLO. The aim of this study is to highlight existing research on the application of YOLO and its variants in detecting and tracking vehicles, especially in traffic management. The journals used are limited to 2019 – 2024 and the journal sources consist of Hindawi, IEEE, MDPI, Research Gate, Science Direct, and Springer. Based on the research that has been reviewed, the YOLO variant algorithm approach has been successfully applied in the field of vehicle monitoring to support smart cities. In addition, many new model combinations and improvements have been proposed, proving that this algorithm has a big influence in the field of computer vision.

Proposing an Efficient Deep Learning Algorithm Based on Segment Anything Model for Detection and Tracking of Vehicles through Uncalibrated Urban Traffic Surveillance Cameras

In this study, we present a novel approach leveraging the segment anything model (SAM) for the efficient detection and tracking of vehicles in urban traffic surveillance systems by utilizing uncalibrated low-resolution highway cameras. This research addresses the critical need for accurate vehicle monitoring in intelligent transportation systems (ITS) and smart city infrastructure. Traditional methods often struggle with the variability and complexity of urban environments, leading to suboptimal performance. Our approach harnesses the power of SAM, an advanced deep learning-based image segmentation algorithm, to significantly enhance the detection accuracy and tracking robustness. Through extensive testing and evaluation on two datasets of 511 highway cameras from Quebec, Canada and NVIDIA AI City Challenge Track 1, our algorithm achieved exceptional performance metrics including a precision of 89.68%, a recall of 97.87%, and an F1-score of 93.60%. These results represent a substantial improvement over existing state-of-the-art methods such as the YOLO version 8 algorithm, single shot detector (SSD), region-based convolutional neural network (RCNN). This advancement not only highlights the potential of SAM in real-time vehicle detection and tracking applications, but also underscores its capability to handle the diverse and dynamic conditions of urban traffic scenes. The implementation of this technology can lead to improved traffic management, reduced congestion, and enhanced urban mobility, making it a valuable tool for modern smart cities. The outcomes of this research pave the way for future advancements in remote sensing and photogrammetry, particularly in the realm of urban traffic surveillance and management.

Mapping off-road tracks and animal paths in protected areas using high-resolution GeoEye-1 panchromatic satellite imagery

ABSTRACT Off-road driving activities are common in many protected areas. This study aimed at applying a curvelet-based approach for the automatic extraction, mapping and separation of off-road tracks and animal paths in Masai Mara National Reserve, Kenya, using high-resolution GeoEye-1 panchromatic satellite imagery (50 cm). A novel hybrid remote sensing-GIS method comprising three main blocks is proposed: (1) extracting the high-contrast curvilinear feature from curvelet magnitudes derived from the finer scale of curvelet coefficient; (2) extracting the low-contrast curvilinear feature from coarser scale curvelets and refine the shape by deformable active contour (Snake), and (3) categorizing the extracted curvilinear feature into vehicle tracks and animal paths by a fuzzy logic inference system. Results from quantification of extraction and categorization performance of the trails were 77.5% for completeness, 89.2% for correctness, and 4.5% for redundancy, with an overall accuracy of 79.5%. Using grid matching, we find a high correlation between off-road vehicle tracks and animal paths in the area (r = 0.75, p < 0.05) indicating co-occurrence of these two types of trails. The proposed approach provides a basis for large-scale mapping and monitoring of off-road tracks and animal paths in the African savanna from space.

Fixed-time formation tracking for unmanned surface vehicles: A multi-layer neural networks approach

This paper investigates the distributed fixed-time formation problem of unmanned surface vehicles (USVs) in the presence of external disturbances, model uncertainties, input saturation and quantization constraints. To deal with the problem, a fixed-time sliding-mode control algorithm is proposed, where multi-layer neural networks (MNNs) are designed to approximate the unknown dynamics and composite disturbances of the system. The proposed MNNs combine the advantages of fuzzy neural networks (FNNs) and radial basis function neural networks (RBFNNs), exhibiting robust dynamic characteristics. Furthermore, the non-singular fast terminal sliding mode (NFTSM) is integrated into the fixed-time control framework to improve the robustness and speed of convergence for uncertain USV systems. Comparative simulations conducted with USVs demonstrate the superiority and effectiveness of the proposed algorithm.