Vehicle Position Research Articles

A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles

Real-time integrity monitoring (IM) is essential for autonomous vehicle positioning, requiring high availability and manageable computational load. This research proposes using precise point positioning real-time kinematic (PPP-RTK) as the positioning method, combined with an improved classification adaptive Kalman filter (CAKF) for processing. PPP-RTK enhances IM availability by allowing undifferenced and uncombined observations, enabling individual observation exclusion during fault detection and exclusion (FDE). The CAKF reduces FDE computational load by using a robustness test instead of traditional FDE methods, improving precision and availability in protection level estimation. Epoch-wise weighting adjustments in the robustness test create a more accurate stochastic model, aided by an adaptive unit weight variance (UWV) calculated with a sliding window, achieving a 7–28% UWV reduction. Three test scenarios with up to four simultaneous faults in code and phase observations, ranging from 1 to 200 m and 0.4 to 20 m, respectively, demonstrated successful identification and de-weighting of faults, resulting in maximum positioning errors of 6 mm (horizontal) and 11 mm (vertical). The method reduced FDE computational load by 50–99.999% compared to other approaches.

Mechatronics Application and Performance Analysis in Intelligent Transportation Systems

This paper presents a comprehensive analysis of the integration of mechatronics in intelligent transportation systems (ITS), elucidating its functional capabilities and limitations. As urbanization accelerates and traffic demand rises, the ITS has emerged as vital solutions for addressing traffic congestion and safety issues. In this regard, mechatronics technology plays a key role by enhancing the intelligence of traffic management, thereby improving the overall efficiency and safety of transportation networks. Thus, the paper provides a detailed analysis of the mechatronics application in intelligent traffic signal control, road condition monitoring and management, and vehicle positioning and navigation. In the domain of intelligent traffic signal control, mechatronics technology facilitates adaptive signal adjustments through real-time data collection and analysis, effectively alleviating traffic congestion and enhancing road capacity. In the field of road condition monitoring and management, this technology combines advanced sensors and monitoring equipment to realize accurate identification and dynamic monitoring of vehicles, providing powerful support for traffic management and law enforcement. In addition, the use of mechatronics technology in critical areas such as vehicle positioning and navigation markedly enhances road utilization, effectively mitigates traffic congestion, as well as supports sustainable urban development initiatives. Meanwhile, the performance of mechatronics in ITS, including real-time, intelligence, safety and reliability, is analyzed, highlighting its remarkable effect in enhancing the overall performance of ITS.

Gravity-aided navigation using Viterbi map matching algorithm

Abstract In Global Navigation Satellite Systems (GNSS)-denied environments, aiding a vehicle's inertial navigation system (INS) is crucial to reducing the accumulated navigation drift caused by sensor errors (e.g. bias and noise). One potential solution is to use measurements of gravity as an aiding source. The measurements are matched to a geo-referenced map of Earth's gravity to estimate the vehicle's position. In this paper, we propose a novel formulation of the map matching problem using a hidden Markov model (HMM). Specifically, we treat the spatial cells of the map as the hidden states of the HMM and present a Viterbi style algorithm to estimate the most likely sequence of states, i.e. most likely sequence of vehicle positions, that results in the sequence of observed gravity measurements. Using a realistic gravity map, we demonstrate the accuracy of our Viterbi map matching algorithm in a navigation scenario and illustrate its robustness compared with existing methods.

Research on Positioning and Tracking Method of Intelligent Mine Car in Underground Mine Based on YOLOv5 Algorithm and Laser Sensor Fusion

Precise positioning has become a key technology in the intelligent development of underground mines. To improve the positioning accuracy of mining vehicles, this paper proposes an intelligent underground mining vehicle positioning and tracking method based on the fusion of the YOLOv5 and laser sensor technology. The system utilizes a camera and the YOLOv5 algorithm for real-time identification and precise tracking of mining vehicles, while the laser sensor is used to accurately measure the straight-line distance between the vehicle and the positioning device. By combining the strengths of both vision and laser sensors, the system can efficiently identify mining vehicles in complex environments and accurately calculate their position using geometric principles based on laser distance measurements. Experimental results show that the YOLOv5 algorithm can efficiently identify and track mining vehicles in real time. When integrated with the laser sensor’s distance measurement, the system achieves high-precision positioning, with horizontal and vertical positioning errors of 1.66 cm and 1.96 cm, respectively, achieving centimeter-level accuracy overall. This system significantly improves the accuracy and real-time performance of mining vehicle positioning, effectively reducing operational errors and safety risks, providing essential technical support for the intelligent development of underground mining transportation systems.

Analysis of the transient multiphase structure and motion characteristics of projectiles launched successively

A cavitation flow can greatly impact a vehicle's attitude and stability when exiting water. This paper adopts an improved delayed detached eddy turbulence model and a Schnerr–Sauer cavitation model as well as the volume-of-fluid method and an overlapping grid technique to investigate this effect. In addition, the experimental system of the underwater launch is designed and built independently, which the numerical results are in good agreement with the experimental results. The transient cavitation flow structure and motion characteristics of the projectiles successively launched underwater are studied. When the axial spacing ranges from 0 to 1.0 times the diameter of the projectile, both projectiles are severely affected to various extents in cavitation pattern, vortex structure, and motion characteristics. It is worth noting that the internal cavity of the secondary projectile is disturbed by the wake of the primary projectile, resulting in large-scale fractures and detachment of the internal cavity, but its motion stability is good.

Cooperative navigation using constrained convex generators

In this article, we suggest a technique of set-based cooperative navigation for a fleet of vehicles under communication constraints. In the suggested approach, only the distances and bearings to other vehicles or known locations can be measured. Each vehicle in the fleet must determine its location and the positions of the other vehicles based on the measurements as well as information shared among the vehicles. Since the measurement set may be nonconvex, it must be approximated by a convex set. To address this issue, we employ constrained convex generators, which is a generalization of the definition for constrained zonotopes that allows ℓ2 unit norm balls and convex cones. To keep the amount of exchanged information low the vehicles transmit an ellipse containing the state estimate to the other vehicles. The obtained estimates are worst-case bounds for the true position, which is important in certain applications such as collision avoidance. The computed sets are then applied to vehicle control algorithms, taking into account the positions of all the agents. Through numerical simulations, we illustrate the application of this method to the problem of cooperative navigation of autonomous underwater vehicles and the problem of fire detection with multiple UAVs.

Fuzzy Optimal Event-Triggered Control for Dynamic Positioning of Unmanned Surface Vehicle

Fuzzy Optimal Event-Triggered Control for Dynamic Positioning of Unmanned Surface Vehicle

BOSVDD-Based GNSS Spoofing Detection for Rail Vehicle Positioning

BOSVDD-Based GNSS Spoofing Detection for Rail Vehicle Positioning

Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting Coordination

This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to optimize traffic signal control, reduce congestion, and enhance safety. Utilizing existing road infrastructure, VLC technology transmits real-time data on vehicle and pedestrian positions, speeds, and queues. AI agents, powered by Deep Reinforcement Learning (DRL), process these data to manage traffic flows dynamically, applying anti-bottlenecking and rerouting techniques. A global agent coordinates local agents, enabling indirect communication and a unified DRL model that adjusts traffic light phases in real time using a queue/request/response system. A key focus of this work is the design of reward functions for standard and rerouting scenarios. In standard scenarios, the reward function prioritizes wide green bands for vehicles while penalizing pedestrian rule violations, balancing efficiency and safety. In rerouting scenarios, it dynamically prevents queuing spillovers at neighboring intersections, mitigating cascading congestion and ensuring safe, timely pedestrian crossings. Simulation experiments in the SUMO urban mobility simulator and real-world trials validate the system across diverse intersection types, including four-way crossings, T-intersections, and roundabouts. Results show significant reductions in vehicle and pedestrian waiting times, particularly in rerouting scenarios, demonstrating the system’s scalability and adaptability. By integrating VLC technology and AI-driven adaptive control, this approach achieves efficient, safe, and flexible traffic management. The proposed system addresses urban mobility challenges effectively, offering a robust solution to modern traffic demands while improving the travel experience for all road users.

Analysis on the Two Core Technologies of Current Intelligent Driving

With the development of the automobile industry, intelligent driving will become the mainstream, it is an important embodiment of China's development, at the same time, it is also an important embodiment of the liberation of automobile productivity, which marks a major reform in the automobile industry. Intelligent driving this advanced technology in the vehicle identification technology and image processing technology will pass the road condition to the driving computer, positioning technology lists the vehicle position, speed and other information, the two complement each other, and promote the safe operation of the vehicle. This paper will analyze the core elements of intelligent driving from the two aspects of vehicle identification technology and positioning technology, and scientifically present the advanced, convenient, safe and reliable features of intelligent driving.

Enhancing Vehicle Location Prediction Accuracy with Road-Aware Rectification for Multi-Access Edge Computing Applications

In future 6G networks, real-time and accurate vehicular data are key requirements for enhancing the data-driven multi-access edge computing (MEC) applications. Existing estimation techniques to forecast vehicle position aim to meet the real-time data needs but compromise accuracy due to a lack of context awareness. While algorithms such as the Kalman filter improve estimation accuracy by considering certainty-grading and current-state estimate of measurements, they do not include the road context, which is vital for more accurate predictions. Unfortunately, current implementations of linear Kalman filters are not road-aware and struggle to predict a two-dimensional movement accurately. To this end, we propose a significant road-aware rectification-assisted prediction mechanism that enhances the modified Kalman filter predictions by incorporating road awareness. The parameters used for the Kalman filter include vehicle location, angle, speed, and time. In contrast, road-aware location rectification incorporates predicted location and lane shape, increasing the accuracy and precision of vehicle location predictions, reaching up to 99.9%. Performance is evaluated by comparing actual, predicted, and rectified vehicular traces at different speeds. The results demonstrate that the prediction error is approximately 0.005, while the proposed rectification process further reduces the error to 0.001, highlighting the effectiveness of the proposed approach. Overall, results support the idea of provisioning accurate, proactive, and real-time vehicular location data at the edge using a road-aware approach, thereby revolutionizing 6G vehicle location provisioning in MEC.

Aerodynamic characteristics of the race car in pitch and roll attitude

Purpose Aerodynamics plays a crucial role in enhancing the performance of race cars. Due to the low ride height, the aerodynamic components of race cars are affected by ground effects. The changes in pitch and roll attitudes during the car’s movement impact its ride height. This study aims to analyze the aerodynamic characteristics of race cars under specific pitch and roll attitudes to understand the underlying aerodynamic mechanisms. This paper focuses on the aerodynamic characteristics of racing cars under variations in body posture associated with different vehicle ride heights. It examines not only the force and pressure distribution resulting from changes in the overall vehicle posture but also the flow field behavior of both surface flow and off‑body flow. Analyzing individual components reveals the impact of the front wing on the overall aerodynamic performance and aerodynamic balance of the racing car under these posture variations. Design/methodology/approach The grid strategy for the computational fluid dynamics (CFD) method was established under baseline conditions and compared with the results from wind tunnel experiments. The CFD approach was further employed to investigate the aerodynamic characteristics of the racing car under varying body postures associated with different vehicle ride heights. Emphasis is placed on the overall aerodynamic performance of the vehicle and the various components’ influence on the changing trends of aerodynamic forces. By considering the surface pressure distribution of the car, the primary reasons behind the changes in aerodynamic forces for each component are investigated. In addition, the surface flow and detached flow (wake and vortex distributions) of the car were observed to gain insights into the overall flow field behavior under different attitudes. Findings The findings indicate that both pitch and roll attitudes result in a considerable loss of downforce on the front wing compared with other components, thereby affecting the overall downforce and aerodynamic balance of the vehicle. Originality/value This paper focuses on the aerodynamic characteristics of racing cars under variations in body posture associated with different vehicle ride heights. It examines not only the force and pressure distribution resulting from changes in the overall vehicle posture but also the flow field behavior of both surface flow and off-body flow. Analyzing individual components reveals the impact of the front wing on the overall aerodynamic performance and aerodynamic balance of the racing car under these posture variations.

An Elastic Filtering Algorithm with Visual Perception for Vehicle GNSS Navigation and Positioning.

Amidst the backdrop of the profound synergy between navigation and visual perception, there is an urgent demand for accurate real-time vehicle positioning in urban environments. However, the existing global navigation satellite system (GNSS) algorithms based on Kalman filters fall short of precision. In response, we introduce an elastic filtering algorithm with visual perception for vehicle GNSS navigation and positioning. Firstly, the visual perception system captures real-time environmental data around the vehicle. It utilizes the interframe differential optical flow method and vehicle state switching characteristics to assess the current driving status. Secondly, we design an elastic filtering model specifically for various vehicle states. This model enhances the precision of Kalman filter-based GNSS navigation. In urban driving, vehicles often experience frequent stationary parking. To address this, we incorporate a zero-speed constraint to further refine vehicle location data when the vehicle is stationary. This constraint matches the data with the appropriate elastic filtering model. Ultimately, we conduct simulation and real-world vehicle navigation experiments to confirm the validity and rationality of our proposed algorithm. Compared with the conventional algorithm and the existing interactive multi-model algorithm, the proposed algorithm significantly improves the navigation and positioning accuracy of vehicle GNSS in urban environments. Compared to the commonly used constant acceleration (CA) and Constant Velocity (CV) models, there has been a significant improvement in positioning accuracy. Furthermore, when benchmarked against the more advanced interactive multi-model (IMM) model, the method proposed in this paper has enhanced the positioning accuracy enhancements in three dimensions: 21.8%, 20.9%, and 31.3%, respectively.

Limiting Performance of Radar-Based Positioning Solutions for the Automotive Scenario.

Road safety applications for automotive scenarios rely on the ability to estimate vehicle positions with high precision. Global navigation satellite systems (GNSS) and, in particular, the global positioning system (GPS), are commonly used for self localization. But, especially in urban vehicular scenarios, due to obstructions, they may not provide the requirements for crucial position-based applications. In this paper, we investigate the potential of GPS-free positioning schemes and, in particular, we compute the ultimate performance, i.e., Cramér-Rao lower bounds (CRLB), of localization schemes in which each vehicle estimates its position exploiting range and/or angle measurements of an assigned set of landmarks with a known position.

Vehicle Positioning Based on BeiDou Satellite Navigation System

This paper is mainly based on the BeiDou Satellite Navigation System (BDS) to solve the application of vehicle positioning. First, the paper illustrates the locating theory of BDS. Second, it analyzes the advantages of BDS based on the theory. Next, the paper emphasizes the application of BDS in vehicle positioning, which includes the application of real-time positioning combined with Global Positioning System (GPS), the collection of live traffic information and planning or choosing the best route. Finally, the paper includes the whole article and makes hope for the future.

Optimizing Sampling Points and Path Planning for Soil Monitoring in Agricultural Land

Current soil mixing sampling surveys face several challenges. These challenges include significant variations in the size and shape of farmland plots, which complicate the sampling process. Additionally, the procedures for establishing mixing points are often cumbersome and non-standardized. Furthermore, when the number of mixing points is large and the plot area is extensive, sampling efficiency is significantly reduced. In this study, an automated method for establishing the layout of sampling points and a threshold-based mixed path planning algorithm are proposed. These approaches are designed to automatically arrange sampling points within delineated sampling units according to various sampling methods. Additionally, real-time path planning is conducted on the basis of the initial position of the sampling vehicle. The experimental results indicate that the maximum relative error in estimating farmland area is 0.22%, which is less than 666.67 m2. The average deviation between the locations of the sampling points generated by the sampling point layout algorithm and those established via related software is 0.0336 m. Furthermore, the threshold-based hybrid algorithm consistently yields optimal or near-optimal paths while reducing the path planning time, with an average duration of 1.0783 s for path planning. This study provides reliable technical support for standardizing the sampling point layout and enhancing the soil sampling efficiency.

Integration of Visible Light Communication, Artificial Intelligence, and Rerouting Strategies for Enhanced Urban Traffic Management

This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to enhance traffic signal control, reduce congestion, and improve safety, through real-time monitoring and dynamic traffic management. Leveraging VLC technology, the system uses existing road infrastructure to transmit live data on vehicle and pedestrian positions, speeds, and queues. AI agents, employing Deep Reinforcement Learning (DRL), process this data to manage traffic flows dynamically, applying anti-bottleneck and rerouting techniques to balance pedestrian and vehicle waiting times. A centralized global agent coordinates the local agents controlling each intersection, enabling indirect communication and data sharing to train a unified DRL model. This model makes real-time adjustments to traffic light phases, utilizing a queue/request/response system for adaptive intersection management. Tested using simulations and real-world trials involving standard and rerouting scenarios, the approach demonstrates significantly better performance in regard to the rerouting configuration, reducing congestion and enhancing traffic flow and pedestrian safety. Scalable and adaptable to various intersection types, including four-way, T-intersections, and roundabouts, the system’s efficacy is validated using the SUMO urban mobility simulator, resulting in notable reductions to travel and waiting times for both vehicles and pedestrians.

A positioning method for AUV based on guiding light source

This paper proposes a method for determining the position of underwater vehicles using light vision guidance. In order to solve the problem of limited perception distance and unclear underwater images, the illumination device is installed on the recovery platform as a light source to enhance the feature information of the recovery platform. The collected underwater light source images are enhanced by the dark channel prior defogging algorithm, and then the YOLOv5 algorithm is used to recognize the targets. Through experiments, it is verified that the image enhancement algorithm and target recognition algorithm are effective. Using the recognition results, the positioning accuracy of the binocular camera ranging algorithm is verified, and the results indicate that the proposed method can accurately determine the distance of the AUV relative to the recovery platform.

Noise assessment of multirotor configurations during landing proceduresa).

This study numerically investigates the noise impact of multirotor aerial vehicles with different rotor scales during landing procedures. The operational environments of individual rotors are influenced by rotational speed and wake dynamics, leading to variations in landing noise characteristics. Noise impacts are evaluated across various landing operations from both physical and psychoacoustic perspectives using noise source hemispheres and noise maps. The physical noise impact is quantified using sound exposure level (SEL), while the psychoacoustic impact is assessed through a psychoacoustic annoyance (PA) based on sound quality metrics. Performance contours are established to compare noise impacts alongside other factors, such as energy consumption, landing duration, vehicle attitudes, and safety considerations. The combined effect of noise source strength and landing duration determines SEL, while PA is primarily influenced by acoustic loudness, which follows a similar trend to noise source strength. Consequently, physical and psychoacoustic noise impacts exhibit distinct trends based on the landing operations. This study outlines a process for optimizing landing operations that meet predefined performance goals while minimizing noise impacts. Because operational performance varies significantly across different landing procedures and vehicle types, the study emphasizes the importance of incorporating comprehensive performance criteria in the design of landing operations.

Research on intelligent computing model of aerodynamic fusion 6-DOF trajectory and attitude of low-orbit vehicle

Research on intelligent computing model of aerodynamic fusion 6-DOF trajectory and attitude of low-orbit vehicle