Vehicle Position Research Articles

Vehicle positioning systems in tunnel environments: a review

Real-time, accurate, and robust positioning system plays a crucial role in many vehicular applications for automatic driving system and Vehicular Ad-hoc Network (VANET). In the tunnel, the positioning accuracy of Global Navigation Satellite System (GNSS) decreases due to blocked satellite signals. In order to estimate the exact location of vehicles in tunnel environments, many positioning systems have been presented. However, there is a lack of effort in systematically comparing, organizing and analyzing these existing positioning systems, and identifying the strengths and weaknesses of different technologies and applicable scenarios. Therefore, this paper undertakes a thorough investigation into current vehicle localization technologies and methods for tunnel scenarios. The analysis starts with discussing various application scenarios for vehicle positioning system. Then, various vehicle positioning technologies are investigated, the advantages and drawbacks of each technology are illustrated. Thereafter, we discuss some widely used positioning methods in terms of range-based localization method, range-free localization method, multi-sensor fusion localization method, and cooperative positioning (CP) method. Finally, we discuss some challenges faced in vehicle positioning for tunnel environments, and propose some potential research topics for future research work.

Source Seeking With a Nonholonomic Vehicle in Static and Dynamic Spatial Fields

ABSTRACTIn this paper, a novel source seeking strategy is presented. A single autonomous vehicle is modeled as a kinematic unicycle moving in two‐dimensional space within a spatial field. The proposed control scheme allows the vehicle to reach a neighborhood of the source of the spatial field in finite time. In this work, the spatial field can be static, translating or dynamically evolving. The control strategy, based on the definition of a reference trajectory and on a second order sliding mode extremum seeking approach, exploits only a point measurement at the vehicle position, and does not require knowledge or an estimate of the spatial gradient of the phenomenon of interest. Explicit bounds on the reference trajectory parameters are introduced in order to guarantee finite time convergence to a neighborhood of the source. The approach is tested in simulation on static and dynamic spatial fields.

Node Selection and Path Optimization for Passive Target Localization via UAVs

The performance of passive target localization is affected by the positions of unmanned aerial vehicles (UAVs) at a large scale. In this paper, to improve resource utilization efficiency and localization accuracy, the node selection problem and the path optimization problem are jointly investigated. Firstly, the target passive localization model is established and the Chan-based time difference of arrival (TDOA) localization method is introduced. Then, the Cramer–Rao lower bound (CRLB) for Chan-TDOA localization is derived, and the problems of node selection and path optimization are formulated. Secondly, a CRLB-based node selection method is proposed to properly divide the UAVs into several groups, localizing different targets, and a CRLB-based path optimization method is proposed to search for the optimal UAV position configuration at each time step. The proposed path optimization method also effectively handles no-fly-zone (NFZ) constraints, ensuring operational safety while maintaining optimal target tracking performance. Also, to improve the efficiency of path optimization, particle swarm algorithm (PSO) is applied to accelerate the searching process. Finally, numerical simulations are performed to verify the validity and effectiveness of the proposed methods in this paper.

IC-GLI: a real-time, INS-centric GNSS-LiDAR-IMU localization system for intelligent vehicles

Abstract The integration of multiple sensors plays a vital role in achieving accurate vehicle positioning. However, light detection and ranging (LIDAR) and global navigation satellite system (GNSS) are susceptible to environmental effects, unlike inertial navigation system (INS), which remains unaffected by external factors. To enhance the precise positioning of intelligent vehicles in diverse environments and maximize the use of inertial measurement unit (IMU) data, this study presents a LIDAR, IMU, and GNSS-based positioning method centered around INS. The proposed method employs an IMU pre-integration model that includes the Earth’s rotation and utilizes a factor graph optimization framework to fuse the measurement data from LIDAR, IMU, and GNSS. By assigning appropriate weights to the residuals and dynamically adjusting the contributions of each sensor based on the prevailing environmental conditions, the optimal localization results are obtained through the solution of the maximum a posteriori estimate and the update of the INS state. Finally, the proposed system’s localization performance is validated using intelligent vehicles in the Robot Operating System framework. The results demonstrate that the system achieves high accuracy and robustness across various environments.

Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN

To improve intersection traffic flow and reduce vehicle energy consumption and emissions at intersections, a signal optimization method based on deep reinforcement learning (DRL) is proposed. The algorithm uses Rainbow DQN as the core framework, incorporating vehicle position, speed, and acceleration information into the state space. The reward function simultaneously considers two objectives: reducing vehicle waiting times and minimizing carbon emissions, with the vehicle queue length as a weighted factor. Additionally, an ACmix module, which integrates self-attention mechanisms and convolutional layers, is introduced to enhance the model’s feature extraction and information representation capabilities, improving computational efficiency. The model is tested using an actual intersection as the study object, with a signal intersection simulation built in SUMO. The proposed approach is compared with traditional Webster signal timing, actuated signal timing, and control strategies based on DQN and D3QN models. The results show that the proposed strategy, through real-time signal timing adjustments, reduces the average vehicle waiting time by approximately 27.58% and the average CO2 emissions by about 7.34% compared with the actuated signal timing method. A comparison with DQN and D3QN models further demonstrates the superiority of the proposed model, achieving a 15% reduction in average waiting time and a 6.5% reduction in CO2 emissions. The model’s applicability is validated under various scenarios, including different proportions of electric vehicles and traffic volumes. This study aims to provide a flexible signal control strategy to enhance intersection vehicle flow and reduce carbon emissions. It offers a reference for the development of green, intelligent transportation systems and holds practical significance for promoting urban carbon reduction efforts.

Development prediction algorithm of vehicle travel time based traffic data

This work is based on encouraging a generous and conceivable estimation for modified an algorithm for vehicle travel times on a highway from the eliminated traffic information using set aside camera image groupings. The strategy for the assessment of vehicle travel times relies upon the distinctive verification of traffic state. The vehicle velocities are gotten from acknowledged vehicle positions in two persistent images by working out the distance covered all through elapsed past time doing mollification between the removed traffic flow data and cultivating a plan to unequivocally predict vehicle travel times. Erbil road data base is used to recognize road locales around road segments which are projected into the commended camera imag-es and later distinguished vehicles are assigned to the looking at route segment so instantaneous and current velocities are calculated. All data were effectively processed and visualized using both MATLAB and Py-thon programming language and its libraries.

Strengthening Road Safety and Mobility at the Urban Level with the Aim of Digitizing and Shaping Smart Cities Through Emerging Vehicular Communications C-V2X, DSRC, and VLC

The simulation results presented based on the proposed system demonstrated significant improvements in communication reliability, packet loss reduction and signal stability, highlighting its superiority in real urban traffic conditions. Using the IEEE 802.11p standard and a modular dual-antenna architecture, the system maintained a latency below 10 ms over distances of over 3 km, without noticeable signal loss. GNSS synchronization ensured precise vehicle positioning and dynamic signal optimization. There are results and approaches that highlight the limitations of IEEE 802.11p in dense traffic scenarios; the current approach has reduced packet loss to below 5%. Its integration also allows compatibility with future technologies such as 5G and C-V2X, guaranteeing scalability and long-term relevance. The proposed prototype sets a new standard in vehicular communications, combining high performance with a flexible and extensible architecture, making it a viable solution for large-scale deployments in smart cities, supporting the transition to safer and more sustainable transportation infrastructures.

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

PurposeAerodynamics 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/approachThe 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.FindingsThe 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/valueThis 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.