Car Navigation Research Articles

Detecting GNSS spoofing and Re-localization on UAV based on imagery matching

Abstract Due to rapid advancements in global navigation satellite system (GNSS), computer, and microelectronics technologies, there is a growing popularity and widespread promotion of high-performance, cost-effective intelligent UAVs across various applications. Recently, GNSS spoofing attacks have emerged as a significant obstacle hindering the long-term development of UAVs. UAVs rely heavily on unprotected GNSS signals for navigation, making them highly vulnerable to spoofing. This paper presents an algorithm that employs image matching for detecting GNSS spoofing and re-localization on UAVs using a deep learning methodology. This method functions autonomously solely reliance on camera and does not require alterations to the antenna configuration. Utilizing a camera-equipped UAV, we evaluate the likeness between real-time aerial photographs and satellite imagery by leveraging the position information provided by UAV. By identifying disparities between images taken by a spoofing affected drone and authentic ones, the spoofing can be identified using deep neural network models. Upon detecting spoofing, this paper presents a vision-based re-localization method for UAVs. Experimental results demonstrate an approximately 88.4% accuracy of our model in detecting GNSS spoofing attacks within 100 ms and 88% success rate of re-localization with the accuracy of less than 15 m. Our algorithm exclusively depends on publicly accessible satellite imagery, offering an intelligent and efficient approach for detecting UAV GNSS spoofing and re-localization in GNSS denied environment.

Counterfactual-based two-stage trajectory prediction framework for autonomous vehicle navigation systems

Abstract Accurate prediction of a vehicle’s trajectory is essential for autonomous driving and robotic operations in a complex, dynamic environment. However, current methods that rely on historical and environmental cues to predict future trajectories make the inner distance between testing and training environments often overlooked. To address this issue, we propose a counterfactual analysis method specifically designed for vehicle trajectory prediction. We begin by constructing a causal graph that includes historical trajectories, future trajectories, and environmental interactions. This graph captures the causal relationships between these elements. Our approach directly intervenes on the trajectory itself, effectively cutting off the inference from the environment to the trajectory. By doing so, we mitigate the negative impact of environmental bias. Furthermore, by comparing observed and intervened trajectory clues, we highlight the influence of environmental distance. Our goal is to improve the accuracy of trajectory prediction. Our counterfactual analysis module integrates seamlessly into various baseline prediction models. Experimental data demonstrates that our method outperforms others on publicly available Argoverse2 motion forecasting benchmarks.

A Robust Factor Graph Optimization Method of GNSS/INS/ODO Integrated Navigation System for Autonomous Vehicle

Abstract The GNSS/INS/ODO integrated navigation system provides accurate positioning for autonomous vehicles. However, the observation quality of GNSS and ODO can decrease in complex environments and various driving conditions, leading to a decline in the accuracy and robustness of the integrated navigation system. An FGO-Robust method is proposed to resolve this challenge by fusing the INS/ODO-Slip factor and adaptive GNSS factor. The INS/ODO-Slip factor compensates for ODO measurement values by estimating the wheel slip ratio in real-time during optimization, thereby reducing ODO measurement errors' impact on factor graph optimization. Additionally, the adaptive GNSS factor minimizes the impact of GNSS outliers in the optimization process by designing a dynamic observation weight function. The vehicle experiments are conducted to evaluate the performance of the proposed method in different simulated conditions with ODO and GNSS measurement failures. The experimental results demonstrate that the FGO-Robust method can effectively improve the positioning accuracy of the integrated navigation system under various measurement failure conditions, with improvements of 83.4%, 66.1%, and 68.1% relative to FGO. Moreover, the FGO-Robust method improves the robustness of the integrated navigation system in complex environments and various driving conditions.

An Automatic Vehicle Navigation System Based on Filters Integrating Inertial Navigation and Global Positioning Systems

The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system.

Multimodal Image-Based Indoor Localization with Machine Learning-A Systematic Review.

Outdoor positioning has become a ubiquitous technology, leading to the proliferation of many location-based services such as automotive navigation and asset tracking. Meanwhile, indoor positioning is an emerging technology with many potential applications. Researchers are continuously working towards improving its accuracy, and one general approach to achieve this goal includes using machine learning to combine input data from multiple available sources, such as camera imagery. For this active research area, we conduct a systematic literature review and identify around 40 relevant research papers. We analyze contributions describing indoor positioning methods based on multimodal data, which involves combinations of images with motion sensors, radio interfaces, and LiDARs. The conducted survey allows us to draw conclusions regarding the open research areas and outline the potential future evolution of multimodal indoor positioning.

Development of autonomous navigation system based on neural network and visual servoing for row-crop tracking in vegetable greenhouses

Accurate navigation line localization strategy and robust path tracking control scheme are two key techniques for advancing precision agriculture and enhancing the sustainability of smart farms. In this paper, a superior and cost-effective autonomous navigation system for a visual servoing-based agricultural vehicle in the greenhouse scenario with a distinct layout of two crop rows in each ridge is presented. First, based on image segmentation techniques, a neural network model named DGLNet integrating recognition of crop rows and prediction of the effective area of the navigation line is designed. The linear equation of the navigation line we required is obtained through post-processing operations of linear fitting completed in the effective area. According to experimental results, directly teaching the neural network to learn navigation line localization features utilizes the semantic and spatial information in the feature map to establish a stronger relationship between crop rows and the navigation line, which greatly improves the generalization of the navigation line localization in the case of large-angle yaw scenarios without additional training datasets. In addition, the performance of the network is significantly enhanced by adding an attention mechanism in the backbone based on U-Net. Subsequently, a dataset of cabbage vegetable images covering a wide range of complexities in the greenhouse scenario is created, and is available to support future academic research now. Further, a path tracking control scheme based on point-feature and line-feature is proposed, which can control the agricultural vehicle using the information from camera images without the need to perform explicit localization or maintain a global map. Finally, extensive field experiments have been carried out to show that this autonomous navigation system can preserve the stability of the vehicle and ensure timely convergence of deviations in different complex scenarios, which indicates that this control scheme is reliable and robust. Meanwhile, benefit from the efficiency of this recognition algorithm and the unnecessity of the complete representation of the path in the control scheme, the system has high real-time performance in every scenario.

A Novel Algorithm for Enhancing Terrain-Aided Navigation in Autonomous Underwater Vehicles

The position error in an inertial navigation system (INS) for autonomous underwater vehicles (AUVs) increases over time. Terrain-aided navigation can assist in correcting these INS position errors. To enhance the matching accuracy under large initial position errors, an improved terrain matching algorithm comprising terrain contour matching (TERCOM), particle swarm optimization (PSO), and iterative closest contour point (ICCP), named TERCOM-PSO-ICCP, is proposed. Initially, an enhanced TERCOM with an increased rotation angle is utilized to minimize heading errors and reduce the initial position error. The similarity extremum approach evaluates the initial matching outcomes, leading to an enhanced accuracy in the initial results. Next, artificial bee colony (ABC)-optimized PSO is employed for secondary matching to further reduce the initial position error and narrow the matching area. Finally, the ICCP, using the Mahalanobis distance as the objective function, is applied for the third matching, leveraging the ICCP’s fine search capabilities. The effective combination of these three algorithms significantly improves the terrain-aided navigation matching effect. Two tests show that the improved TERCOM-PSO-ICCP effectively reduces the matching error and corrects the position of the INS.

Agricultural path detection systems using Canny-edge detection and Hough transform

Navigation is one of the crucial aspects of automation technology within the field of agriculture, such as robotics systems or autonomous agricultural vehicles. Despite many navigation systems having been developed for agricultural land, due to their high development and component costs, these systems are difficult to access for farmers or organizations with limited capital. In this study, the Canny-edge detection and Hough transform methods are implemented in a path detection system on agricultural land to find an alternative, cost-effective navigation system for autonomous farming robots or vehicles. The system is tested on ground-level view images, which are captured from a low perspective and under three different lighting conditions. The testing and experimentation process involves adjusting the parameters of the Canny-edge detection and Hough transform methods for different lighting conditions. Subsequently, an evaluation is conducted using Intersection over Union to obtain the best accuracy results, followed by fine-tuning of the canny-edge detection and Hough transform method parameters. The identified parameters, specifically a 15×15 Gaussian kernel, low threshold of 50, high threshold of 150, Hough threshold, minimum line length of 150, and maximum line gap, have been discerned as optimal for the canny-edge and Hough transform algorithms under medium lighting conditions (G=1.0). The observed efficacy of these parameter configurations suggests the method’s viability for implementation in path detection systems for agricultural vehicles or robots. This underscores its potential to deliver reliable performance and navigate seamlessly across diverse lighting scenarios within the agricultural context.

GNSS/IMU/map-matching feedback integration with adaptive GNSS accuracy estimation by using low-quality sensors for vehicle localization in urban canyon

In urban canyons, the performance of vehicle navigation systems based on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) is notably compromised due to satellite signal obstructions and severe multipath effects. This article proposes a GNSS/IMU/Map-Matching feedback (MMF) integration with the adaptive GNSS accuracy estimation to provide accurate and robust positions for vehicles in urban canyons. This system integrates self-constraints and MMF to control the drift of low- quality IMU. To avoid fatal positioning errors caused by incorrect MMF, motion states are categorized into straight-line driving and corner turning, with different criteria set based on their characteristics to validate MMF. Furthermore, GNSS accuracy is estimated adaptively with MMF. Extensive experiments in Hong Kong’s dense urban canyons demonstrated significantly improved positioning accuracy of 37% and 51% in the north and east directions, respectively, outperforming traditional GNSS/IMU/Map configurations. The results demonstrate the proposed approach’s marked enhancement in urban vehicular navigation.

User Interface and Navigation System for an Electric Vehicle for the Disabled

The purpose of this work is to link a natural user interface and an autonomous navigation system to a conventional wheelchair with two control modalities: manual and automatic. It is taken into account that not all people have the same type or degree of disability, for this reason we seek to implement a user interface that involves different forms of user-machine interaction, with which the user can choose the manual mode that allows direct commands to control the movement of the chair and the automatic mode, which only needs the point or place where you need to go and through predefined trajectories, the autonomous navigation system will execute the necessary movements for the chair to follow the desired route.

An Efficient Approach for Shortest Path Planning in Automotive Navigation System

Emerging automotive engineering solutions extensively rely on navigation tools. Shortest path evaluation is core competency of optimal path planning, one of the most critical component of any navigation application. Fibonacci heap variant of state-of-the-art Dijkstra’s algorithm solve single source shortest path problems in asymptotic O(E + V log V) worst-case time. In spite Fibonacci heap is theoretically dominant in time efficiency, it might not work much better empirically with real-world data, because although large and intricate, the real-world road network graphs are sparse and the graph topology greatly affects the performance of the algorithms. SGO algorithm, a more efficient approch to address single source shortest path problem, is applied to solve optimal path planning problem in automotive navigation applications. Each road segment of the road graph leads to a particular location thus will hold unique distance values all the time. The SGO calculates the total distance from start location to the end of the each road segment and evaluates shortest path based on these calculations. The SGO algorithm and the Fibonacci and Binary heap variants of Dijkstra’s algorithm were tested on real-world intercity and intra-city road network graphs to evaluate running time efficiency of the algorithms in automotive navigation systems. The experimental results show that the SGO algorithm outperformed both the variants. The empirical superiority of the SGO algorithm suggests its usefulness in automotive navigation systems.

Implementation of the Shortest Path Method with Excel Solver to Optimize Goods Delivery Routes

The shortest path solver is a program that aims to find the route with the lowest total edge weight between two points in a graph. Commonly used algorithms include Dijkstra for graphs with non-negative edge weights, Bellman-Ford for graphs with negative edge weights, and Floyd-Warshall for finding the shortest path between all point pairs. Its application is wide, ranging from navigation systems, computer networks, to logistics and games. The process of using it involves creating a graph model , selecting the appropriate algorithm, running a solver, and analyzing the results. A practical example shows how the Dijkstra algorithm can be used to determine the shortest route between cities in a road network , with effective and accurate results . The shortest path solver proves to be a versatile and essential tool for solving a wide range of problems in a variety of fields. This research uses quantitative methods with an experimental approach to test the effectiveness and efficiency of using the shortest path method with Solver Excel in optimizing goods delivery routes. The research object is the delivery route from the warehouse to several delivery destinations, with a sample of 10 routes that are most frequently used and have the highest delivery volume. Primary data was obtained through direct observation and interviews with company logistics managers. The results of this study show that the use of shortest path method with Excel Solver is effective in optimising the route of delivery of goods, reducing the cost and time of delivery by 15% and 10%. Although there are limitations for large networks, this tool remains useful and flexible. This implementation can be a reference for other companies to improve their logistics efficiency.

Aggregation operators based on Einstein averaging under q-spherical fuzzy rough sets and their applications in navigation systems for automatic cars

This study introduces innovative operational laws, Einstein operations, and novel aggregation algorithms tailored for handling q-spherical fuzzy rough data. The research article presents three newly designed arithmetic averaging operators: q-spherical fuzzy rough Einstein weighted averaging, q-spherical fuzzy rough Einstein ordered weighted averaging, and q-spherical fuzzy rough Einstein hybrid weighted averaging. These operators are meticulously crafted to enhance precision and accuracy in arithmetic averaging. By thoroughly examining their characteristics and interrelations with existing aggregate operators, the article uncovers their distinct advantages and innovative contributions to the field. Furthermore, the study illustrates the actual implementation of these newly constructed operators in a variety of attribute decision-making scenarios employing q-SFR data, yielding useful insights. Our suite of decision-making tools, including these operators, is specifically designed to address complex and uncertain data. To validate our approach, this study offers a numerical example showcasing the real-world applicability of the proposed operators. The results not only corroborate the efficacy of the proposed method but also underscore its potential significance in practical decision-making processes dealing with intricate and ambiguous data. Additionally, comparative and sensitivity analyses are presented to assess the effectiveness and robustness of our proposed work relative to other approaches. The acquired knowledge enriches the current understanding and opens new avenues for future research.

Cruise Speed Model Based on Self-Attention Mechanism for Autonomous Underwater Vehicle Navigation

This study proposes a cruise speed model based on the Self-Attention mechanism for speed estimation in Autonomous Underwater Vehicle (AUV) navigation systems. By utilizing variables such as acceleration, angle, angular velocity, and propeller speed as inputs, the Self-Attention mechanism is constructed using Long Short-Term Memory (LSTM) for handling the above information, enhancing the model’s accuracy during persistent bottom-track velocity failures. Additionally, this study introduces the water-track velocity information to enhance the generalization capability of the network and improve its speed estimation accuracy. The sea trial experiment results indicate that compared to traditional methods, this model demonstrates higher accuracy and reliability with both position error and velocity error analysis when the used Pathfinder DVL fails, providing an effective solution for AUV combined navigation systems.

VisioSignNet: A Dual-Interactive Neural Network for enhanced traffic sign detection

Effective traffic sign detection is crucial for the safety and operational efficiency of autonomous vehicle navigation systems, particularly in dynamically changing environments. Addressing the primary challenges of long-range pixel dependencies and enhancing the detectability of small objects in complex scenes, we present VisioSignNet: A Dual-Interactive Neural Network designed for enhanced traffic sign detection. This architecture incorporates Local and Global Interactive Modules (LGIM) and Enhancing Channel and Space Interaction (ECSI) modules. The LGIM is engineered to balance local and global feature interactions, while the ECSI optimizes the interchange of information across channel and spatial dimensions. Their synergistic interaction not only enhances the perceptual field at early processing stages but also significantly improves the recognition of small-scale, critical traffic signs. Evaluated on the TT100K and GTSDB datasets, VisioSignNet achieved mean average precision (mAP) scores of 90.5% and 97%, respectively, with a model size of 26M parameters. Its enhanced variant, VisioSignNet_l, with 34M parameters, reached mAP scores of 93.2% and 97.8%. These outcomes substantiate VisioSignNet’s efficacy in tackling the complexities of traffic sign detection, confirming its potential as a robust solution in the field of autonomous driving technologies.

Enhancing Vehicle Navigation and Safety through Integration of Pre-Recorded Maps with Vehicle Control Unit

Powerful navigation and safety systems are vital to ensuring autonomous (DV) or semi-autonomous vehicles continue well in varied environmental conditions. However, in such a world those who depend on that internet connectivity to navigate their car would have problems in areas where network reliability is less than perfect. The purpose of this work is to investigate the feasibility and advantages of combining offline mapping with locally sensed positioning systems for better vehicle navigation and safety. While connected to the internet, vehicles cache offline maps and use local sensor information (such as GPS) from their inertial navigation system or computer vision without needing continuous access. The research outlines a hardware-software architecture with embedded offline map data storage, edge-based road following algorithms and an integration mechanism to advanced driving assistance systems (ADAS). In the evaluation of performance, accuracy assessments with online mapping systems are considered for offline maps and how these can have implications on end driver usability as well impacts on real-world autonomous driving technologies. The results suggest that offline mapping and on-board sensor-based localization would be able to improve vehicle contextual navigation performance in diverse driving scenarios.

Deep Q-Learning-Based Trajectory Optimization for Vehicle Navigation in CARLA

This paper presents a comprehensive study that focuses on simulating a vehicle within the autonomousvehicle simulator, CARLA. The primary objective of this research is to enable the vehicle to accurately follow apredetermined trajectory while effectively avoiding obstacles in its environment. Deep Q-Learning algorithms areemployed to achieve this goal, aiming to optimize the safety of the vehicle'snavigation. The simulation of the vehicleserves as a platform for studying the rules of Deep Q-Networks (DQN) and their impact on the vehicle's navigation.The objective is to identify the most suitable rule that leads to improved optimization of the vehicle's trajectory. Byleveraging the capabilities of CARLA as the simulation environment and implementing state-of-the-art DQNalgorithms, this research contributes to the advancement of autonomous vehicle technology. The findings of this studyhave practical implications for enhancing the safety and efficiency of autonomous vehicle navigation systems, makingthem highly relevant to industryprofessionals, researchers, and academic scholars in this field.

Rescuing impaired hippocampal-cortical interactions and spatial reorientation learning and memory during sleep in a mouse model of Alzheimer's disease using hippocampal 40 Hz stimulation.

One of the earliest symptoms of Alzheimer's disease (AD) is getting lost in space or experiencing deficits in spatial navigation, which involve navigation computations as well as learning and memory. We investigated cross brain region interactions supporting memory formation as a potential causative factor of impaired spatial learning and memory in AD. To assess this relationship between AD pathophysiology, brain changes, and behavioral alterations, we used a targeted approach for clearing amyloid beta and tau to rescue functional interactions in the brain. This research strongly connects brain activity patterns during sleep to tau and amyloid accumulation, and will aid in understanding the mechanisms underlying cognitive dysfunction in AD. Furthermore, the results offer insight for improving early identification and treatment strategies.

Optimizing the Matching Area for Underwater Gravity Matching Navigation Based on a New Gravity Field Feature Parameters Selection Method

This article mainly studies the selection of the matching area in gravity matching navigation systems of underwater vehicles. Firstly, we comprehensively consider 14 types of gravity field feature parameters, and a new gravity field feature parameters selection method is proposed based on feature selection principles and support vector machine algorithms. Secondly, according to the new gravity field feature parameters selection method, the five feature parameters, including range, pooling difference, standard deviation of gravity anomaly, roughness, and correlation coefficient, were selected from the 14 gravity field features parameters. The selected five feature parameters are integrated using SVM, and a classification model is constructed with carefully chosen training and testing sets and parameters for validation. Based on the experimental results, compared to the pre-calibrated results, the classification accuracy of the testing set reaches 91%, demonstrating the effectiveness of the gravity field feature parameter selection method in distinguishing between the suitable and the unsuitable areas. Finally, this method is applied to another area, and we carried out navigation experiments in the areas that were suitable areas in all four directions, as not all areas were suitable in four directions. The results showed that the areas that were suitable in all four directions provided better matching effects, the mean positioning accuracy was less than 100 m, and the accuracy was more than 90%. In path planning, priority can be given to areas that are suitable in all four directions.

Control and motion planning of fixed-wing UAV through reinforcement learning

Autonomous systems, driven by advancements in artificial intelligence and machine learning, are increasingly integral to various domains, including aerial vehicle control. This paper explores the application of reinforcement learning (RL) in the Guidance, Navigation, and Control (GNC) systems of aerial vehicles, specifically focusing on motion planning for fixed-wing UAVs. We present two key applications: waypoint tracking and dynamic target interception. These findings underscore the potential of reinforcement learning to enhance the robustness, adaptability, and efficiency of aerial vehicle control systems, paving the way for more autonomous and intelligent flight operations.