Car Navigation Research Articles

Analysis of the approaches to protection against interference of UAV satellite navigation receivers.

The main approaches to the construction of signal receivers of global satellite navigation systems of unmanned aerial vehicles, protected from the influence of artificially created interference, are considered. Such systems are equipped with multi-element phased antenna arrays and devices of controlled reception pattern antenna. In the process of processing the received information signal by the Controlled Reception Pattern Antenna device. the selection of interference and their suppression (compensation) is ensured, which is equivalent to the formation of dips in the spatial pattern of the antenna array at the corners that correspond to the directions of the interference sources. The basic principles of the implementation of the adaptive algorithm for processing the received information signal are given. The design and composition of the navigation receiver of the «Orlan-10» unmanned aerial vehicle equipped with a Controlled Reception Pattern Antenna device is considered. A conclusion was made regarding the fundamental possibility of creating a domestic navigation receiver with a Controlled Reception Pattern Antenna device. In the future, it is envisaged to deploy works on the creation of Controlled Reception Pattern Antenna devices with a greater number of antenna elements and the use of optimization principles of the quantitative composition of adaptation operations, in order to ensure the performance of these devices with a greater number of simultaneously enabled sources of interference and higher levels of their signal power.

Analysis of the possibility of using the satellite navigation system in the railway traffic control systems

The analysis of the possibility of using satellite navigation for rail vehicle positioning, which is the subject of many studies and projects, is aimed at assessing the uncertainty and accuracy of train positioning using satellite navigation systems, along with defining its impact on the capacity and safety of the rail infrastructure. Existing studies in this area prevent a clear assessment of the feasibility of implementing a satellite navigation system for rail vehicle positioning in rail traffic control systems. Therefore, it is necessary to analyze the possibility of using satellite navigation for rail vehicle positioning in terms of research problems arising from the limitations of satellite positioning system functionality, which include parameters related to accuracy, reliability, and speed of information transfer between the vehicle and the rail traffic control system. The implementation of the research work should be focused on developing a research model, subject to validation for the parameters and conditions of movement of rail vehicles in the defined area of railroad infrastructure, adopted within the framework of the work carried out. The research and the model form the basis for developing the knowledge of implementing satellite navigation in practical solutions on the railroad network.

AI-Enhanced Automotive Navigation: Enriching Driving Experiences through Intelligent Contextual Information Delivery

AI-Enhanced Automotive Navigation: Enriching Driving Experiences through Intelligent Contextual Information Delivery

How Do In-Car Navigation Aids Impair Expert Navigators’ Spatial Learning Ability?

Reliance on digital navigation aids has already shown negative impacts on navigators’ innate spatial abilities. How this happens is still an open research question. We report on an empirical study with twenty-four experienced (male) taxi drivers to evaluate the long-term impacts of in-car navigation system use on the spatial learning ability of these navigation experts. Specifically, we measured cognitive load by means of electroencephalography (EEG) coupled with eye tracking to assess their visuospatial attention allocation during a video-based route-following task while driving through an unknown urban environment. We found that long-term reliance on in-car navigation aids did not affect participants’ visual attention allocation during spatial learning but rather limited their ability to encode viewed geographic information into memory, which, in turn, led to greater cognitive load, especially along route segments between intersections. Participants with greater dependence on in-car navigation aids performed worse on the spatial knowledge tests. Our combined behavioral and neuropsychological findings provide evidence for the impairment of expert navigators’ spatial learning ability when exposed to long-term use of digital in-car navigation aids.

Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning

In this paper, we present the exploration of indoor positioning technologies for UAVs, as well as navigation techniques for path planning and obstacle avoidance. The objective was to perform warehouse inventory tasks, using a drone to search for barcodes or markers to identify objects. For the indoor positioning techniques, we employed visual-inertial odometry (VIO), ultra-wideband (UWB), AprilTag fiducial markers, and simultaneous localization and mapping (SLAM). These algorithms included global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. For UAV navigation, we combined the SLAM-based RTAB-map indoor mapping and navigation path planning of the ROS for indoor environments. This system enabled precise drone positioning indoors and utilized global and local path planners to generate flight paths that avoided dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. To achieve warehouse inventory inspection, a reinforcement learning approach was proposed, recognizing markers by adjusting the UAV’s viewpoint. We addressed several of the main problems in inventory management, including efficiently planning of paths, while ensuring a certain detection rate. Two reinforcement learning techniques, AC (actor–critic) and PPO (proximal policy optimization), were implemented based on AprilTag identification. Testing was performed in both simulated and real-world environments, and the effectiveness of the proposed method was validated.

A novel robust adaptive Kalman filter with application to urban vehicle integrated navigation systems

This study addresses the formidable challenge presented by non-Gaussian, time-varying, heavy-tailed process noise in integrated vehicle navigation systems. While robust adaptive filtering methods exist, their efficacy is constrained, and certain algorithms exhibit suboptimal usability due to an excess of a priori parameters. We deduce an adaptive robust Kalman filter, characterizing process noise with a Student's t distribution and measurement noise adhering to a Gaussian distribution. To enhance robustness against unknown or varying noise statistics, our method simultaneously estimates the scale matrix, degrees of freedom parameters, state vector, and measurement noise covariance through the expectation–maximization (EM) algorithm and Bayesian variational inference theory. Rigorous simulations and empirical experiments validate its superior accuracy and stability, requiring minimal a priori parameter information. Vehicle-based experiments further demonstrate positioning error below 2.78 m, velocity error below 0.34 m/s, and heading error less than 1.28° after achieving a steady state. This approach exhibits significant potential for advancing the precision and reliability of vehicle navigation systems.

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons.

Silicon-based optical phased array with a reconfigurable aperture for “gaze” scanning of LiDAR

Light detection and ranging (LiDAR) serves as one of the key components in the fields of autonomous driving, surveying mapping, and environment detection. Conventionally, dense points clouds are pursued by LiDAR systems to provide high-definition 3D images. However, the LiDAR is typically used to produce abundant yet redundant data for scanning the homogeneous background of scenes, resulting in power waste and excessive processing time. Hence, it is highly desirable for a LiDAR system to “gaze” at the target of interest by dense scanning and rough sparse scans on the uninteresting areas. Here, we propose a LiDAR structure based on an optical phased array (OPA) with reconfigurable apertures to achieve such a gaze scanning function. By virtue of the cascaded optical switch integrated on the OPA chip, a 64-, 128-, 192-, or 256-channel antenna can be selected discretionarily to construct an aperture with variable size. The corresponding divergence angles for the far-field beam are 0.32°, 0.15°, 0.10°, and 0.08°, respectively. The reconfigurable-aperture OPA enables the LiDAR system to perform rough scans via the large beam spots prior to fine scans of the target by using the tiny beam spots. In this way, the OPA-based LiDAR can perform the “gaze” function and achieve full-range scanning efficiently. The scanning time and power consumption can be reduced by 1/4 while precise details of the target are maintained. Finally, we embed the OPA into a frequency-modulated continuous-wave (FMCW) system to demonstrate the “gaze” function in beam scanning. Experiment results show that the number of precise scanning points can be reduced by 2/3 yet can obtain the reasonable outline of the target. The reconfigurable-aperture OPA (RA-OPA) can be a promising candidate for the applications of rapid recognition, like car navigation and robot vision.

Memory-based Distribution Shift Detection for Learning Enabled Cyber-Physical Systems with Statistical Guarantees

Incorporating learning based components in the current state-of-the-art cyber-physical systems (CPS) has been a challenge due to the brittleness of the underlying deep neural networks. On the bright side, if executed correctly with safety guarantees, this has the ability to revolutionize domains like autonomous systems, medicine, and other safety-critical domains. This is because it would allow system designers to use high-dimensional outputs from sensors like camera and LiDAR. The trepidation in deploying systems with vision and LiDAR components comes from incidents of catastrophic failures in the real world. Recent reports of self-driving cars running into difficult to handle scenarios is ingrained in the software components which handle such sensor inputs. The ability to handle such high-dimensional signals is due to the explosion of algorithms which use deep neural networks. Sadly, the reason behind the safety issues is also due to deep neural networks themselves. The pitfalls occur due to possible over-fitting and lack of awareness about the blind spots induced by the training distribution. Ideally, system designers would wish to cover as many scenarios during training as possible. However, achieving a meaningful coverage is impossible. This naturally leads to the following question: is it feasible to flag out-of-distribution (OOD) samples without causing too many false alarms? Such an OOD detector should be executable in a fashion that is computationally efficient. This is because OOD detectors often are executed as frequently as the sensors are sampled. Our aim in this article is to build an effective anomaly detector. To this end, we propose the idea of a memory bank to cache data samples which are representative enough to cover most of the in-distribution data. The similarity with respect to such samples can be a measure of familiarity of the test input. This is made possible by an appropriate choice of distance function tailored to the type of sensor we are interested in. Additionally, we adapt conformal anomaly detection framework to capture the distribution shifts with a guarantee of false alarm rate. We report the performance of our technique on two challenging scenarios: a self-driving car setting implemented inside the simulator CARLA with image inputs and autonomous racing car navigation setting with LiDAR inputs. From the experiments, it is clear that a deviation from the in-distribution setting can potentially lead to unsafe behavior. It should be noted that not all OOD inputs lead to precarious situations in practice, but staying in-distribution is akin to staying within a safety bubble and predictable behavior. An added benefit of our memory-based approach is that the OOD detector produces interpretable feedback for a human designer. This is of utmost importance since it recommends a potential fix for the situation as well. In other competing approaches, such feedback is difficult to obtain due to reliance on techniques which use variational autoencoders.

A Risk Assessment Framework Based on Fuzzy Logic for Automotive Systems

Recent advancements in the automotive field have significantly increased the level of complexity and connectivity of modern vehicles. In this context, the topic of cybersecurity becomes extremely relevant, as a successful attack can have an impact in terms of safety on the car navigation, potentially leading to harmful behavior. Risk assessment is typically performed using discrete input and output scales, which can often lead to an identical output in terms of risk evaluation despite the inputs presenting non-negligible differences. This work presents a novel fuzzy-logic-based methodology to assess cybersecurity risks which takes attack feasibility and safety impact as input factors. This technique allows us explicitly model the uncertainty and ambiguousness of input data, which is typical of the risk assessment process, providing an output on a more detailed scale. The fuzzy inference engine is based on a set of control rules expressed in natural language, which is crucial to maintaining the interpretability and traceability of the risk calculation. The proposed framework was applied to a case study extracted from ISO/SAE 21434. The obtained results are in line with the traditional methodology, with the added benefit of also providing the scatter around the calculated value, indicating the risk trend. The proposed method is general and can be applied in the industry independently from the specific case study.

Evaluating the Accuracy of Probe-Based Truck Volumes using Continuous and Short-Duration Traffic Counts

The widespread nature of cell phones and connected vehicle navigation systems has led to the development of commercially available probe-based traffic data products. This study assesses the accuracy of annual average daily total traffic, truck traffic, medium-duty truck traffic, and heavy-duty truck traffic volumes obtained using probe-based traffic activity indices from a North American company called StreetLight Data (StL). The probe-based estimates were compared with 2019, 2020, and 2021 volumes at eleven continuous count sites and 2019 volumes at twenty-nine short-duration count (SDC) sites in the Winnipeg Metropolitan Region. The results showed reasonable agreement between the ground truth and probe-based total traffic estimates with mean absolute percent errors (MAPEs) ranging from 8.8% to 22.1% across the study years. The medium-duty truck estimates had larger errors than total traffic with MAPEs of 29.9% to 37.5%. Despite having higher volumes than medium-duty trucks, heavy-duty trucks had the smallest probe data sample and largest errors with MAPEs of 56.6% to 96.4%. Benefiting from its larger sample size, the StL medium-duty truck index was found to be a better predictor of heavy-duty truck traffic than the heavy-duty truck index. Further, the total truck volumes estimated using only the medium-duty index were more accurate than those taken as the sum of the medium and heavy-duty truck volumes obtained using their respective indices. Finally, the percent differences for the 2019 annual average daily total traffic and truck traffic estimates at the SDC sites were comparable when only the medium-duty index was used for truck volume estimation.

Development of Fuzzy Logic Controller in Automatic Vehicle Navigation using IoT

The integration of fuzzy logic controllers in automatic vehicle navigation systems represents a significant advancement in intelligent transportation systems, especially when paired with Internet of Things (IoT) functionalities and optimized through genetic algorithms. This innovative fusion harnesses the precision of fuzzy logic, the connectivity of IoT, and the optimization capabilities of genetic algorithms to transform automatic vehicle navigation. Fuzzy logic controllers excel in managing uncertainty and imprecision, providing decision-making capabilities akin to human reasoning. By simultaneously assessing multiple inputs and determining actions based on degrees of truth, fuzzy logic enables safe and efficient navigation in dynamic driving environments with fluctuating variables like obstacle proximity and traffic flow. IoT integration enhances navigation systems by enabling real-time data collection and sharing among vehicles and infrastructure, fostering adaptive route planning and improving the overall navigation experience. Genetic algorithms further optimize system performance by iteratively adjusting fuzzy logic controller parameters, ensuring efficient decision-making tailored to specific performance criteria such as travel time and fuel consumption. This collaborative integration of fuzzy logic controllers, IoT, and genetic algorithms offers a holistic solution to the challenges of automatic vehicle navigation, enhancing safety, efficiency, and adaptability in complex driving scenarios. Beyond enhancing individual vehicle performance, this approach contributes to overall transportation system efficiency and safety by mitigating traffic congestion, reducing emissions, and minimizing accidents. Consequently, these integrated systems address crucial societal challenges and pave the way for widespread adoption of autonomous vehicles in the future.

Federated Learning-Based Detection and Control Mechanism of In-Car Navigation Safety System

Federated Learning-Based Detection and Control Mechanism of In-Car Navigation Safety System

Automated Car: Intelligent Maneuver

Abstract: Automated vehicles are rapidly evolving to meet the demands of modern transportation systems, aiming for enhanced safety, efficiency, and user experience. Autonomous vehicles are also a big part of these technologies. The most important action of a driver has to do is to follow the lanes on the way to the destination. This research presents Intelligent Maneuver, a sophisticated framework integrating four key functionalities crucial for automated car navigation: object avoidance, traffic light detection, self-parking, and lane detection. Leveraging advanced computer vision techniques and sensors fusion our framework enables vehicles to navigate complex urban environments autonomously

Intelligent vehicle navigation systems and autonomous driving technology: A comprehensive analysis

This paper conducts a comprehensive study and analysis of intelligent vehicle navigation systems and autonomous driving technology. We review the historical development of autonomous driving technology, discuss key concepts such as perception, decision-making, and control, explore various types of autonomous vehicles, and examine various aspects of intelligent vehicle navigation systems. Additionally, we investigate safety, reliability, legal frameworks in the field of autonomous driving, as well as future trends and ethical considerations. Finally, we summarize the main findings of the research and provide recommendations for future studies.

Enhancing Overt and Covert Attention Using a Real-Time Neurofeedback Game With Consumer-Grade EEG

Neurofeedback training is emerging as a promising tool for cognitive enhancement in healthy as well as cognitive-deficit patients. In this paper, we propose the design of a real-time neurofeedback game using a consumer-grade wireless Electroencephalography (EEG) system and examine its efficacy in enhancing overt and covert attention abilities for healthy individuals. The game uses a simulated car navigation task, in which a car is navigated using the attention level of the player. A total of twenty-three healthy subjects participated in four sessions of neurofeedback training. The effectiveness of the proposed neurofeedback game is evaluated using attention scores based on the Sample entropy of EEG signals, beta-to-(alpha + theta) ratio, and the time taken for completing the game. The variations in covert attention are assessed by introducing visual distractions when the player navigates the car and quantified using changes in the attention score and the time taken for restoring the initial attention level. The attention score significantly improved from the first session to the final session by 22.64% and 21.43% for overt attention and covert attention tasks, respectively. The experimental results demonstrate that the proposed neurofeedback game is an effective tool for enhancing overt and covert attention skills in healthy individuals.

Development of real-time navigation system for laparoscopic hepatectomy using magnetic micro sensor

Background We report a new real-time navigation system for laparoscopic hepatectomy (LH), which resembles a car navigation system. Material and methods Virtual three-dimensional liver and body images were reconstructed using the “New-VES” system, which worked as roadmap during surgery. Several points of the patient’s body were registered in virtual images using a magnetic position sensor (MPS). A magnetic transmitter, corresponding to an artificial satellite, was placed about 40 cm above the patient’s body. Another MPS, corresponding to a GPS antenna, was fixed on the handling part of the laparoscope. Fiducial registration error (FRE, an error between real and virtual lengths) was utilized to evaluate the accuracy of this system. Results Twenty-one patients underwent LH with this system. Mean FRE of the initial five patients was 17.7 mm. Mean FRE of eight patients in whom MDCT was taken using radiological markers for registration of body parts as first improvement, was reduced to 10.2 mm (p = .014). As second improvement, a new MPS as an intraoperative body position sensor was fixed on the right-sided chest wall for automatic correction of postural gap. The preoperative and postoperative mean FREs of 8 patients with both improvements were 11.1 mm and 10.1 mm (p = .250). Conclusions Our system may provide a promising option that virtually guides LH.

PARALLEL AND DISTRIBUTED COMPUTING TECHNOLOGIES FOR AUTONOMOUS VEHICLE NAVIGATION

Context. Autonomous vehicles are becoming increasingly popular, and one of the important modern challenges in their development is ensuring their effective navigation in space and movement within designated lanes. This paper examines a method of spatial orientation for vehicles using computer vision and artificial neural networks. The research focused on the navigation system of an autonomous vehicle, which incorporates the use of modern distributed and parallel computing technologies.
 Objective. The aim of this work is to enhance modern autonomous vehicle navigation algorithms through parallel training of artificial neural networks and to determine the optimal combination of technologies and nodes of devices to increase speed and enable real-time decision-making capabilities in spatial navigation for autonomous vehicles.
 Method. The research establishes that the utilization of computer vision and neural networks for road lane segmentation proves to be an effective method for spatial orientation of autonomous vehicles. For multi-core computing systems, the application of parallel programming technology, OpenMP, for neural network training on processors with varying numbers of parallel threads increases the algorithm’s execution speed. However, the use of CUDA technology for neural network training on a graphics processing unit significantly enhances prediction speeds compared to OpenMP. Additionally, the feasibility of employing PyTorch Distributed Data Parallel (DDP) technology for training the neural network across multiple graphics processing units (nodes) simultaneously was explored. This approach further improved prediction execution times compared to using a single graphics processing unit.
 Results. An algorithm for training and prediction of an artificial neural network was developed using two independent nodes, each equipped with separate graphics processing units, and their synchronization for exchanging training results after each epoch, employing PyTorch Distributed Data Parallel (DDP) technology. This approach allows for scalable computations across a higher number of resources, significantly expediting the model training process.
 Conclusions. The conducted experiments have affirmed the effectiveness of the proposed algorithm, warranting the recommendation of this research for further advancement in autonomous vehicles and enhancement of their navigational capabilities. Notably, the research outcomes can find applications in various domains, encompassing automotive manufacturing, logistics, and urban transportation infrastructure. The obtained results are expected to assist future researchers in understanding the most efficient hardware and software resources to employ for implementing AI-based navigation systems in autonomous vehicles. Prospects for future investigations may encompass refining the accuracy of the proposed parallel algorithm without compromising its efficiency metrics. Furthermore, there is potential for experimental exploration of the proposed algorithm in more intricate practical scenarios of diverse nature and dimensions.

Development of a correction algorithm for the integrated navigation system of an unmanned aerial vehicle in the landing task on a moving platform

Development of a correction algorithm for the integrated navigation system of an unmanned aerial vehicle in the landing task on a moving platform

БЛОЧНЫЙ ФИЛЬТР КАЛМАНА С ЛИНЕЙНОЙ ВЫЧИСЛИТЕЛЬНОЙ СЛОЖНОСТЬЮ ДЛЯ КОМПЛЕКСИРОВАННЫХ С ТЕХНИЧЕСКИМ ЗРЕНИЕМ ИНЕРЦИАЛЬНЫХ НАВИГАЦИОННЫХ СИСТЕМ

As the use of computer vision in vehicle navigation systems increases, there is a growing need for computationally efficient optimal filtering algorithms that can jointly process measurements from inertial sensors and a large number of visual information sources. Contribution. This paper proposes a new modification of the Kalman filter (the block Kalman filter) with linear computational complexity in the number of measurement information sources. A numerically stable version of the algorithm is derived using LDL-factorization of covariance matrices. Purpose of the study. The aim is to develop an LDL-factorized block Kalman filter algorithm with linear computational complexity with respect to the number of information sources in the measurement system and demonstrate its applicability in a integrated visual-inertial navigation system. Materials and Methods. This research demonstrates the algebraic equivalence of the block Kalman filter to the standard one in a specific case. A method is proposed for approximating the estimates of the block filter to those of the standard one with desired accuracy by expanding the state vector. The computational complexity of the block Kalman filter is compared to the computational complexity of standard one within a numerical experiment. Numerical modeling of the block filter operation within a navigation system is conducted for comparison with the standard filter. Results. The equations of the block LDL-factorized Kalman filter are obtained. Its linear computational complexity with respect to the number of information sources is verified. A method for approximating the estimates of the block Kalman filter to those of the standard filter by expanding the state vector is proposed and verified within the framework of navigation system simulation. Conclusion. The main theoretical properties of the block Kalman filter were confirmed in numerical experiments. Further research will explore alternative methods of forming the extended state vector of the filter; the Kalman block filter will be tested within more complex scenarios.