Car Navigation Research Articles

ANALYSIS OF AUTOMATIC CONTROL SYSTEMS OF A MULTIPURPOSE AUTONOMOUS UNMANNED UNDERWATER VEHICLE WITH COMPLEX MOTION DYNAMICS

The overview article is devoted to the analysis of control systems of autonomous unmanned underwater vehicles, which belong to robotic underwater complexes and are used to provide search and deep-sea emergency and rescue operations, survey of various underwater objects (main pipelines, bottom structures, ship hulls, etc.), conducting scientific oceanographic surveys research, monitoring of the marine environment. The work examines scientific literary sources, which provide the results of development and research of orientation, navigation and motion control systems of multi-purpose autonomous unmanned underwater vehicles with complex movement dynamics, in particular when performing missions in a limited space with movement along complex trajectories. Special attention is focused on such modes of movement of devices as trajectory tracking, path-following and way point tracking. An analysis of the means of navigation and control, which can be used to ensure different modes of movement, was carried out, as well as the directions of development of both the unmanned underwater vehicles themselves and their control systems were considered.
 In order to substantiate the development of a control system for a multi-purpose maneuverable mini-class autonomous vehicle, the main requirements are formulated. It is proposed to conduct a synthesis and research of the control system based on the simulation of the dynamics of the movement of the mini-vehicle for different modes of movement in terms of parameters and trajectories, to develop algorithms for the adaptive control of the vehicle, to improve the system orientation and navigation, built on the basis of MEMS sensors. An information model of the development process is proposed and substantiated, which specifies the sequence of determining important parameters and characteristics of the vehicle and performing important stages of synthesis and analysis of the control system.

Route planning using divide-and-conquer: A GAT enhanced insertion transformer approach

Route planning is widely used in areas such as car navigation. For some popular mobility services such as meal delivery and online car-hailing, planning routes that closely follow drivers’ actual routes is important. Some existing methods propose that drivers plan routes in two steps: first identify intermediate waypoints on the route that connects the origin and destination; then find a series of sub-routes between successive waypoints, respectively. In this research, we advance these methods as follows: (1) We generalize the two-step process to a divide-and-conquer framework. We first identify intermediate waypoints between the origin and the destination and decompose the route planning problem into several sub-problems, whose intermediate waypoints are identified again. We recursively repeat this process until no more waypoints are identified for all the sub-problems; and (2) Unlike existing studies that identify waypoints solely based on the travel frequency of links, we propose a Graph Attention Network (GAT) Enhanced Insertion Transformer (GEIT) model for waypoint identification. GEIT model uses an Insertion Transformer to learn the relationship among links, which can better capture the mobility pattern embedded in historical routes. In addition, by incorporating a GAT to enhance our link representations, our model spreads the learned information along the road network to address the trajectory sparseness problem. Numerical experiments on a real-world trajectory data set demonstrate that the routes planned by our model show fewer deviations from the actual routes compared with state-of-the-art route planning algorithms.

Research on optimal path planning technology for vehicle positioning and navigation system

Abstract Intelligent Transportation System (ITS) is created in the context of the full development and progress of contemporary science and technology, aiming to help people make the best choice, drive the best vehicle, and control the best road network traffic. Intelligent vehicle positioning and navigation system is a high-tech integrated system that applies automatic vehicle positioning technology, geographic information system and database technology, computer technology, multimedia technology, and modern communication technology. In this paper, based on the research of GPS and GIS technology, by analyzing and comparing the advantages and disadvantages of different optimal path planning algorithms, we propose to optimize the A* algorithm by using the minimum heap sorting method for nodes and combining the twoway search and hierarchical search strategies to complete the improvement of the A* algorithm. The experimental results show that the path search speed of the improved algorithm has been significantly improved and the path design optimization is obvious. The improvement algorithm proposed in this paper is feasible and reasonable, which is good for upgrading the in-vehicle navigation system and relieving urban traffic congestion.

Non-Line-of-Sight Detection Based on Neuromorphic Time-of-Flight Sensing

Non-line-of-sight (NLOS) detection and ranging aim to identify hidden objects by sensing indirect light reflections. Although numerous computational methods have been proposed for NLOS detection and imaging, the post-signal processing required by peripheral circuits remains complex. One possible solution for simplifying NLOS detection and ranging involves the use of neuromorphic devices, such as memristors, which have intrinsic resistive-switching capabilities and can store spatiotemporal information. In this study, we employed the memristive spike-timing-dependent plasticity learning rule to program the time-of-flight (ToF) depth information directly into a memristor medium. By coupling the transmitted signal from the source with the photocurrent from the target object into a single memristor unit, we were able to induce a tunable programming pulse based on the time interval between the two signals that were superimposed. Here, this neuromorphic ToF principle is employed to detect and range NLOS objects without requiring complex peripheral circuitry to process raw signals. We experimentally demonstrated the effectiveness of the neuromorphic ToF principle by integrating a HfO2 memristor and an avalanche photodiode to detect NLOS objects in multiple directions. This technology has potential applications in various fields, such as automotive navigation, machine learning, and biomedical engineering.

Exploring the technological position and role of vehicle navigation companies by using patent citation network

Autonomous driving is the future trend when it comes to the age of 5G at the end of 21st century, and vehicle navigation is the indispensable key technology. The companies with car navigation software and hardware business are facing fierce competition. Business operators must know the company's position and role in the industry, in order to adopt strategies to respond. In this study, two indicators are investigated using the social network analysis technics such as technological redundancy and network centrality in patent citation network (PCN). It is understood that technological redundancy is determined by technology knowledge status (TKS) as well as technology knowledge reliability (TKR). There are also four types of social network centrality: degree centrality, eigenvector centrality, closeness centrality, and betweenness centrality. In this study, an analysis of the technical position and role of individual companies in the industry will be presented by using the two indicators and the primary components. Moreover, technological redundancy is used to study the technical position and role of a company's unitary patent capability in the industry. This will reinforce the company's understanding, and then take strategies. By using this model, the study can effectively identify the technical position and role of the company and its unitary patent capability. Furthermore, the study also shows that based on this model, there is a total of three companies where positions and roles are as leading status in the vehicle navigation industry, i.e. Tomtom map company, Alpine and Asin AW two electronic companies. In addition, companies with unique technologies, such as Sirius Xm Radio, etc. can use their role and position to conduct merger and acquisition, collaboration, and alliance strategies.

SEAMLESS REALTIME LANE LEVEL VEHICULAR NAVIGATION IN GNSS CHALLENGING ENVIRONMENTS USING A RTK GNSS/IMU/VINS INTEGRATION SCHEME

Abstract. Outdoor positioning requires a reliable solution that can work in environments where satellite signals are often blocked or degraded. Global Navigation Satellite System (GNSS) is a common choice, but it may not provide accurate results for land vehicles. To address this challenge, this research proposes a multi-sensor integrated system for vehicle navigation that combines GNSS with other sensors. The system uses Extended Kalman Filter (EKF) to fuse the data from different sources and improve the navigation performance. The algorithm targets to provide seamless navigation for urban environments as well as various indoor environments fields with INS/GNSS/VIO aiding integrated solutions. The experimental vehicle of this research is equipped with a tactical-grade inertial sensing measurement unit (IMU) as the test system, a self-designed and assembled visual platform, which includes a camera with a time synchronization protocol and a low-cost IMU. Also, both indoor experimental fields and outdoor urban scenarios with different high challenging were tested to verify the developed algorithm. To evaluate the performance of the proposed real-time navigation system, we use a high-accuracy navigation-grade system as a reference, which provides a stable and reliable trajectory. The result indicates that using the GNSS RTK solution with VIO aiding integration scheme reduced the RMS errors in long outage (450 sec, 1812 m) by 87.4% and 79.9% in position and velocity error, respectively. In urban scenario, the along-track/cross-track maximum errors can achieve 1.4 m / 1.5 m. Overall, these contribute to the development of real-time navigation systems for self-driving vehicle in the future.

The system of coordinated operation of two underwater vehicles in a complex environment

The paper discusses methods that ensure the coordinated operation of two autonomous underwater vehicles, one of which is equipped with a multi–link manipulator and a vision system, and the second - auxiliary – only a vision system. These methods ensure the joint operation of two autonomous underwater vehicles both in the mode of semi-automatic positional remote control of the manipulator, and in the mode of automatic execution of operations with various objects by them. At the same time, in both modes, work is considered in situations when the vision system of the main underwater vehicle does not allow observing the object of work, which is nevertheless located in the working area of the manipulator. In this case, the auxiliary inspection underwater vehicle with its own vision system is used to monitor the object. In the semi-automatic remote control mode, the manipulator automatically takes into account the current spatial orientation of the optical axis of the vision system of the inspection underwa-ter vehicle, as well as the spatial orientations of both underwater vehicles at yaw angles in the absolute coordinate system. And in automatic mode, an accurate transfer of the spatial location and orientation of the object, which are determined using the vision system of the inspection underwater vehicle, is additionally provided to the associated coordinate system of the main underwater vehicle. At the same time, it is possible to identify and eliminate errors that occur during the operation of the vision system and navigation systems of both underwater vehicles. The re-sults of experimental studies have confirmed the operability of the proposed methods, the technical implementa-tion of which does not cause fundamental difficulties.

Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions

In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the GPS signal becomes unstable, other auxiliary navigation systems, such as computer-vision-based positioning, are employed for more accurate localization and mapping. However, the quality of data obtained from AV’s sensors might be deteriorated by the extreme environmental conditions too, which infinitely leads to the decrease in navigation performance. To verify our computer-vision-based localization system design, we considered the Arctic region use case, which poses additional challenges for the AV’s navigation and might employ artificial visual landmarks for improving the localization quality, which we used for the computer vision training. We further enhanced our data by applying affine transformations to increase its diversity. We selected YOLOv4 image detection architecture for our system design, as it demonstrated the highest performance in our experiments. For the computational platform, we employed a Nvidia Jetson AGX Xavier device, as it is well known and widely used in robotic and AV computer vision, as well as deep learning applications. Our empirical study showed that the proposed computer vision system that was further trained on the dataset enhanced by affine transformations became robust regarding image quality degradation caused by extreme environmental conditions. It was effectively able to detect and recognize images of artificial visual landmarks captured in the extreme Arctic region’s conditions. The developed system can be integrated into vehicle navigation facilities to improve their effectiveness and efficiency and to prevent possible navigation performance deterioration.

AMM: An Adaptive Online Map Matching Algorithm

Online map matching is essential for some location-based services, such as car navigation. However, due to GPS measurement errors and/or the lack of sufficient information, the performance of most existing online algorithms will degrade in the increasingly complex traffic environment. In this paper, we propose an adaptive online map matching algorithm called AMM. The basic idea is that AMM should be able to calibrate GPS observation data for various measurement errors and under complex urban conditions. First, we establish a collaborative evaluation model between GPS points and candidate points to effectively filter low-quality GPS measurement points, dynamically set the weights of different features and comprehensively select the best candidate points. Second, we propose a retrospective correction mechanism to correct the previous matching results when more information is available, which will help improve the accuracy of future GPS points. Furthermore, we define parameter self-tuning rules for AMM to enhance its portability by avoiding time-consuming parameter tuning steps. We conduct extensive experiments to evaluate the performance of AMM on real vehicle trajectory datasets. The experiment results show that AMM outperforms its counterparts by up to 32% in terms of accuracy and its performance in different traffic conditions is more stable.

MARSIM: A Light-Weight Point-Realistic Simulator for LiDAR-Based UAVs

The emergence of low-cost, small form factor and light-weight solid-state LiDAR sensors have brought new opportunities for autonomous unmanned aerial vehicles (UAVs) by advancing navigation safety and computation efficiency. Yet the successful developments of LiDAR-based UAVs must rely on extensive simulations. Existing simulators can hardly perform simulations of real-world environments due to the requirements of dense mesh maps that are difficult to obtain. In this paper, we develop a point-realistic simulator of real-world scenes for LiDAR-based UAVs. The key idea is the underlying point rendering method, where we construct a depth image directly from the point cloud map and interpolate it to obtain realistic LiDAR point measurements. Our developed simulator is able to run on a light-weight computing platform and supports the simulation of LiDARs with different resolution and scanning patterns (e.g., spinning LiDARs and solid-state LiDARs), dynamic obstacles, and multi-UAV systems. Developed in the ROS framework, the simulator can easily communicate with other key modules of an autonomous robot, such as perception, state estimation, planning, and control. Finally, the simulator provides 10 high-resolution point cloud maps of various real-world environments, including forests of different densities, historic building, office, parking garage, and various complex indoor environments. These realistic maps provide diverse testing scenarios for an autonomous UAV. Evaluation results show that the developed simulator achieves superior performance in terms of time and memory consumption against Gazebo and that the simulated UAV flights highly match the actual one in real-world environments. We believe such a point-realistic and light-weight simulator is crucial to bridge the gap between UAV simulation and experiments and will significantly facilitate the research of LiDAR-based autonomous UAVs in the future.

Prioritization of Lane-Specific Traffic Jam Detection for Automotive Navigation Framework Utilizing Suddenness Index and Automatic Threshold Determination

Prioritization of Lane-Specific Traffic Jam Detection for Automotive Navigation Framework Utilizing Suddenness Index and Automatic Threshold Determination

Localization System for Vehicle Navigation Based on GNSS/IMU Using Time-Series Optimization with Road Gradient Constrain

In this paper, we propose a GNSS/IMU localization system for mobile robots when wheel speed sensors cannot be attached. Highly accurate location information is required for autonomous navigation of mobile robots. A typical method of acquiring location information is to use a Kalman filter for position estimation. The Kalman filter is a maximum-likelihood estimation method that assumes normally distributed noise. However, non-normally distributed GNSS multipath noise that frequently occurs in urban environments causes the Kalman filter to break down, and degrades the estimation performance. Other GNSS/IMU localization methods capable of lane-level estimation in urban environments use wheel speed sensors, which are unsuitable for the present situation. In this study, we aim to improve the performance of lane-level localization by adding a vehicle speed estimation function to adapt the method to those requiring wheel speed sensors. The proposed method optimizes time-series data to accurately compensate for accelerometer bias errors and reduce GNSS multipath noise. The evaluation confirmed the effectiveness of the proposed method, with improved velocity and position estimation performance compared with the Kalman filter method.

AESOP: Adjustable Exhaustive Search for One-Pixel Attacks in Deep Neural Networks

Deep neural networks have achieved remarkable performance in various fields such as image recognition and natural language processing. However, recent research has revealed that even a small imperceptible perturbation can confound well-trained neural network models and yield incorrect answers. Such adversarial examples are regarded as a key hazard to the application of machine learning techniques to safety-critical systems, such as unmanned vehicle navigation and security systems. In this study, we propose an efficient technique for searching one-pixel attacks in deep neural networks, which are recently reported as an adversarial example. Using exhaustive search, our method can identify one-pixel attacks which existing methods cannot detect. Moreover, the method can adjust exhaustiveness to reduce the search space dramatically. However, it still identifies most attacks. We present our experiment using the MNIST data set to demonstrate that our adjustable search method efficiently identifies one-pixel attacks in well-trained deep neural networks, including convolutional layers.

A Hybrid RISS/GNSS Method During GNSS Outage in the Land Vehicle Navigation System

The integrated navigation system of an inertial navigation system (INS) and a global navigation satellite system (GNSS) is a standard solution in land vehicle navigation applications. Considering a low-cost solution, the reduced inertial sensor system (RISS) is adopted in place of INS to provide better navigation performance for land vehicles with fewer inertial sensors and lower computations. However, low-cost sensors can quickly deteriorate the navigation solution during GNSS outage. Hence, a novel RISS /GNSS method with the assistance of the long short-term memory (LSTM) neural network (NN), which has the ability of adaptive memorizing, is proposed to bridge GNSS outage by means of data fusion. In addition, zero-velocity detection is applied to advance the navigation performance provided by the LSTM algorithm during GNSS outage. We examined the performance of this method by using real road test experiments in a land vehicle equipped with GNSS receivers and inertial sensors in addition to a high-end GNSS /INS to provide the reference solution. During 300-s GNSS outage, the experimental results illustrate that this hybrid method based on the LSTM algorithm can enhance the navigation accuracy by 50% when compared with the standalone RISS algorithm, and provide 30% improvements in comparison with the nonlinear autoregressive with exogenous input (NARX) algorithm.

Accelerated and Refined Lane-Level Route-Planning Method Based on a New Road Network Model for Autonomous Vehicle Navigation

Lane-level route planning is a critical issue for a lane-level navigation system for autonomous vehicles. Current route-planning methods mainly focus on the road level and applying them directly to search for lane-level routes results in a reduction in search efficiency. In addition, previously developed lane-level methods lack consideration for vehicle characteristics and adaptability to multiple road network structures. To solve this issue, this study proposes an accelerated and refined lane-level route-planning algorithm based on a new lane-level road network model. First, five sub-layers are designed to refine the internal structure of the divided road and intersection areas so that the model can express multiple variations in road network structures. Then, a multi-level route-planning algorithm is designed for sequential planning at the road level, lane group level, lane section level, and lane level to reduce the search space and significantly improve routing efficiency. Last, an optimal lane determination algorithm considering traffic rules, vehicle characteristics, and optimization objectives is developed at the lane level to find the optimal lanes on roads with different configurations, including those with a constant or variable number of lanes while satisfying traffic rules and vehicle characteristics. Tests were performed on simulated road networks and a real road network. The results demonstrate the algorithm’s better adaptability to changing road network structures and vehicle characteristics compared with past hierarchical route planning, and its higher efficiency compared with direct route planning, past hierarchical route planning, and the Apollo route-planning method, which can better support autonomous vehicle navigation.

MTrajPlanner: A Multiple-Trajectory Planning Algorithm for Autonomous Underwater Vehicles

Trajectory planning is a crucial task in designing the navigation systems of automatic underwater vehicles (AUVs). Due to the complexity of underwater environments, decision makers may hope to obtain multiple alternative trajectories in order to select the best. This paper focuses on the multiple-trajectory planning (MTP) problem, which is a new topic in this field. First, we establish a comprehensive MTP model for AUVs, by taking into account the complex underwater environments, the efficiency of each trajectory, and the diversity among different trajectories, simultaneously. Then, to solve the MTP, we develop an ant colony-based trajectory optimizer, which is characterized by a niching strategy, a decayed alarm pheromone measure, and a diversified heuristic measure. The niching strategy assists in identifying and maintaining a diverse set of high-quality solutions. The use of decayed alarm pheromone and diversified heuristic further improves the search effectiveness and efficiency of the algorithm. Experimental results on practical datasets show that our proposed algorithm not only provides multiple AUV trajectories for a flexible choice, but it also outperforms the state-of-the-art algorithms in terms of the single trajectory efficiency.

Do Social Aspects Affect Built-in Car Navigation Habits? A Stereotype Study

We encounter prejudices and stereotypes in all areas of life, including human navigation. This study seeks to answer whether there is a basis for navigation stereotypes such as: does our age, gender, place of residence or country affect our car navigation habits? An online questionnaire was distributed in three Central European countries (Hungary, Romania and Austria) and 1556 respondents were reached. In addition to habitancy (settlement type), it was noted that half of the examined concerns are influenced by stereotypes. A significant difference can be shown in questions that are directly or indirectly related to the financial situation of the driver (e.g., the existence and quality of the built-in GPS depends on whether someone has enough money to buy it). However, it seems that other stereotypes we have about driving and navigation are mostly false. The results suggest that it may be possible to personalize built-in navigation systems. By incorporating various “extra” services (e.g., personalized interface, placement), driving and navigation can be made safer and more comfortable for particular segments of society—the driver does not need to use other devices.

A Security and Privacy Preserved Intelligent Vehicle Navigation System

With emergence of smart city concept, intelligent transportation becomes one of its key enablers, with navigation as an indispensable component. Vehicular ad hoc network (VANET) based systems emerge to provide navigation services to vehicle users, which is characterized by employing Roadside Units (RSUs) to collect road condition information from nearby vehicles. The querying vehicle exploits the information retrieved through the RSUs to determine an optimal route. We focus on addressing two key issues in such system. First, while querying, the destination of the vehicle user should not be leaked to the queried RSU and trusted authority (TA), which is likely to be misused. Second, the navigation process should be independent of participation of the centralized TA, which can be a single point of failure. To address these issues, this article proposes a security and privacy preserved intelligent vehicle navigation system, adopting a distributed approach, to retrieve navigation without revealing the destination. The security of the system has been thoroughly evaluated using an adversary model, BAN Logic and AVISPA tool. In addition, the performance of the proposed system has been evaluated, and compared with the existing systems. The comparison illustrates that the proposed system is computationally efficient than the existing systems.

Development of human motion prediction strategy using inception residual block

Human Motion Prediction is a crucial task in computer vision and robotics. It has versatile application potentials such as in the area of human-robot interactions, human action tracking for airport security systems, autonomous car navigation, computer gaming to name a few. However, predicting human motion based on past actions is an extremely challenging task due to the difficulties in detecting spatial and temporal features correctly. To detect temporal features in human poses, we propose an Inception Residual Block(IRB), due to its inherent capability of processing multiple kernels to capture salient features. Here, we propose to use multiple 1-D Convolution Neural Network (CNN) with different kernel sizes and input sequence lengths and concatenate them to get proper embedding. As kernels strides over different receptive fields, they detect smaller and bigger salient features at multiple temporal scales. Our main contribution is to propose a residual connection between input and the output of the inception block to have a continuity between the previously observed pose and the next predicted pose. With this proposed architecture, it learns prior knowledge much better about human poses and we achieve much higher prediction accuracy as detailed in the paper. Subsequently, we further propose to feed the output of the inception residual block as an input to the Graph Convolution Neural Network (GCN) due to its better spatial feature learning capability. We perform a parametric analysis for better designing of our model and subsequently, we evaluate our approach on the Human 3.6M dataset and compare our short-term as well as long-term predictions with the state of the art papers, where our model outperforms most of the pose results, the detailed reasons of which have been elaborated in the paper.

Stereo matching algorithm for autonomous vehicle navigation using integrated matching cost and non-local aggregation

Stereo matching algorithm plays an important role in an autonomous vehicle navigation system to ensure accurate three-dimensional (3D) information is provided. The disparity map produced by the stereo matching algorithm directly impacts the quality of the 3D information provided to the navigation system. However, the accuracy of the matching algorithm is a challenging part to be solved since it is directly affected by the surrounding environment such as different brightness, less texture surface, and different image pair exposure. In this paper, a new framework of stereo matching algorithm that used the integration of census transform (CT) and sum of absolute difference (SAD) at the matching cost computation step, non-local cost aggregation at the second step, winner take all strategy at the third step, and a median filter at the final step to minimize disparity map error. The results show that the accuracy of the disparity map is improved using the proposed methods after some parameter adjustment. Based on the standard Middlebury and KITTI benchmarking dataset, it shows that the proposed framework produced accurate results compared with other established methods.