Navigation System For Autonomous Vehicle Research Articles

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.

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.

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.

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.

EVALUATING NAVIGATION PERFORMANCE OF ELASTICALLY CONSTRUCTED HD MAP WITH MULTI-SENSOR FUSION ENGINE SYSTEM

Abstract. In response to the rapid development of autonomous vehicles and the increasing demand for HD maps, the conventional mapping processes following HD maps guidelines require significant manpower and time resources. Therefore, we propose flexible procedures and methods for HD maps creation, aiming to reduce cost expenditure by employing diverse source of ground control point, sensor data collection, and mapping algorithm. This approach accelerates the production speed and capability of HD maps. In this study, we select Taiwan's National Highway No. 8 as the trial field for the elastic HD map construction method, and equipped with autonomous vehicle-grade GNSS, IMU, and LiDAR systems.We align the constructed-map data to the global coordinate system, in order to realize the concept of control point cloud map. To assess the assistance and correction capabilities of HD maps in autonomous vehicle navigation systems, we conduct accuracy evaluation through both direct and indirect methods, and analyse the strengths and weaknesses of each approach. The analysis result demonstrates that the elastic method-built HD maps not only meet the mapping accuracy requirements specified in the HD maps verification and validation guidelines, but also assist autonomous vehicles in realizing positioning, navigation, and timing with “where in lane” level (0.5 meter) accuracy.

Investigation of Accuracy of TOA and SNR of Radio Pulsar Signals for Vehicles Navigation.

It is known that X-ray and gamma-ray pulsars can only be observed by spacecraft because signals from these pulsars are impossible to be detected on the Earth's surface due to their strong absorption by the Earth's atmosphere. The article is devoted to the theoretical aspects regarding the development of an autonomous radio navigation system for transport with a small receiving antenna, using radio signals from pulsars, similar to navigation systems for space navigation. Like GNSS systems (X-ray and radio), they use signals from four suitable pulsars to position the object. These radio pulsars (out of 50) are not uniformly distributed but are grouped in certain directions (at least 6 clusters can be determined). When using small antennas (with an area of up to tens of square meters) for pulsar navigation, the energy of the pulsar signals received within a few minutes is extremely insufficient to obtain the required level of SNR at the output of the receiver to form TOA estimation, ensuring positioning accuracy up to tens of kilometers. This is one of the scientific tasks that is solved in the paper by studying the relationship between the SNR of the receiver output, which depends on the size of the antenna, the type of signal processing, and the magnitude of the TOA accuracy estimate. The second scientific task that is solved in the paper is the adaptation of all the possible approaches and algorithms suggested in the statistical theory of radars in the suggested signal algorithm for antenna processing and to evaluate the parameters of the TOA and DS pulsar signals, in order to increase the SNR ratio at the receiver output, while preserving the dimensions of the antenna. In this paper, the functional structure of signal processing in a pulsar transport navigation system is proposed, and the choice of the observed second and millisecond pulsars for obtaining a more accurate TOA estimate is discussed. The proposed estimates of positioning accuracy (TOA only, no phase) in an autonomous pulsar vehicle navigation system would only be suitable for the navigation of large vehicles (sea, air, or land) that do not require accurate navigation at sea, air, or desert. Large-sized antennas with an area of tens of square meters to hundreds of square meters can be installed in such vehicles.

A Curbside Parking Planning and Searching Navigation System for Autonomous Vehicles

A Curbside Parking Planning and Searching Navigation System for Autonomous Vehicles

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.

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.

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.

A review of path following control strategies for autonomous robotic vehicles: Theory, simulations, and experiments

AbstractThis article presents an in‐depth review of path following control strategies that are applicable to a wide range class of marine, ground, and aerial autonomous robotic vehicles. From a control system standpoint, path following can be formulated as the problem of stabilizing a path following error system that describes the dynamics of position and possibly orientation errors of a vehicle with respect to a path, with the errors defined in an appropriate reference frame. In spite of the large variety of path following methods described in the literature we show that, in principle, most of them can be categorized in two groups: stabilization of the path following error system expressed either in the vehicle's body frame or in a frame attached to a “reference point” moving along the path, such as a Frenet‐Serret (F‐S) frame or a Parallel Transport (P‐T) frame. With this observation, we provide a unified formulation that is simple but general enough to cover many methods available in the literature. We then discuss the advantages and disadvantages of each method, comparing them from the design and implementation standpoint. We further show experimental results of the path following methods obtained from field trials testing with under‐actuated and over‐actuated autonomous marine vehicles. In addition, we introduce open‐source Matlab and Gazebo/ROS simulation toolboxes that are helpful in testing path following methods before their integration in the combined guidance, navigation, and control systems of autonomous vehicles.

Characterization, Detection, and Segmentation of Work-Zone Scenes From Naturalistic Driving Data

This paper elucidates the automatic detection and analysis of work zones (construction zones) in naturalistic roadway images. An underlying motivation is to identify locations that may pose a challenge to advanced driver assistance systems (ADAS) or autonomous vehicle navigation systems. We first present an in-depth characterization of work-zone scenes from a custom data set collected from more than a million miles of naturalistic driving data. We then describe two machine learning algorithms based on the ResNet and U-Net architectures. The first approach works in an image classification framework that classifies an image as a work-zone scene or non-work-zone scene. The second algorithm was developed to identify individual components representing evidence of a work zone (signs, barriers, machines, etc.). These systems achieved an [Formula: see text] score of 0.951 for the classification task and an [Formula: see text] score of 0.611 for the segmentation task. We further demonstrate the viability of our proposed models through saliency map analysis and ablation studies. To our knowledge, this is the first study to consider the detection of work zones in large-scale naturalistic data. The systems demonstrate potential for real-time detection of construction zones using forward-looking cameras mounted on automobiles. Such a system can be incorporated with ADAS to assist drivers in navigating through challenging environments such as construction zones, making those areas safer for commuters. The code is available on our GitHub page: https://github.com/VTTI/Segmentation-and-detection-of-work-zone-scenes .

User Interface and AI Navigation System for Autonomous Vehicle

The issue of vehicle accidents due to human error and behavior (rash driving, drink and drive, etc.) is on rising resulting into fatality and economical loss. In order to overcome these issues, the autonomous vehicle is being explored as a probable solution. Driver will be replaced by AI based autonomous system; The main objective is to convert the manual operated EV into autonomous vehicle. The project work is divided into three main parts i.e., Control System for Vehicle Control, AI (Artificial Intelligence) and UI (User Interface). The main objective of User Interface is to give information regarding vehicles health i.e. battery remaining, charging time, distance to be covered, etc. AI will be use as decision making system or we can say brain of the autonomous vehicle. System Control or Vehicle Control System is to control the vehicle’s steering, acceleration and braking system with the help of PID controller while the vehicle is being operated in autonomous mode. The main purpose is to make an autonomous EV for the passengers to go to their selected destination with a single click by selecting destination on display (UI). So the vehicle control is responsible for the vehicle’s steering i.e., at what desired angle the vehicle need to turn, during acceleration the factors that are considered are; at what speed the vehicle needs to move in an straight path or during a turn and the braking system is operated with the help of LIDAR where if it detects an object at what distance it needs to apply the brakes and when to release the brakes. All the factors and conditions required in developing the autonomous vehicle will be considered, as use cases to improve the accuracy of autonomous vehicle, resulting into achieving desired objective of safe travel.

A numerical and experimental study on the obstacle collision avoidance system using a 2D LiDAR sensor for an autonomous surface vehicle

In recent years, great efforts have been made to develop unmanned navigation systems for Autonomous Surface Vehicles (ASVs), while further improvements are still required for ASVs operating around obstacles. In this circumstance, this paper presents an Obstacle Collision Avoidance Guidance (OCAG) using obstacle detection with a two-dimensional (2D) LiDAR sensor. After verifying the algorithm of the OCAG in a numerical manoeuvring simulation, the OCAS was embedded in a physical model of a Catamaran-type ASV with a 2D LiDAR sensor. When the ASV sails along with a predefined global path, the LiDAR sensor detects the obstacles, the collision-avoidance actions are determined based on multiple factors including the motion and orientation information of the vehicle, the measured distance and direction information of the obstacles, as well as the types of the obstacles. The proposed OCAG showed good collision-avoidance performances with different obstacle types along the path.

Autonavi3at Software Interface to Autonomously Navigate on Urban Roads Using Omnidirectional Vision and a Mobile Robot


 
 
 
 
 
 
 The design of efficient autonomous navigation systems for mobile robots or autonomous vehicles is fundamental to perform the programmed tasks. Basically, two kind of sensors are used in urban road following: LIDAR and cameras. LIDAR sensors are highly accurate but expensive and extra work is needed for human understanding of the point cloud scenes; however, visual content is understood better by human beings, which should be used to develop human-robot interfaces. In this work, a computer vision-based urban road following software tool called AutoNavi3AT for mobile robots and autonomous vehicles is presented. The urban road following scheme proposed in AutoNavi3AT uses vanishing point estimation and tracking on panoramic images to control the mobile robot heading on the urban road. To do that, Gabor filters, region growing, and particle filters were used. In addition, laser range data are also employed for local obstacle avoidance. Quantitative results were achieved using two kind of tests, one uses datasets acquired at the Universidad del Valle campus, and field tests using a Pioneer 3AT mobile robot. As a result, important improvements in the vanishing point estimation of 68.26 % and 61.46 % in average were achieved, which is useful for mobile robots and autonomous vehicles when they are moving on urban roads.
 
 
 
 
 
 

A Seamless Navigation System and Applications for Autonomous Vehicles Using a Tightly Coupled GNSS/UWB/INS/Map Integration Scheme

Seamless positioning systems for complex environments have been a popular focus of research on positioning safety for autonomous vehicles (AVs). In particular, the seamless high-precision positioning of AVs indoors and outdoors still poses considerable challenges and requires continuous, reliable, and high-precision positioning information to guarantee the safety of driving. To obtain effective positioning information, multiconstellation global navigation satellite system (multi-GNSS) real-time kinematics (RTK) and an inertial navigation system (INS) have been widely integrated into AVs. However, integrated multi-GNSS and INS applications cannot provide effective and seamless positioning results for AVs in indoor and outdoor environments due to limited satellite availability, multipath effects, frequent signal blockages, and the lack of GNSS signals indoors. In this contribution, multi-GNSS-tightly coupled (TC) RTK/INS technology is developed to solve the positioning problem for a challenging urban outdoor environment. In addition, ultrawideband (UWB)/INS technology is developed to provide accurate and continuous positioning results in indoor environments, and INS and map information are used to identify and eliminate UWB non-line-of-sight (NLOS) errors. Finally, an improved adaptive robust extended Kalman filter (AREKF) algorithm based on a TC integrated single-frequency multi-GNSS-TC RTK/UWB/INS/map system is studied to provide continuous, reliable, high-precision positioning information to AVs in indoor and outdoor environments. Experimental results show that the proposed scheme is capable of seamlessly guaranteeing the positioning accuracy of AVs in complex indoor and outdoor environments involving many measurement outliers and environmental interference effects.

Deep and Transfer Learning Approaches for Pedestrian Identification and Classification in Autonomous Vehicles

Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in individuals being harmed. In this work, a review of the convolutional neural networks (CNN) to tackle pedestrian detection is presented. We further present models based on CNN and transfer learning. The CNN model with the VGG-16 architecture is further optimised using the transfer learning approach. This paper demonstrates that the use of image augmentation on training data can yield varying results. In addition, a pre-processing system that can be used to prepare 3D spatial data obtained via LiDAR sensors is proposed. This pre-processing system is able to identify candidate regions that can be put forward for classification, whether that be 3D classification or a combination of 2D and 3D classifications via sensor fusion. We proposed a number of models based on transfer learning and convolutional neural networks and achieved over 98% accuracy with the adaptive transfer learning model.

Road Curb Detection: A Historical Survey.

Curbs are used as physical markers to delimit roads and to redirect traffic into multiple directions (e.g., islands and roundabouts). Detection of road curbs is a fundamental task for autonomous vehicle navigation in urban environments. Since almost two decades, solutions that use various types of sensors, including vision, Light Detection and Ranging (LiDAR) sensors, among others, have emerged to address the curb detection problem. This survey elaborates on the advances of road curb detection problems, a research field that has grown over the last two decades and continues to be the ground for new theoretical and applied developments. We identify the tasks involved in the road curb detection methods and their applications on autonomous vehicle navigation and advanced driver assistance system (ADAS). Finally, we present an analysis on the similarities and differences of the wide variety of contributions.