Vehicle Navigation Research Articles

Preliminary insights on fast GNSS signal capture using SFT and FFT frequency shift

Global Navigation Satellite Systems (GNSS) are the cornerstone of modern navigation and positioning technology. In this paper, we introduce an innovative approach to GNSS signal acquisition, specifically designed to enhance the performance and speed of the signal lock-on process with real-world applications in mind. Our proposed algorithm leverages advanced mathematical tools, including Fast Fourier Transform (FFT) and Inverse FFT, to streamline the acquisition process. Notably, it eliminates the need for an Intermediate Frequency (IF) down mixer, simplifying hardware and potentially reducing power consumption in GNSS receivers. One key breakthrough is using the FFT's "circle shift" to handle Doppler frequency offsets resulting from relative satellite-receiver motion. This method significantly reduces the computational burden during Doppler frequency search, ensuring a faster and more efficient lock-on to satellite signals. Furthermore, we exploit the sparse nature of GNSS signals in the reverse FFT calculation, utilizing a Sparse Fourier Transform (SFT) for efficient processing. This innovation allows quicker reverse FFT computations, pivotal in the acquisition process. Through extensive simulations, we demonstrate the superior performance of our improved algorithm. Its speed and reliability make it well-suited for many practical GNSS applications, such as vehicle navigation, precision agriculture, surveying, and geolocation services. By enhancing acquisition efficiency, our approach contributes to more accurate, responsive, and energy-efficient GNSS positioning, benefiting users in both civilian and industrial sectors.

Terrain detection and segmentation for autonomous vehicle navigation: A state-of-the-art systematic review

This review comprehensively investigates the current state and emerging trends of autonomous vehicle terrain detection and segmentation. By systematically reviewing literature from various databases, this study outlines the evolution of detection and segmentation techniques from traditional computer vision methods to advanced machine learning and deep learning approaches. It identifies critical technological advancements, evaluates their performance, and discusses the challenges faced under various environmental conditions, data acquisition, and integration with vehicle systems. This study also highlights the need for standardized benchmarks and datasets to facilitate the development and testing of robust terrain detection systems. This review encompasses terrain detection and segmentation in structured environments, such as urban roads and highways, and unstructured environments, including rural paths and off-road terrains, to comprehensively analyze autonomous vehicle navigation challenges. By analyzing recent research findings, this review provides insights into future directions for overcoming these limitations and fostering innovation in the autonomous driving domain.

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.

A novel EMS design framework for SPHTs based on instantaneous layer, driving event layer, and driving cycle layer

To effectively harness the energy-saving potential of series–parallel hybrid transmissions (SPHTs) with multiple gears and modes and to enhance the driving cycle adaptability of the rule-based energy management strategy (RB EMS), this study establishes a novel EMS design framework for SPHTs at three levels: the instantaneous, the driving event, and the driving cycle layers. Firstly, an equivalent fuel factor based on the principles of energy conservation and calorific value measurements is constructed, the energy consumption of different working modes and torque combinations under different instantaneous power units is determined, and the decision conditions of working modes are determined. Secondly, the energy consumption of engine and motor torque combination under constant speed, acceleration and deceleration driving events is compared, and the torque distribution feature of multi-power sources is determined. Thirdly, a driving cycle distribution factor is introduced and calculated using vehicle navigation map information, which can adjust the gear switching condition of parallel driving mode online. Based on above, an RB EMS adjusted with driving cycles (ADC-RB EMS) is proposed. The effectiveness of the proposed strategy is then verified through real-world vehicle tests. The fuel consumption performance of this strategy falls between the RB EMS and the dynamic programming (DP) strategy. According to the fuel consumption results, the RB EMS consumes an additional 0.35 L/100 km of fuel, while the DP strategy saves only 3.66% more energy compared to this strategy. The application potential of driving cycle distribution factor in instantaneous optimal control is tested. Compared with ADC-RB and CD-CS, the fuel saving rate is 1.71% and 5.67% respectively, which proves the energy saving performance of A-ECMS. This study provides a development direction for improving the energy saving effect of the RB EMS.

Enhanced Vision-Based Taillight Signal Recognition for Analyzing Forward Vehicle Behavior.

This study develops a vision-based technique for enhancing taillight recognition in autonomous vehicles, aimed at improving real-time decision making by analyzing the driving behaviors of vehicles ahead. The approach utilizes a convolutional 3D neural network (C3D) with feature simplification to classify taillight images into eight distinct states, adapting to various environmental conditions. The problem addressed is the variability in environmental conditions that affect the performance of vision-based systems. Our objective is to improve the accuracy and generalizability of taillight signal recognition under different conditions. The methodology involves using a C3D model to analyze video sequences, capturing both spatial and temporal features. Experimental results demonstrate a significant improvement in the model's accuracy (85.19%) and generalizability, enabling precise interpretation of preceding vehicle maneuvers. The proposed technique effectively enhances autonomous vehicle navigation and safety by ensuring reliable taillight state recognition, with potential for further improvements under nighttime and adverse weather conditions. Additionally, the system reduces latency in signal processing, ensuring faster and more reliable decision making directly on the edge devices installed within the vehicles.

Multi-Unmanned Aerial Vehicle-Assisted Flood Navigation of Waterborne Vehicles Using Deep Reinforcement Learning

Abstract During disasters, such as floods, it is crucial to get real-time ground information for planning rescue and response operations. With the advent of technology, unmanned aerial vehicles (UAVs) are being deployed for real-time path planning to provide support to evacuation teams. However, their dependency on expert human pilots for command and control limits their operational capacity to the line-of-sight range. In this article, we utilize a deep reinforcement learning algorithm to autonomously control multiple UAVs for area coverage. The objective is to identify serviceable paths for safe navigation of waterborne evacuation vehicles (WBVs) to reach critical location(s) during floods. The UAVs are tasked to capture the obstacle-related data and identify shallow water regions for unrestricted motion of the WBV(s). The data gathered by UAVs is used by the minimum expansion A* (MEA*) algorithm for path planning to assist WBV(s). MEA* addresses the node expansion issue with the standard A* algorithm, by pruning the unserviceable nodes/locations based on the captured information, hence expediting the path planning process. The proposed approach, MEA*MADDPG, is compared with other prevalent techniques from the literature over simulated flood environments with moving obstacles. The results highlight the significance of the proposed model as it outperforms other techniques when compared over various performance metrics.

Application of Atmospheric Augmentation for PPP-RTK with Instantaneous Ambiguity Resolution in Kinematic Vehicle Positioning

The long convergence time and non-robust positioning accuracy are the main factors limiting the application of precision single-point positioning (PPP) in kinematic vehicle navigation. Therefore, a dual/triple-frequency multi-constellation PPP-RTK method with atmospheric augmentation is proposed to achieve cm-level reliable kinematic positioning. The performance was assessed using a set of static station and kinematic vehicle positioning experiments conducted in Wuhan. In the static experiments, instantaneous convergence within 1 s and centimeter-level positioning accuracy were achieved for PPP-RTK using dual-frequency observation. For the kinematic experiments, instantaneous convergence was also achieved for dual-frequency PPP-RTK in open areas, with RMS of 2.6 cm, 2.6 cm, and 7.5 cm in the north, east, and up directions, respectively, with accuracy similar to short-baseline real-time kinematic positioning (RTK). Horizontal positioning errors of less than 0.1 m and 3D positional errors of less than 0.2 m were 99.54% and 98.46%, respectively. Additionally, after the outage of GNSS and during satellite reduction in obstructed environments, faster reconvergence and greater accuracy stability were realized compared with PPP without atmospheric enhancement. Triple-frequency PPP-RTK was able to further enhance the robustness and accuracy of positioning, with RMS of 2.2 cm, 2.0 cm, and 7.3 cm, respectively. In summary, a performance similar to RTK was achieved based on dual-frequency PPP-RTK, demonstrating that PPP-RTK has the potential for lane-level navigation.

Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation

Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation

Tight integration of real-time GNSS PPP and INS: Ionosphere-free combined vs uncombined models

The GNSS Precise Point Positioning (PPP) model is usually established in either an ionospheric-free (IF) combined form or an uncombined (UC) form. These formulations can be equivalent in theory but their applications in practice could still perform differently when integrated with external sensors. In this study, we compared the positioning performance of the two PPP models tightly coupled with the Inertial Navigation System (INS) using a high-grade inertial measurement unit (IMU) in real vehicle navigation tests. The ambiguity resolution (AR) was also exploited in the two PPP models after applying the observable specific biases (OSB) to the GNSS raw code and phase measurements. According to the results, under good satellite observability the UC PPP/INS tightly-coupled integration (TCI) significantly outperforms the IF PPP/INS TCI. The UC TCI model with AR could achieve a positioning accuracy of 4.6 and 3.0 cm in the horizontal and vertical directions, which are improved by 37 % and 63 % respectively relative to the IF TCI model. However, in the case of frequent GNSS signal interruptions or poor satellite observation condition, the IF TCI model shows a superior reliability than the UC TCI. Nevertheless, when the ionospheric parameters are properly constrained in the UC TCI model, substantial improvements in terms of convergence and accuracy are obtained. The UC PPP augmented with external precise ionospheric information would greatly increase the cost, and users may select the appropriate PPP model with INS TCI in real applications in accordance with the demanded accuracy level and measuring conditions.

Challenges in the Guidance, Navigation and Control of Autonomous and Transport Vehicles

In recent years, autonomous and transportation vehicles have been evolving towards more electric or hybrid electric systems, with new challenges arising in their guidance, navigation and control (GNC) systems [...]

A novel model for real-time risk evaluation of vehicle–pedestrian interactions at intersections

Safety decisions for vehicles at an intersection rely on real-time, objective and continuous assessment of risks in vehicle–pedestrian interactions. Existing surrogate safety models, constrained by ideal assumptions of constant current speed and reliant on interaction points, often misjudge risks, and show inefficiency, inaccuracy and discontinuity. This work proposes a novel model for evaluation of those risks in vehicle–pedestrian interactions at intersections, which abstracts the pedestrian distribution density around a vehicle into a generalized model of driver-pedestrian interaction preferences. The introduction of two conceptions: ’driving risk index’ and ’driving risk gradient,’ facilitates the delineation of driving spaces for identifying safety–critical events. By means of the trajectory data from three intersections, model parameters are calibrated and a multidimensional vehicle–pedestrian interaction risk (VPIR) model is proposed to adapt the complex and dynamic characteristics of vehicle–pedestrian interactions at intersections. Commonly used surrogate safety models, such as Time to Collision (TTC), are selected as benchmark models. Results show that the proposed model overcomes the limitations of the existing interaction-point-based models, and offers a ideal assessment of driving risks at intersections. Finally, the model is illustrated with a case study that assesses the risks in vehicle–pedestrian interactions in varied scenarios and the case study indicates that the VPIR model works well in evaluating vehicle–pedestrian interaction risks. This work can facilitate humanoid learning in the autonomous driving domain, and achieve an ideal evaluation of vehicle–pedestrian interaction risks for safe and efficient vehicle navigation through an intersection.

Hybrid Path Planning Strategy Based on Improved Particle Swarm Optimisation Algorithm Combined with DWA for Unmanned Surface Vehicles

Path planning is one of the core issues in the autonomous navigation of an Unmanned Surface Vehicle (USV), as the accuracy of the results directly affects the safety of the USV. Hence, this paper proposes a USV path planning algorithm that integrates an improved Particle Swarm Optimisation (PSO) algorithm with a Dynamic Window Approach (DWA). Firstly, in order to advance the solution accuracy and convergence speed of the PSO algorithm, a nonlinear decreasing inertia weight and adaptive learning factors are introduced. Secondly, in order to solve the problem of long path and path non-smoothness, the fitness function of PSO is modified to consider both path length and path smoothness. Finally, the International Regulations for Preventing Collisions at Sea (COLREGS) are utilised to achieve dynamic obstacle avoidance while complying with maritime practices. Numerical cases verify that the path planned via the proposed algorithm is shorter and smoother, guaranteeing the safety of USV navigation while complying with the COLREGS.

Enhancing autonomous vehicle navigation using SVM-based multi-target detection with photonic radar in complex traffic scenarios

Efficient transportation systems are essential for the development of smart cities. Autonomous vehicles and Intelligent Transportation Systems (ITS) are crucial components of such systems, contributing to safe, reliable, and sustainable transportation. They can reduce traffic congestion, improve traffic flow, and enhance road safety, thereby making urban transportation more efficient and environmentally friendly. We present an innovative combination of photonic radar technology and Support Vector Machine classification, aimed at improving multi-target detection in complex traffic scenarios. Central to our approach is the Frequency-Modulated Continuous-Wave photonic radar, augmented with spatial multiplexing, enabling the identification of multiple targets in various environmental conditions, including challenging weather. Notably, our system achieves an impressive range resolution of 7 cm, even under adverse weather conditions, utilizing an operating bandwidth of 4 GHz. This feature is particularly crucial for precise detection and classification in dynamic traffic environments. The radar system's low power requirement and compact design enhance its suitability for deployment in autonomous vehicles. Through comprehensive numerical simulations, our system demonstrated its capability to accurately detect targets at varying distances and movement states, achieving classification accuracies of 75% for stationary and 33% for moving targets. This research substantially contributes to ITS by offering a sophisticated solution for obstacle detection and classification, thereby improving the safety and efficiency of autonomous vehicles navigating through urban environments.

Research on multi-path optimization problem based on particle swarm optimization algorithm

The problem of finding the optimal path within a certain range exists in various scenarios in our lives, such as the traveling salesman problem, robot automatic path selection problem, vehicle and pedestrian navigation, game path navigation, communication routing, logistics and transportation planning problems, etc. , through the optimization of path problems, we can help people improve resource utilization, improve work efficiency, reduce production costs, or complete the goal of a specific scenario, etc. Therefore, solving the path optimization problem is a very important task in our reality. In order to improve the game experience of the players in the game, this paper studies how to use the particle swarm optimization method to solve the optimal route of NPC.

Real-time path planning for autonomous vehicle off-road driving.

Autonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and actuation, communication and safety measures, among others. In this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning. The reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.

Tightly coupled integration of vector HD map, LiDAR, GNSS, and INS for precise vehicle navigation in GNSS-challenging environment

ABSTRACT High-precision positioning and navigation are necessary for autonomous driving. GNSS RTK and INS integrated system is commonly used in vehicular navigation. But it suffers from severe signal reflections and blockages of GNSS signals and error accumulation of INS with MEMS-IMU in GNSS-challenging environment. In addition, high-definition (HD) maps in vector format and light detection and ranging (LiDAR) are two common options for intelligent vehicles. A tightly coupled localization method with vector HD map, LiDAR, GNSS RTK, and INS is proposed to take advantage of their complementary characteristics to accurately navigate a vehicle in a GNSS-challenging environment. The method is based on a particle filter (PF) framework. Lateral positions of particles are constrained by LiDAR measurements and lane information in the vector HD map. A constrained and damped LAMBDA searching method is proposed to update particle weights, aiming to find accurate longitudinal localization. Experimental results prove that our method can maintain sub-meter level horizontal positioning accuracy in GNSS-challenging environment, with improvements of (77%, 75%, 64% and 65%), regarding to three-dimensional position and yaw, compared to traditional GNSS-RTK/INS integration, while the improvement of the popular GNSS-RTK/INS/LiDAR integrated framework LIO-SAM is (16%, 53%, 48% and 51%).

A hybrid information fusion method for SINS/GNSS integrated navigation system utilizing GRU-aided AKF during GNSS outages

Abstract To enhance the performance of integrated inertial navigation system (INS) and global navigation satellite system (GNSS) during GNSS outages, this paper proposed a fusion positioning method based on predictive observation information and adaptive filter parameter. Combined with an adaptive Kalman filter (AKF) and a Gated Recurrent Unit neural network (NN) that directly relates the inertial measurement unit (IMU) output sequence to the error estimation, the hybrid information fusion system can provide effective corrections to compensate for horizontal position errors under the constraints of complex and dynamic vehicle movement data during GNSS outages. Meanwhile, the designed adaptive parameter of the integrated navigation filter can adjust the credibility of the state prediction section when the GNSS is reconnected, ensuring the system can switch rapidly between the INS/GNSS and INS/NN integrated modes. The performance of the proposed information fusion method has been experimentally validated using IMU and GNSS data collected in a vehicle navigation test conducted on a stretch of expressway. The comparison results indicate that the proposed algorithm has error suppression capabilities under various experimental constraints and demonstrates a degree of extendibility and reusability.

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.

An improved SSVEP-based brain-computer interface with low-contrast visual stimulation and its application in UAV control.

Efficient communication and regulation are crucial for advancing brain-computer interfaces (BCIs), with the steady-state visual-evoked potential (SSVEP) paradigm demonstrating high accuracy and information transfer rates. However, the conventional SSVEP paradigm encounters challenges related to visual occlusion and fatigue. In this study, we propose an improved SSVEP paradigm that addresses these issues by lowering the contrast of visual stimulation. The improved paradigms outperform the traditional paradigm in the experiments, significantly reducing the visual stimulation of the SSVEP paradigm. Furthermore, we apply this enhanced paradigm to a BCI navigation system, enabling two-dimensional navigation of unmanned aerial vehicles (UAVs) through a first-person perspective. Experimental results indicate the enhanced SSVEP-based BCI system's accuracy in performing navigation and search tasks. Our findings highlight the feasibility of the enhanced SSVEP paradigm in mitigating visual occlusion and fatigue issues, presenting a more intuitive and natural approach for BCIs to control external equipment.NEW & NOTEWORTHY In this article, we proposed an improved steady-state visual-evoked potential (SSVEP) paradigm and constructed an SSVEP-based brain-computer interface (BCI) system to navigate the unmanned aerial vehicle (UAV) in two-dimensional (2-D) physical space. We proposed a modified method for evaluating visual fatigue including subjective score and objective indices. The results indicated that the improved SSVEP paradigm could effectively reduce visual fatigue while maintaining high accuracy.

Flood risk assessment of the Garita River in the urban zone of San Luis Potosí City, by hydrodynamic modeling

Rapid and uncontrolled urban growth and land use changes in watersheds worldwide have led to increased surface runoff within metropolitan areas, coupled with climate change, creating a risk for residents during the rainy season. The city of San Luis Potosí is no exception to this phenomenon. One affected watercourse is the Garita Stream, which flows inside the city near urbanization. It is essential to analyze the effects of urban sprawl on this stream based on historical precipitation data for the town. Hydrological and topographical information were required to conduct this research. The hydrological study of the basin involved analyzing the region’s geomorphology and historical climatological data. For the stream’s topography, aerial photogrammetry using an unmanned aerial Vehicle (UAV) and Global Navigation Satellite System (GNSS) equipment was employed to conduct topographic surveys in the area. To find out when the Garita stream would overflow and which areas are most likely to flood, numerical modeling was done using 1D, 2D, and 3D programs like SWMM5 (Storm Water Management Model), HEC-RAS (Hydrologic Engineering Center’s River Analysis System), and EDFC Explorer (Environmental Fluid Dynamics Code). These models simulated different return periods and their correlation with current flooding events recorded in the area, thereby further proposing solutions to mitigate overflow issues. By conducting these simulations and analyzing the results, solutions can be suggested to address the overflow problems in the area based on historical flood events at various return periods caused by the Garita Stream.