Search Results (20)

The advancement of Artificial Intelligence (AI) and the Internet of Things (IoT) has accelerated the development of Intelligent Transportation Systems (ITS) in smart cities, playing a crucial role in optimizing traffic flow, enhancing road safety, and improving the driving experience. With urban traffic becoming increasingly complex, timely detection and response to congestion and accidents are critical to ensuring safety and situational awareness. This paper presents Passable, an intelligent and adaptive traffic light control system that monitors traffic conditions in real time using deep learning and computer vision. By analyzing images captured from cameras at traffic lights, Passable detects road incidents and dynamically adjusts signal timings based on current vehicle density. It also employs wireless communication to alert drivers and update a centralized dashboard accessible to traffic management authorities. A working prototype integrating both hardware and software components was developed and evaluated. Results demonstrate the feasibility and effectiveness of designing an adaptive traffic signal control system that integrates incident detection, instantaneous communication, and immediate reporting to the relevant authorities. Such a design can enhance traffic efficiency and contribute to road safety. Future work will involve testing the system with real-world vehicular communication technologies on multiple coordinated intersections while integrating pedestrian and emergency vehicle detection. Full article

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article

This paper aims to solve the problems of poor mobility, passability, and stability in heavy-duty vehicles, and proposes an active suspension system control strategy based on position–force feedback optimal control to coordinately enhance vehicle passability and wheel grounding performance. Firstly, a two-degrees-of-freedom one-sixth vehicle active suspension model and a valve-controlled hydraulic actuator system model are constructed, and the advantages of impedance control in robot compliance control are integrated to analyze their applicability in hydraulic active suspension. Next, a position feedback controller and force feedback LQG optimal controller for fuzzy PID control are designed, the fuzzy PID-LQG (FPL) integrated method is applied to the hydraulic active suspension system, and the dynamic load of the wheel is tracked by impedance control to obtain the spring mass displacement correction. Then, a suspension system model under the excitation of a C-class road surface and a 0.11 m raised road surface is constructed, and the dynamic simulation and comparison of active/passive suspension systems are carried out. The results show that, compared with PS and LQR control, the body vertical acceleration, suspension dynamic deflection, and wheel dynamic load root-mean-square value of the proposed FPL integrated control active suspension are reduced, which can effectively reduce the body vibration and wheel dynamic load and meet the design objectives proposed in this paper, effectively improving vehicle ride comfort, handling stability, passability, and wheel grounding performance. Full article

To tackle the energy-saving optimization issue of plug-in hybrid electric trucks traversing multiple traffic light intersections continuously, this paper presents a double-layer energy management strategy that utilizes the dynamic programming–twin delayed deep deterministic policy gradient (DP-TD3) algorithm to synergistically optimize the speed planning and energy management of plug-in hybrid electric trucks, thereby enhancing the vehicle’s passability through traffic light intersections and fuel economy. In the upper layer, the dynamic programming (DP) algorithm is employed to create a speed-planning model. This model effectively converts the nonlinear constraints related to the position, phase, and timing information of each traffic signal on the road into time-varying constraints, thereby improving computational efficiency. In the lower layer, an energy management model is constructed using the twin delayed deep deterministic policy gradient (TD3) algorithm to achieve optimal allocation of demanded power through the interaction of the TD3 agent with the truck environment. The model’s validity is confirmed through testing on a hardware-in-the-loop test machine, followed by simulation experiments. The results demonstrate that the DP-TD3 method proposed in this paper effectively enhances fuel economy, achieving an average fuel saving of 14.61% compared to the dynamic programming–charge depletion/charge sustenance (DP-CD/CS) method. Full article

This study aims to evaluate the feasibility of using non-contact data collection methods—specifically, UAV (unmanned aerial vehicle)-based and terrestrial laser scanning technologies—to assess forest stand passability, which is crucial for military operations. The research was conducted in a mixed forest stand in the Březina military training area, where the position of trees and their DBHs (Diameter Breast Heights) were recorded. The study compared the effectiveness of different methods, including UAV RGB imaging, UAV-LiDAR, and handheld mobile laser scanning (HMLS), in detecting tree positions and estimating DBH. The results indicate that HMLS data provided the highest number of detected trees and the most accurate positioning relative to the reference measurements. UAV-LiDAR showed better tree detection compared to UAV RGB imaging, though both aerial methods struggled with canopy penetration in densely structured forests. The study also found significant variability in DBH estimation, especially in complex forest stands, highlighting the challenges of accurate tree detection in diverse environments. The findings suggest that while current non-contact methods show promise, further refinement and integration of data sources are necessary to improve their applicability for assessing forest passability in military or rescue contexts. Full article

The development of autonomous vehicles has grown significantly recently due to the promise of improving safety and productivity in cities and industries. The scene perception module has benefited from the latest advances in computer vision and deep learning techniques, allowing the creation of more accurate and efficient models. This study develops and evaluates semantic segmentation models based on a bilateral architecture to enhance the detection of traversable areas for autonomous vehicles on unstructured routes, particularly in datasets where the distinction between the traversable area and the surrounding ground is minimal. The proposed hybrid models combine Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and Multilayer Perceptron (MLP) techniques, achieving a balance between precision and computational efficiency. The results demonstrate that these models outperform the base architectures in prediction accuracy, capturing distant details more effectively while maintaining real-time operational capabilities. Full article

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments. Full article

Aiming at the problems of poor passability of a tracked chassis due to small plots, complicated road conditions and steep slopes during mechanized potato harvesting in hilly and mountainous areas. To design a potato combine harvester and take the tracked chassis of the harvester as the research object to study its driving performance under typical road conditions in mountainous areas. Firstly, mechanical analysis and theoretical calculation are carried out on the tracked chassis to obtain the relevant parameters of key components, and secondly, its driving performance is analyzed to obtain the driving limit values of passing performance under different working conditions; RecurDyn software was used to establish the dynamics simulation and analysis model of the whole machine, and the driving limit values of the harvester were determined under five different road conditions through simulation. The results show the following: the driving limit gradient angle is 20° in cross-gradient conditions, 25° in longitudinal up-gradient conditions, 25° in longitudinal down-gradient conditions, the limiting height of the chassis that can be overpassed in obstacle-crossing conditions is 450 mm, and the limiting width of the chassis that can be spanned in trench-crossing conditions is 1200 mm. The simulation results were verified through field tests, and the results of the field tests showed that the harvester met the requirements of stable travelling on longitudinal slopes of 24°, climbed over a 450 mm straight wall and crossed over a 1200 mm trench, which were similar to the simulation results, indicating that the simulation results were accurate and feasible, and met the design requirements for the travelling passage of the crawler potato harvester. This study provides an in-depth understanding of the tracked chassis of the potato combine harvester in hilly mountainous areas, with a view to providing reference for the design of tracked harvesters for other crops in hilly mountainous areas. Full article

Multi-axle distributed electric drive heavy-duty vehicles have the characteristics of high transmission efficiency, strong maneuverability, and good passability, making them widely used in large cargo transportation. However, the current driving torque control strategies of multi-axle distributed electric drive heavy-duty vehicles lack comprehensive consideration of their longitudinal and lateral dynamic characteristics, making it difficult to comprehensively optimize multiple performances such as power economy, comfort, and stability. In order to solve the above problems, This work focuses on a five-axle distributed electric drive heavy-duty vehicle. Firstly, given the differences in dynamics between two-axle vehicles and multi-axle vehicles, the dynamic model of the multi-axle distributed electric drive heavy-duty vehicle and its critical components is constructed. Then, by analyzing the characteristics of power economy, comfort, and stability of the multi-axle distributed electric drive heavy-duty vehicle, an optimal driving torque control strategy based on multiple performance coordination is proposed. Finally, on the hardware-in-the-loop (HiL) platform, the performance of the optimal driving torque control strategy proposed in this paper is verified by using the China Heavy-Duty Commercial Vehicle Test Cycle for Truck (CHTC-HT) and a straight-line acceleration driving condition on a split friction road. The simulation test results show that, compared with the traditional torque average distribution strategy, the proposed optimal driving torque control strategy can reduce the energy consumption rate by 3.45% in CHTC-HT. The strategy is attributed to the driving torque distribution based on the vehicle’s optimal instantaneous energy consumption, and vehicle comfort is also ensured by the driving mode switching frequency suppression. Subsequently, the vehicle’s stability on the split friction road is effectively improved by the torque coordination compensation strategy. This control strategy significantly improves the comprehensive performance of multi-axle distributed electric drive heavy-duty vehicles. Full article

In this study, we proposes a humanoid dual-arm explosive ordnance disposal (EOD) robot design. First, a seven-degree-of-freedom high-performance collaborative and flexible manipulator is developed, aiming at the transfer and dexterous operation of dangerous objects in EOD tasks. Furthermore, an immersive operated humanoid dual-arm dexterous explosive disposal robot (FC-EODR) is designed, which has a high passability to complex terrains such as low walls, slope roads, and stairs. It can remotely detect, manipulate, and remove explosives in dangerous environments through immersive velocity teleoperation. In addition, an autonomous tool-changing system is constructed, which enables the robot to flexibly switch between different tasks. The effectiveness of the FC-EODR is finally verified through a series of experiments, including the platform performance test, manipulator load test, teleoperated wire trimming, and screw-screwing experiments. This letter provides the technical foundation for robots to replace humans in EOD tasks and emergency situations. Full article

Skid-steered wheeled vehicles can be applied in military, agricultural, and other fields because of their flexible layout structure and strong passability. The research and application of vehicles are developing towards the direction of “intelligent” and “unmanned”. As essential parts of unmanned vehicles, the motion planning and control systems are increasingly demanding for model and road parameters. In this paper, an estimation method for tire and road parameters is proposed by combining offline and online identification. Firstly, a 3-DOF nonlinear dynamic model is established, and the interaction between tire and road is described by the Brush nonlinear tire model. Then, the horizontal and longitudinal stiffness of the tire is identified offline using the particle swarm optimization (PSO) algorithm with adaptive inertia weight. Referring to the Burckhardt adhesion coefficient formula, the extended forgetting factor recursive least-squares (EFRLS) method is applied to identify the road adhesion coefficient online. Finally, the validity of the proposed identification algorithm is verified by TruckSim simulation and real vehicle tests. Results show that the relative error of the proposed algorithm can be well controlled within 5%. Full article

The ability of an off-road robot to traverse obstacles determines whether the robot can complete complex environmental tasks. In order to improve the off-road ability of off-road robots, this paper proposes a new design idea, in which four hub motors are the power system of the robot, the steering system of the robot is composed of a steering machine and a stepping motor, and a five degree of freedom robot model is established. The body structure is designed according to the characteristics of arthropods. The body structure is divided into three modules, and the connecting rod is used as the joint system of the robot to connect the three parts. The body can deform when facing complex obstacles, so as to adapt to different terrains. Then the body structure is simplified, and a mathematical model is established to describe the mathematical relationship between body joint changes. In order to verify the ability of the adaptive all-terrain cross-country robot to traverse obstacles, the load-bearing experiment and obstacle-crossing simulation experiment were carried out through Adams software, and the continuous traversing performance at low obstacles and the ability to break through high obstacles were tested, respectively. The experimental results prove that the designed adaptive all-terrain off-road robot is feasible, has good carrying capacity, and has good passability in the face of low obstacles and high obstacles. Using Ansys software to perform finite element analysis on the wheel connection, the experimental results show that the strength meets the material strength requirements. Finally, a real vehicle test is carried out to verify the correctness of the simulation results. Full article

The article focuses on the assessment and evaluation of the passability in densely populated parts of cities with multi-storey housing estates, in terms of the operation of the integrated rescue system (IRS) in the Czech Republic. The aim of the research is to minimize the arrival times to conduct the intervention as efficiently as possible. The presented problem is caused by unsystematic development of housing estates and the emergence of secondary problems in the form of inability to reach the place of intervention by the larger IRS vehicles. The vision presented in this document presents a systematic approach to improve the serviceability of individual blocks of flats. The main aim is to ensure passability, even for the largest equipment, such as fire engine ladders. Detailed mapping of the selected sites by drones, construction of their digital model, and subsequent virtual verification of the passability by specific vehicle models on identified access roads was performed. The results obtained by this procedure can then be implemented in the navigation of the fire safety forces and facilitate their arrival at the site of intervention. At the end, specific ways are presented in which the whole system can be modified to be able to intuitively change and choose individual access routes in real time, based on the current situation in the area. Full article

The determination of the route of movement is a key factor which enables navigation. In this article, the authors present the methodology of using different resolution terrain passability maps to generate graphs, which allow for the determination of the optimal route between two points. The routes are generated with the use of two commonly used pathfinding algorithms: Dijkstra’s and A-star. The proposed methodology allows for the determination of routes in various variants—A more secure route that avoids all terrain obstacles with a wide curve, or a shorter route, which is, however, more difficult to pass. In order to achieve that, two functions that modify the value of the index of passability (IOP), which is assigned to the primary fields that the passability map consists of, have been used. These functions have a β parameter that augments or reduces the impact of the applied function on IOP values. The paper also shows the possibilities of implementation of the methodology for the movement of single vehicles or unmanned ground vehicles (UGVs) by using detailed maps as well as for determining routes for large military operational units moving in a 1 km wide corridor. The obtained results show that the change in β value causes the change of a course of the route as expected and that Dijkstra’s algorithm is more stable and slightly faster than A-star. The area of application of the presented methodology is very wide because, except for planning the movement of unmanned ground vehicles or military units of different sizes, it can be used in crisis management, where the possibility of reaching the area outside the road network can be of key importance for the success of the salvage operation. Full article

A flood is an overflow of water that swamps dry land. The gravest effects of flooding are the loss of human life and economic losses. An early warning of these events can be very effective in minimizing the losses. Social media websites such as Twitter and Facebook are quite effective in the efficient dissemination of information pertinent to any emergency. Users on these social networking sites share both textual and rich content images and videos. The Multimedia Evaluation Benchmark (MediaEval) offers challenges in the form of shared tasks to develop and evaluate new algorithms, approaches and technologies for explorations and exploitations of multimedia in decision making for real time problems. Since 2015, the MediaEval has been running a shared task of predicting several aspects of flooding and through these shared tasks, many improvements have been observed. In this paper, the classification framework VRBagged-Net is proposed and implemented for flood classification. The framework utilizes the deep learning models Visual Geometry Group (VGG) and Residual Network (ResNet), along with the technique of Bootstrap aggregating (Bagging). Various disaster-based datasets were selected for the validation of the VRBagged-Net framework. All the datasets belong to the MediaEval Benchmark Workshop, this includes Disaster Image Retrieval from Social Media (DIRSM), Flood Classification for Social Multimedia (FCSM) and Image based News Topic Disambiguation (INTD). VRBagged-Net performed encouraging well in all these datasets with slightly different but relevant tasks. It produces Mean Average Precision at different levels of 98.12, and Average Precision at 480 of 93.64 on DIRSM. On the FCSM dataset, it produces an F1 score of 90.58. Moreover, the framework has been applied on the dataset of Image-Based News Topic Disambiguation (INTD), and exceeds the previous best result by producing an F1 evaluation of 93.76. The VRBagged-Net with a slight modification also ranked first in the flood-related Multimedia Task at the MediaEval Workshop 2020. Full article