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There is a growing body of scholarship on the ethics of autonomous vehicles. Yet the ethical discourse has mostly been focusing on the behavior of the vehicle in accident scenarios. This paper offers a different ethical prism: the implications of the autonomous vehicle for human well-being. As such, it contributes to the growing discourse on the wider societal and moral implications of the autonomous vehicle. The paper is premised on the neo-Aristotelian approach which holds that as human beings, our well-being depends on developing and exercising our innate human capacities: to know, understand, love, be sociable, imagine, create and use our bodies and use our willpower. To develop and exercise these capacities, our environments need to provide a range of opportunities which will trigger the development and exercise of the capacities. The main argument advanced in the paper is that one plausible future of the autonomous vehicle—a future of single-rider autonomous vehicles—may effectively reduce the opportunities to develop and exercise our capacities to know, be sociable and use our willpower. It will therefore be bad for human well-being, and this provides us with a moral reason to resist this plausible future and search for alternative ones. [ABSTRACT FROM AUTHOR]

The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles. [ABSTRACT FROM AUTHOR]

The rapid advancement of autonomous vehicle technology has brought into focus the critical need for enhanced road safety systems, particularly in the areas of road damage detection and surface classification. This paper explores these two essential components, highlighting their importance in autonomous driving. In the domain of road damage detection, this study explores a range of deep learning methods, particularly focusing on one-stage and two-stage detectors. These methodologies, including notable ones like YOLO and SSD for one-stage detection and Faster R-CNN for two-stage detection, are critically analyzed for their efficacy in identifying various road damages under diverse conditions. The review provides insights into their comparative advantages, balancing between real-time processing and accuracy in damage localization. For road surface classification, the paper investigates the classification techniques based on both environmental conditions and material road composition. It highlights the role of different convolutional neural network architectures and innovations at the neural level in enhancing classification accuracy under varying road and weather conditions. The main finding of this work is that it offers a comprehensive overview of the current state of the art, showcasing significant strides in utilizing deep learning for road analysis in autonomous vehicle systems. The study concludes by underscoring the importance of continued research in these areas to further refine and improve the safety and efficiency of autonomous driving. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs), as a member of the unmanned intelligent ocean vehicle group, can replace human beings to complete dangerous tasks in the ocean. It is of great significance to apply reinforcement learning (RL) to AUVs to realize intelligent control. This paper proposes an AUV obstacle avoidance framework based on event-triggered reinforcement learning. Firstly, an environment perception model is designed to judge the relative position relationship between the AUV and all unknown obstacles and known targets. Secondly, considering that the detection range of AUVs is limited, and the proposed method needs to deal with unknown static obstacles and unknown dynamic obstacles at the same time, two different event-triggered mechanisms are designed. Soft actor–critic (SAC) with a non-policy sampling method is used. Then, improved reinforcement learning and the event-triggered mechanism are combined in this paper. Finally, a simulation experiment of the obstacle avoidance task is carried out on the Gazebo simulation platform. Results show that the proposed method can obtain higher rewards and complete tasks successfully. At the same time, the trajectory and the distance between each obstacle confirm that the AUV can reach the target well while maintaining a safe distance from static and dynamic obstacles. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant development in unmanned platform technologies, specifically unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). As a result, their application scenarios have expanded considerably. Unmanned platforms are considered integral components of the Internet of Things system. However, certain challenges arise when dealing with specialized tasks, such as navigating complex urban low-altitude terrain with multiple obstacles and limited communication capabilities. These challenges can greatly impact the efficiency of the system due to information isolation. To address this issue, a messenger drone mechanism is introduced in this paper, which utilizes air superiority to facilitate indirect communication between unmanned platforms. Additionally, a task sequence planning algorithm based on sampling transformation is designed. This algorithm efficiently assigns the drone to mobile UGVs by discretely sampling their paths and considering the UAV-UGV motion relationship. By transforming the problem into an asymmetric traveler problem, it allows for a fast solution. Finally, the effectiveness of the algorithm is verified through comparative analysis in different scenarios. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) rely on their perception systems to detect traffic objects, with the uncertainty in detection results significantly influencing the safety of their decision‐making and control mechanisms. This paper introduces a safety potential field for CAVs that accounts for target detection errors. Initially, the paper categorizes errors arising from target detection into classification, labelling, and positioning categories. Subsequently, an elliptical model‐based safety potential field is developed, incorporating potential field line optimization using safety thresholds and lane lines. This approach facilitates the determination of critical values and safety distribution for the potential field. The paper then proceeds with coefficient calibration and experimental analysis to validate the reliability of the proposed model. Findings indicate that as target detection errors increasingly manifest, the safety potential field area for CAVs becomes more restrictive, enhancing the field's sensitivity to these errors. The critical safety value for CAVs is maintained within the range of [0 m, 7 m], providing a stable basis for decision‐making and control. Additionally, the safety value for CAVs falls between [15, 25], favouring the improvement of safety gradient distribution under the calibrated safety potential field values. [ABSTRACT FROM AUTHOR]

Connected Autonomous Vehicle (CAV) dedicated lanes can spatially eliminate the disturbance from Human-Driven Vehicles (HDVs) and increase the probability of vehicle cooperative platooning, thereby enhancing road capacity. However, when the penetration rate of CAVs is low, CAV dedicated lanes may lead to a waste of road resources. This paper proposes a cooperative lane-changing control method for multiple vehicles considering Dynamic Intelligent Connected (DIC) dedicated lanes. Initially, inspired by the study of dedicated bus lanes, the paper elucidates the traffic regulations for DIC dedicated lanes, and two decision-making approaches are presented based on the type of lane-change vehicle and the target lane: CAV autonomous cooperative lane change and HDV mandatory cooperative lane change. Subsequently, considering constraints such as acceleration, speed, and safe headway, cooperative lane-change control models are proposed with the goal of minimizing the weighted sum of vehicle acceleration and lane-change duration. The proposed model is solved by the TOPSIS multi-objective optimization algorithm. Finally, the effectiveness and advancement of the proposed cooperative lane-changing method are validated through simulation using the SUMO software (Version 1.19.0). Simulation results demonstrate that compared to traditional lane-changing models, the autonomous cooperative lane-changing model for CAVs significantly improves the success rate of lane changing, reduces lane-changing time, and causes less speed disturbance to surrounding vehicles. The mandatory cooperative lane-changing model for HDVs results in shorter travel times and higher lane-changing success rates, especially under high traffic demand. The methods presented in this paper can notably enhance the lane-changing success rate and traffic efficiency while ensuring lane-changing safety. [ABSTRACT FROM AUTHOR]

This paper focuses on the obstacle avoidance trajectory planning problem for autonomous vehicles on structured roads. The objective is to design a trajectory planning algorithm that can ensure vehicle safety and comfort and satisfy the rationality of traffic regulations. This paper proposes a path and speed decoupled planning method for non-split vehicle trajectory planning on structured roads. Firstly, the path planning layer adopts the improved artificial potential field method. The obstacle-repulsive potential field, gravitational potential field, and fitting method of the traditional artificial potential field are improved. Secondly, the speed planning aspect is performed in the Frenet coordinate system. Speed planning is accomplished based on S-T graph construction and solving convex optimization problems. Finally, simulation and experimental verification are performed. The results show that the method proposed in this paper can significantly improve the safety and comfortable riding of the vehicle. [ABSTRACT FROM AUTHOR]

With the development of automated vehicles, researches related to automated vehicle platoon (AVP) have received more and more attention. AVP is considered one of the effective means to alleviate traffic congestion and reduce vehicle energy consumption. This paper studies a three‐layer method of avoiding obstacle vehicles in traffic by switching the formation for the automated heavy vehicle platoon. In the decision‐making layer, a decision‐making system based on the finite‐state machine is established for formation switching. In the second layer, the lane‐changing trajectory is optimized based on the quantic polynomial curve fitting for vehicles that need to change lanes. In terms of vehicle control layer, each vehicle has a longitudinal controller based on sliding mode control and a lateral controller based on model predictive control to track the planned trajectory to complete the target formation. Finally, the proposed method is simulated in MATLAB/TruckSim. The simulation results show that the proposed method could effectively avoid the obstacle vehicles by switching the formation and has a small average value of errors in speed tracking and trajectory tracking. [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects. [ABSTRACT FROM AUTHOR]

As urbanization accelerates, traffic congestion and frequent accidents have become prominent issues, prompting the development of intelligent transportation systems. This paper focuses on the research of vehicle and pedestrian detection algorithms to improve detection accuracy in complex traffic environments. Considering the limitations of traditional object detection algorithms in complex situations, this study adopts the deep learning-based YOLOv8 algorithm and introduces the Coordinate Attention (CA) module to enhance the model's feature extraction and localization capabilities. Experimental results show that the improved YOLOv8 network achieves a 1.1% increase in detection accuracy while maintaining its original speed. Furthermore, this paper constructs a vehicle and pedestrian dataset suitable for Chinese traffic scenes, providing an effective solution for autonomous driving assistance systems. Overall, this study holds significant reference value for vehicle and pedestrian detection in the field of intelligent transportation. [ABSTRACT FROM AUTHOR]

Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, and types of roads and their settings. In this work, we look into the other domains and technologies required to build an autonomous vehicle and conduct a relevant literature analysis. In this work, we look into the current state of research and development in environment detection, pedestrian detection, path planning, motion control, and vehicle cybersecurity for autonomous vehicles. We aim to study the different proposed technologies and compare their approaches. For a car to become fully autonomous, these technologies need to be accurate enough to gain public trust and show immense accuracy in their approach to solving these problems. Public trust and perception of auto vehicles are also explored in this paper. By discussing the opportunities as well as the obstacles of autonomous driving technology, we aim to shed light on future possibilities. [ABSTRACT FROM AUTHOR]

On-demand transportation is a mobility service that adapts to the passengers, including the route, location of picking-up and dropping-off, and time. In general, an area with less travel demand is highly suitable for its operation as it moves according to the request only, unlike the typical public transit that operates within a fixed route leading to an inefficient operation and environmental burden. Although the system introduction seems appealing, the operation possibly encounters technical or organizational problems. Therefore, detailed consideration of system operation is mandatory for stakeholders to maintain operation sustainability while simultaneously satisfying the passengers. In this paper, the authors aimed to highlight various challenges of the service and the implemented technologies in the demand-responsive transportation (DRT) system. A systematic literature review with the help of mapping studies was conducted to give broader insights into these research questions: What problems does the already-existing DRT system have and how do researchers propose the solution? What are the main technologies used within the DRT services? What are the possible improvements or innovations for the system? The study will collect and evaluate related papers from the Scopus database to answer the research questions. With the presented information, the paper simultaneously aims to provide insight for other stakeholders on DRT systems for policy consideration and present literature for researchers in this field. In relation to the development opportunities, the study revealed that the integration of DRT systems with micro-mobility services such as bike-sharing and scooters, integration between existing transport services, and autonomous vehicles may elevate the current DRT system. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient tracking accuracy caused by object occlusion in the process of multi-object tracking, this paper proposes a multi-order semantic fusion pedestrian multi-object tracking network. Firstly, the feature pyramid attention module is used in the backbone network to enlarge the receptive field and obtain more abundant feature information to improve the detection accuracy of different scale objects. Secondly, a size-aware module is integrated into the pedestrian re-identification branch network to fuse semantic features from different resolutions and extract more basic pedestrian features, thereby improving the tracking accuracy. Finally, the detection head is reconstructed and the small object detection layer is fused to make the proposed network adapt to objects of different sizes. Experiments on the MOT16 and MOT17 datasets show that the multi-object tracking accuracy of the proposed network reaches 75.4% (MOT16) and 74.3% (MOT17), which effectively deals with the problem of low tracking accuracy caused by occlusion in the field of autonomous driving, and achieves good tracking results. The network proposed in this paper improves the tracking accuracy of pedestrians and provides a basis for further practical applications. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) have emerged as a promising technology for enhancing road safety and mobility. However, designing AVs involves various critical aspects, such as software and system requirements, that must be carefully addressed. This paper investigates safety-aware approaches for AVs, focusing on the software and system requirements aspect. It reviews the existing methods based on software and system design and analyzes them according to their algorithms, parameters, evaluation criteria, and challenges. This paper also examines the state-of-the-art artificial intelligence-based techniques for AVs, as AI has been a crucial element in advancing this technology. This paper reveals that 63% of the reviewed studies use various AI methods, with deep learning being the most prevalent (34%). The article also identifies the current gaps and future directions for AV safety research. This paper can be a valuable reference for researchers and practitioners on AV safety. [ABSTRACT FROM AUTHOR]

This paper introduces the issue of personal and non-personal data in autonomous vehicles. Its main objective is to provide a procedure for determining the processed and retained data, to explain the current legal regulation that must be met, and to propose appropriate recommendations for each data category. Current law (and the regulatory framework which it has produced) generally distinguishes between two basic types of data, i.e. non-personal data and personal data. The phenomenon that a combination of several pieces of non-personal data may, under certain circumstances2, result in processing of personal data, which is subject to significant regulation, is not limited solely to the field of autonomous mobility. This paper deals with data classification in autonomous vehicles, focusing on the challenging cases of non-personal data that can turn into personal data. The initial stage involves identifying the data to be processed, followed by the classification of the identified data. Based on this classification, relevant legal obligations and recommendations are described. The primary objective of this article is to provide guidance to data administrators (or, to use the terminology of data protection laws, data controllers) in autonomous mobility, enabling them to identify data and take appropriate measures to ensure compliance with legal regulations. The ultimate aim is to ensure that data controllers adhere to legal requirements, a goal that is critical for all data controllers. Beyond that, this article should be relevant also for other stakeholders in the autonomous mobility ecosystem, such as vehicle manufacturers and their suppliers, software application and service providers, and others. [ABSTRACT FROM AUTHOR]

Scene-flow (SF) estimation is considered to be one of the most fundamental problems in scene understanding and autonomous control. The majority of the existing methods adopted for SF estimation suffer lack of robustness in some environments and cannot be easily applied for high-speed applications such as autonomous driving. Although some of the available methods are precise, they include high computational costs or require a GPU. The most serious challenge faced in SF estimation is its inability to strike a balance between speed, precision, robustness, and the computational costs. This paper, therefore, aims at proposing a novel sparse scene-flow (stereo-RSSF) method which is highly distinguished in terms of its faster speed, robustness, and precision using the following: stereo calibrated frames, sparse optical flow such as the LKT algorithm, a new inlier detection module based on spatial correlation analysis, epipolar geometry, and modified circular matching techniques. The comparisons made between stereo-RSSF and several advanced methods indicate that this sparse method has significantly higher accuracy than all the other state-of-the-art methods in the points it estimates. In this paper, the effects of each module and hyper-parameters of stereo-RSSF on the performance and running time are analyzed. Stereo-RSSF has also been evaluated on the KITTI test dataset, and the results have been independently verified by the reference group. The code for our implementation of stereo-RSSF is available at: https://github.com/salehierfan/Stereo-RSSF. [ABSTRACT FROM AUTHOR]

With the development of Mobile Edge Computing, driverless, 5 G, and related techniques, Edge-Cloud based Urban Intelligent Video Systems are extremely promising to support public safety through powerful analysis and timely response. Furtherly, flexible Unmanned Ground Vehicles(UGVs), which are equipped with edge devices, can enhance these edge systems to withstand these abnormalities: natural disasters, abnormal crowd flows, and other emergencies. In this regard, as a critical issue in edge systems, task placement in these systems needs to consider these "mobile" edge nodes: ICVs(UGVs). Therefore, a novel and effective framework named Optimized Centroids K-means based Task Placement framework is proposed: we firstly involve the clustering approach to optimize initial centroids as the positions of ICVs in terms of Edge Nodes, various typical optimization methods can be utilized to place related edge tasks effectively. The experimental results demonstrate that our novel framework has a great improvement over several existing typical strategies and supports multiple optimization methods well in this paper. [ABSTRACT FROM AUTHOR]

To ensure unmanned vehicles can perform a sortie mission quickly, efficiently, safely and reliably after receiving the command, it is necessary to calculate the sortie mission reliability of the shipborne unmanned vehicle group before loading. Aimed at the layout and sortie characteristics of an unmanned vehicle group, a sortie mission network model and a calculation method for sortie mission reliability are designed in this paper. Firstly, this paper uses space partition to parallel search for equal-length minimal paths based on the two-terminal network reliability. Secondly, this paper adopts the sum of disjoint products to process the equal-length minimal path set, innovatively proposing a calculation method for the sortie mission reliability of the shipborne unmanned vehicle group. Finally, the sortie mission reliability for three typical cases was calculated and compared with the Monte Carlo method. The comparative analysis indicates that the proposed method is both accurate and efficient, thereby corroborating its scientific validity and practical effectiveness. This study fills the gap in the field of sortie mission reliability and lays a theoretical foundation for subsequent research. Meanwhile, the method proposed in this paper can also be extended to the reliability calculation of a multiple-vehicle sortie mission in similar enclosed spaces. [ABSTRACT FROM AUTHOR]

In this paper, an innovative method is proposed to improve the global path planning of Unmanned Surface Vehicles (USV) in complex sea areas, combining fluid mechanic calculations with an improved A* algorithm. This method not only generates smooth paths but also ensures feasible global solutions, significantly enhancing the efficiency and safety of path planning. Firstly, in response to the water depths limitation, this study set up safe water depths, providing strong guarantees for the safe navigation of USVs in complex waters. Secondly, based on the hydrological and geographical characteristics of the study sea area, an accurate ocean environment model was constructed using Ansys Fluent software and computational fluid dynamics (CFD) technology, thus providing USVs with a feasible path solution on a global scale. Then, the local sea area with complex obstacles was converted into a grid map to facilitate detailed planning. Meanwhile, the improved A* algorithm was utilized for meticulous route optimization. Furthermore, by combining the results of local and global planning, the approach generated a comprehensive route that accounts for the complexities of the maritime environment while avoiding local optima. Finally, simulation results demonstrated that the algorithm proposed in this study shows faster pathfinding speed, shorter route distances, and higher route safety compared to other algorithms. Moreover, it remains stable and effective in real-world scenarios. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). This solution is based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be applied as the first line of the response to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or in the open sea during transport activities in a fast, efficient, and low-cost way. The paper describes the work done in the development of a team of autonomous vehicles able to carry as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), and the development of a multi-robot framework for efficient oil spill mitigation. Field tests have been performed in Portugal and Spain's harbors, with a simulated oil spill, and the coordinate oil spill task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV) STORK has been validated. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Robust object detection and weather classification are essential for the safe operation of autonomous vehicles (AVs) in adverse weather conditions. While existing research often treats these tasks separately, this paper proposes a novel multi objectives model that treats weather classification and object detection as a single problem using only the AV camera sensing system. Our model offers enhanced efficiency and potential performance gains by integrating image quality assessment, Super-Resolution Generative Adversarial Network (SRGAN), and a modified version of You Only Look Once (YOLO) version 5. Additionally, by leveraging the challenging Detection in Adverse Weather Nature (DAWN) dataset, which includes four types of severe weather conditions, including the often-overlooked sandy weather, we have conducted several augmentation techniques, resulting in a significant expansion of the dataset from 1027 images to 2046 images. Furthermore, we optimize the YOLO architecture for robust detection of six object classes (car, cyclist, pedestrian, motorcycle, bus, truck) across adverse weather scenarios. Comprehensive experiments demonstrate the effectiveness of our approach, achieving a mean average precision (mAP) of 74.6%, underscoring the potential of this multi objectives model to significantly advance the perception capabilities of autonomous vehicles' cameras in challenging environments. [ABSTRACT FROM AUTHOR]

Since Leonardo da Vinci's creation of a self-propelled cart in the 1500s (Palmer. in Significant figures in world history p. 75--7, 2018), the evolution of Autonomous Vehicles (AVs) has aimed to revolutionize transportation. While AVs promise improved safety, traffic efficiency, and industrial optimization by reducing human intervention, ensuring their security remains paramount. This paper provides a thorough literature review spanning from historical milestones to contemporary advancements in AV technology. It delves into the significance of Vehicular Ad-hoc NETworks (VANETs) for safety applications and underscores the critical role of speed harmonization and string stability in safeguarding AV platoons. Furthermore, the paper addresses cybersecurity threats targeting platoon networks, advocating for research into encryption mechanisms, road-side units, control algorithms, hybrid communications, and on-board system security to bolster communication security within platoons. By advocating for a balance between AV technological advancements and robust security measures, this paper facilitates safe and reliable AV platooning operations. [ABSTRACT FROM AUTHOR]

Vehicle pose detection plays a vital role in modern automotive technology, which can improve driving safety, enhance vehicle stability and provide important support for the development of autonomous driving technology. The current pose estimation methods have the problems of accumulation errors, large algorithm computing power, and expensive cost, so they cannot be widely used in intelligent connected vehicles. This paper proposes a vehicle pose detection method based on an RSU (Roadside Unit). First, the on-board GPS performs the positioning of the target vehicle and transmits the positioning information to the RSU through the UDP (User Data Protocol). Next, the RSU transmits a forward command to the OBU (On-board Unit) through the UDP. The OBU sends the command to the ECU (Electronic Control Unit) to control the vehicle forward. Then, the RSU detects and tracks the vehicle. The RSU takes pictures of two images before and after the movement and obtains the coordinates of the four angle points and the center point by image processing. The vehicle heading direction is determined by the moving direction of the center point of the front and rear two images. Finally, the RSU captures the vehicle images in real time, performs the process of tracking, rectangular fitting and pose calculation to obtain the pose information and transmits the information to the OBU to complete the whole process of vehicle pose detection and information transmission. Experiments show that the method can realize accurate and efficient detection of vehicle pose, meet the real-time requirements of vehicle pose detection, and can be widely used in intelligent vehicles. [ABSTRACT FROM AUTHOR]

The formation of unmanned surface vehicles (USVs) stands as a paramount concern in USV formation control research. In addressing this, this paper introduces a novel formation strategy for USVs, which accounts for their arbitrary initial positions and headings. Firstly, given the random distribution of the USVs' initial positions, to enhance the efficiency of formation assembly, the point set of the USVs is matched with basic formations using the Hausdorff distance to establish the formation. Building upon this foundation and acknowledging the potential hindrance posed by the arbitrary initial headings of the USVs, which could hinder their ability to reach the designated formation target points, a cost matrix is constructed based on the designed distance–azimuth evaluation function. Subsequently, the Hungarian algorithm is utilized to efficiently assign formation target points for the USVs. Ultimately, the effectiveness of the proposed strategy is validated through simulation experiments. [ABSTRACT FROM AUTHOR]

To realize accurate environment perception, which is the technological key to enabling autonomous vehicles to interact with their external environments, it is primarily necessary to solve the issues of object detection and tracking in the vehicle-movement process. Multi-sensor fusion has become an essential process in efforts to overcome the shortcomings of individual sensor types and improve the efficiency and reliability of autonomous vehicles. This paper puts forward moving object detection and tracking methods based on LiDAR—camera fusion. Operating based on the calibration of the camera and LiDAR technology, this paper uses YOLO and PointPillars network models to perform object detection based on image and point cloud data. Then, a target box intersection-over-union (IoU) matching strategy, based on center-point distance probability and the improved Dempster–Shafer (D–S) theory, is used to perform class confidence fusion to obtain the final fusion detection result. In the process of moving object tracking, the DeepSORT algorithm is improved to address the issue of identity switching resulting from dynamic objects re-emerging after occlusion. An unscented Kalman filter is utilized to accurately predict the motion state of nonlinear objects, and object motion information is added to the IoU matching module to improve the matching accuracy in the data association process. Through self-collected data verification, the performances of fusion detection and tracking are judged to be significantly better than those of a single sensor. The evaluation indexes of the improved DeepSORT algorithm are 66% for MOTA and 79% for MOTP, which are, respectively, 10% and 5% higher than those of the original DeepSORT algorithm. The improved DeepSORT algorithm effectively solves the problem of tracking instability caused by the occlusion of moving objects. [ABSTRACT FROM AUTHOR]

The contradiction between increasing traffic and the relatively poor roundabout infrastructure is getting stronger. The control and optimization of the macroscopic traffic flow needs to be improved to resolve congestion and safety problems at roundabouts and the connected road network. In order to better understand the gaps and trends in this field, we have systematically reviewed the main research and developments in traffic phenomena, driving behaviour, autonomous vehicles (AVs), intelligent connected vehicles and real vehicle trajectory data sets at roundabouts. The study is based on 388 papers about roundabouts, selected through a comprehensive literature search. The review demonstrates that based on a microscopic perspective, sensing, prediction, decision-making, planning and control aspects of AVs and intelligent connected vehicles can be designed and optimized to fundamentally and significantly improve traffic capacity and driving safety at roundabouts. However, the generation mechanism of traffic conflicts among traffic participants at roundabouts is complex, which is a tremendous challenge for the systematic design of AVs. Therefore, based on naturalistic driving data and machine learning theory, it is an important research direction to build driver models by learning and imitating human driver decision-making and driving behaviours. [ABSTRACT FROM AUTHOR]

Smart cities are becoming an essential part in most of the countries. Safety also becomes important when such transformations occur. Safety can be achieved with the help of autonomous vehicles. Adoption of AI has provided practical solutions to most of the autonomous driving problems. Extended Traffic sign recognition is important for advanced driver assistance systems, autonomous vehicles, and mobile robots to inform the driver about the potential hazard ahead. Several research on traffic sign recognition has been carried out in the past, and most existing works were focused on traffic signs. Although research works have been done for detecting traffic signs, still there is a need for detection of small construction zone traffic signs and road barriers with better accuracy. This paper aims to showcase the results obtained using state of art deep networks, namely DSOD, a variant of SSD, YOLOV3, YOLOv4 for detection of construction zone traffic signs from the video sequences captured by a camera mounted on a car. Further, the classification of signs is carried out using CNN, which uses the detector feature as input. In addition to that, tracking is performed to include the missed detections and exclude the false positives arrived during detection. The proposed approach can achieve a maximum of 90% accuracy with 0.5 IOU for the detection of construction zone signs and road barriers. [ABSTRACT FROM AUTHOR]

Autonomous navigation is the leading technology in current era, in this intelligent traffic light, sign detection, ADAS and obstacle detections were playing major role. Image segmentation is the process of dividing an image into different regions, or semantic classes. This is a challenging problem in autonomous vehicle technology because it requires the vehicle to be able to understand its surroundings to safely navigate. The major challenges in this platform are the accuracy and efficiency of model performance. The proposed method in the abstract uses a convolutional neural network (CNN) to perform image segmentation. CNNs are a type of deep learning model that is well-suited for image processing tasks. The CNN in this paper was trained on a local city dataset, and it was able to achieve a mean intersection over union (IoU) of 73%. IoU is a measure of how well the segmentation results match the ground truth labels. A score of 100% indicates that the segmentation is perfect, while a score of 0% indicates that the segmentation is completely wrong. This means that the method can segment images at a very fast rate, which is important for autonomous vehicles that need to make real-time decisions. Overall, the proposed method is a promising approach for image segmentation in autonomous vehicles. It can achieve high accuracy and speed, and it is easy to implement using Python. The proposed method attains an accuracy of 98.34 %, a Sensitivity of 97.26 % and a sensitivity of 96.37 % had been attained. The method could be used to improve the safety and efficiency of autonomous vehicles by enabling them to better understand their surroundings. [ABSTRACT FROM AUTHOR]

Multi-modal fusion technology gradually become a fundamental task in many fields, such as autonomous driving, smart healthcare, sentiment analysis, and human-computer interaction. It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities. Under complex scenes, multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions. However, achieving outstanding performance is challenging because of equipment performance limitations, missing information, and data noise. This paper comprehensively reviews existing methods based on multi-modal fusion techniques and completes a detailed and in-depth analysis. According to the data fusion stage, multi-modal fusion has four primary methods: early fusion, deep fusion, late fusion, and hybrid fusion. The paper surveys the three major multi-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various fields. Finally, it discusses the challenges and explores potential research opportunities. Multi-modal tasks still need intensive study because of data heterogeneity and quality. Preserving complementary information and eliminating redundant information between modalities is critical in multi-modal technology. Invalid data fusion methods may introduce extra noise and lead to worse results. This paper provides a comprehensive and detailed summary in response to these challenges. [ABSTRACT FROM AUTHOR]

Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called RHA*, which achieves efficient pathfinding and high path quality for large-scale unequal-weighted maps. Firstly, the original map grid cells were aggregated into fixed-size clusters. Then, an abstract map was constructed by aggregating equal-weighted clusters into rectangular regions of different sizes and calculating the nodes and edges of the regions in advance. Finally, real-time pathfinding was performed based on the abstract map. The experiment showed that the computation time of real-time pathfinding was reduced by 96.64% compared to A* and 20.38% compared to HPA*. The total cost of the generated path deviated no more than 0.05% compared to A*. The deviation value is reduced by 99.2% compared to HPA*. The generated path can be used for autonomous vehicle traveling in off-road environments. [ABSTRACT FROM AUTHOR]

Path planning and obstacle avoidance are fundamental problems in unmanned ground vehicle path planning. Aiming at the limitations of Deep Reinforcement Learning (DRL) algorithms in unmanned ground vehicle path planning, such as low sampling rate, insufficient exploration, and unstable training, this paper proposes an improved algorithm called Dual Priority Experience and Ornstein–Uhlenbeck Soft Actor-Critic (DPEOU-SAC) based on Ornstein–Uhlenbeck (OU noise) and double-factor prioritized sampling experience replay (DPE) with the introduction of expert experience, which is used to help the agent achieve faster and better path planning and obstacle avoidance. Firstly, OU noise enhances the agent's action selection quality through temporal correlation, thereby improving the agent's detection performance in complex unknown environments. Meanwhile, the experience replay is based on double-factor preferential sampling, which has better sample continuity and sample utilization. Then, the introduced expert experience can help the agent to find the optimal path with faster training speed and avoid falling into a local optimum, thus achieving stable training. Finally, the proposed DPEOU-SAC algorithm is tested against other deep reinforcement learning algorithms in four different simulation environments. The experimental results show that the convergence speed of DPEOU-SAC is 88.99% higher than the traditional SAC algorithm, and the shortest path length of DPEOU-SAC is 27.24, which is shorter than that of SAC. [ABSTRACT FROM AUTHOR]

The growing relevance of sustainability, as well as the necessity to replace traditional forms of transportation with sustainable ones, has made sustainable urban mobility an imperative. In order to respond to the ever-increasing need to develop sustainable modes of transport, the importance of electric, autonomous, and electric autonomous vehicles is increasingly emphasized. In addition, as trends of growth and development in electric autonomous vehicle technology are increasing, one of the questions that has appeared is whether autonomous electric vehicles represent one of the mechanisms that will be used to increase the sustainability of urban mobility. With this in mind, the results of a systematic analysis of existing research in the WOS and Scopus databases using the keywords "urban mobility", "electric vehicles", and "autonomous vehicles" was carried out to identify research trends in the use of autonomous electric vehicles in urban areas. The research showed that authors focus on the advantages and disadvantages of autonomous electric vehicles and their usage in the urban mobility system, but an insufficient number of authors consider and define the need to plan the transition towards incorporating autonomous electric vehicles into the urban system. The results of this research also indicate an insufficient number of papers that research and describe the application of autonomous electric vehicles in distribution logistics. This paper provides an overview of existing research related to autonomous electric vehicles and the challenges of transition in the context of infrastructure and the development of a culture of sustainability among urban residents. [ABSTRACT FROM AUTHOR]

Autonomous vehicles must navigate independently in an outdoor environment using features or objects. However, some objects may be more or less suitable for localization due to their attributes. Therefore, this work investigates the suitability of landmarks for camera- and object-based outdoor localization methods. First, object attributes are methodically derived from the requirements of object-based localization. The physical representation on the camera image plane, probability of occurrence, and persistence were identified as influencing the object localization suitability. The influence of the object's camera image plane representation regarding object recognition algorithms is not considered or discussed, but advice on the minimum object pixel size is provided. The first milestone was the creation of an equation for object localization suitability calculation by normalizing and multiplying the identified attributes. Simultaneously, potential objects from the outdoor environment were identified, resulting in a structured object catalog. The results of the equation and catalog are a ranked according to the object localization suitability in a comparison table. Our comparison demonstrates that objects such as buildings or trees are more suitable than street lane markings for self-localization. However, most current datasets do not include the proposed instantiated objects. The paper addresses this issue, assists in the object selection for outdoor localization methods and provides input for the creation of future-oriented datasets and autonomous driving maps. [ABSTRACT FROM AUTHOR]

Three-colour four-way traffic light completed 100 years in 2020. Even though the traffic light in the form of Semaphore arms has been in use in London since 1868, electric traffic lights came into existence in 1912 and the standard three-colour four-way light in 1920. Research is continuously being carried out to develop better algorithms to improve safety, reduce travel delays, and optimize road capacity. Hence a review of the evolution of traffic lights is warranted. This paper presents an all-round review using a six-prong approach. Timeline of the evolution of the literature in the last 100 years, the evolution of hardware, algorithms, traffic control schemes, standards and the pedestrian lights and count down timer are the six areas in which the review is carried out. A timeline of the different keywords related to the various algorithms in use is presented. This article delves into the thinking and meticulous approach of early researchers and practitioners of the field while dwelling on the past. They laid the rock-solid foundation of today's research. Also, future research areas like connected vehicles and automated vehicles are pointed out, and a summary of the findings is presented at the end. [ABSTRACT FROM AUTHOR]

This paper offers a novel view for managing and controlling the movement of driverless, i.e., autonomous, vehicles by converting this movement to a simulated train movement moving on a rail track. It expands on the "virtual track" theory and creates a model for virtual track autonomous vehicle management and control based on the ideas and methods of railway train operation. The developed model and adopted algorithm allow for large-scale autonomous driving vehicle control on the highway while considering the temporal-spatial distribution of vehicles, temporal-spatial trajectory diagram optimization, and the management and control model and algorithm for autonomous vehicles, as design goals. The ultimate objective is to increase the safety of the road traffic environment when autonomous vehicles are operating in it together with human-driven vehicles and achieve more integrated and precise organization and scheduling of these vehicles in such mixed traffic conditions. The developed model adopted a "particle swarm" optimization algorithm that is tested in a hypothetical network pending a full-scale test on a real highway. The paper concludes that the proposed management and control model and algorithm based on the "virtual track" theory is promising and demonstrates feasibility and effectiveness for further development and future application. [ABSTRACT FROM AUTHOR]

Featured Application: Path Tracking Control for Autonomous Driving. This paper presents a method to design a path tracking controller with a constraint on tire slip angles under low-friction road conditions. On a low-friction road surface, a lateral tire force is easily saturated and decreases as a tire slip angle increases by a large steering angle. Under this situation, a path tracking controller cannot achieve its maximum performance. To cope with this problem, it is necessary to limit tire slip angles to a value where the maximum lateral tire force is achieved. The most commonly used controllers for path tracking, linear quadratic regulator (LQR) and model predictive control (MPC), are adopted as a controller design methodology. The control inputs of LQR and MPC are front and rear steering angles and control yaw moment, which have been widely used for path tracking. The constraint derived from tire slip angles is imposed on the steering angles of LQR and MPC. To fully verify the performance of the path tracking controller with the constraint on tire slip angles, a simulation is conducted on vehicle simulation software. From the simulation results, it is shown that the path tracking controller with the constraint on tire slip angles presented in this paper is quite effective for path tracking on low-friction road surface. [ABSTRACT FROM AUTHOR]

Connected and automated vehicle platoons (CAVPs) are considered an effective way to alleviate traffic congestion, reduce the incidence of traffic accidents, and improve vehicle economy in the intelligent transportation system (ITS). Vehicles in the CAVPs can communicate with each other through V2X technology, which could optimize the economy of the platoon. Cooperative adaptive cruise control (CACC) can make effective use of the characteristics of CAVPs and contribute to resource conservation, ecological driving, and traffic system development. In this paper, a two-stage CACC method is proposed for CAVPs to reduce fuel consumption in the multislope road section. In the first stage, the optimal velocity profiles for the leader based on dynamic programming (DP) are planned according to the road information and the fuel consumption model. In the second stage, a vehicle longitudinal third-order differential dynamics model is utilized to build the platoon time-delay system considering communication delay and actuator delay. A feedback controller is developed for each vehicle considering the internal stability and the string stability of the CAVPs. Results show that the proposed method can save 5.33% of fuel consumption compared to the constant speed cooperative adaptive cruise control (CS-CACC) method and has a better tracking performance compared to the model predictive control (MPC) method. The CACC method proposed in this paper can provide a theoretical basis and data support for building an ecological CACC strategy for CAVPs. [ABSTRACT FROM AUTHOR]

With the advancement of IT technology, intelligent devices such as autonomous vehicles, unmanned equipment, and drones are rapidly evolving. Consequently, the proliferation of defense systems based on these technologies is increasing worldwide. In response, the U.S. Department of Defense is implementing the RMF (Risk Management Framework) to ensure the cybersecurity of defense systems and conducting cybersecurity T&E (test and evaluation) concurrently. However, RMF and cybersecurity T&E conducted during the acquisition phase of defense systems often result in fragmented cybersecurity assessments, excluding the operational environment of the defense systems. This omission fails to account for the complex network integration, data exchange functionalities, and mission-specific requirements in actual cyber attack scenarios. For these reasons, vulnerabilities in defense systems that remain unidentified during the acquisition phase can potentially pose significant cybersecurity threats during operational phases, necessitating substantial costs and efforts for remediation. Therefore, this paper proposes a mission-based cybersecurity T&E model using a Multi-Cyber Range to effectively apply these two systems in a practical manner. The Multi-Cyber Range integrates independently operated cyber ranges into a network to expand the evaluation environment, which better reflects the mission environment of defense systems. The proposed model's effectiveness is validated using a cyber attack simulation system targeting a virtualized arbitrary defense system. This paper not only presents an enhanced model for mission-based cybersecurity T&E, but also contributes to the advancement of cybersecurity T&E methodologies by providing a concrete application process. [ABSTRACT FROM AUTHOR]

In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account. [ABSTRACT FROM AUTHOR]

Work zones pose mobility issues to the traveling public and safety challenges to travelers and road maintenance workers. These safety and mobility issues may be exacerbated by the presence of heavy vehicles. Connected and automated vehicle (CAV) technologies have been identified as a potential solution for these issues. This paper investigates the operational impacts of connected and automated heavy vehicles (CAHV) on freeway work zone operations on interstate highways. A microsimulation model, calibrated to empirical work zone field data, was used to study the operational impacts of CAHV platoons under various work zone and traffic conditions. It was found that, as the CAHV market penetration rate increases, the average work zone delay and queue length decreases. In addition, as the demand and heavy vehicle percentage increases, so do the benefits of using CAHV technology. For example, it was found that, when all heavy vehicles are classified as CAHV, the average flow rate is approximately 67% higher, and the maximum queue size and average delay decrease by approximately 97%. The methodology used in this paper will help transportation agencies as they design work zones to accommodate heavy vehicles equipped with CAV technologies. [ABSTRACT FROM AUTHOR]

The safety of autonomous driving is closely linked to accurate depth perception. With the continuous development of autonomous driving, depth completion has become one of the crucial methods in this field. However, current depth completion methods have major shortcomings in small objects. To solve this problem, this paper proposes an end-to-end architecture with adaptive spatial feature fusion by encoder–decoder (ASFF-ED) module for depth completion. The architecture is built on the basis of the network architecture proposed in this paper, and is able to extract depth information adaptively with different weights on the specified feature map, which effectively solves the problem of insufficient depth accuracy of small objects. At the same time, this paper also proposes a depth map visualization method with a semi-quantitative visualization, which makes the depth information more intuitive to display. Compared with the currently available depth map visualization methods, this method has stronger quantitative analysis and horizontal comparison ability. Through experiments of ablation study and comparison, the results show that the method proposed in this paper exhibits a lower root-mean-squared error (RMSE) and better small object detection performance on the KITTI dataset. [ABSTRACT FROM AUTHOR]

Road detection technology is an important part of the automatic driving environment perception system. With the development of technology, the situations that automatic driving needs to consider will become broader and more complex. This paper contributes a lightweight convolutional neural network model, incorporating novel convolution and parallel pooling modules, an improved network activation function, and comprehensive training and verification with multiple datasets. The proposed model achieves high accuracy in detecting drivable areas in complex autonomous driving situations while significantly improving real-time performance. In addition, we collect data in the field and create small datasets as reference datasets for testing algorithms. This paper designs relevant experimental scenarios based on the datasets and experimental platforms and conducts simulations and real-world vehicle experiments to verify the effectiveness and stability of the algorithm models and technical solutions. The method achieves an MIoU of 90.19 and a single batch time of 340 ms with a batch size of 8, which substantially reduces the runtime relative to a typical deep network structure like ResNet50. [ABSTRACT FROM AUTHOR]

Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system. Traditionally, stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images. Despite the extensive amount of research, these traditional techniques still suffer in the presence of highly textured areas, large uniform regions, and occlusions. Motivated by their growing success in solving various 2D and 3D vision problems, deep learning for stereo-based depth estimation has attracted a growing interest from the community, with more than 150 papers published in this area between 2014 and 2019. This new generation of methods has demonstrated a significant leap in performance, enabling applications such as autonomous driving and augmented reality. In this paper, we provide a comprehensive survey of this new and continuously growing field of research, summarize the most commonly used pipelines, and discuss their benefits and limitations. In retrospect of what has been achieved so far, we also conjecture what the future may hold for deep learning-based stereo for depth estimation research. [ABSTRACT FROM AUTHOR]