Operation Of Autonomous Vehicles Research Articles

Wavelet-powered hierarchical frequency filtering framework for autonomous vehicle sensors fault diagnosis and correction under open environments

The reliable operation of autonomous vehicles heavily depends on the functionality of numerous sensors that facilitate environmental perception and vehicle control. However, sensor malfunctions can significantly undermine the dependability and safety of autonomous vehicles. Despite the significant advancements made in sensor fault diagnosis through deep learning, the technology's susceptibility to noise and limited frequency analysis capacity renders it less effective in addressing frequency aliasing and noise interference issues. In addition, repairing incorrect sensor data holds greater significance for autonomous vehicles than merely identifying sensor failures. To this end, this paper proposes a wavelet-powered hierarchical frequency filtering framework (WavePHF) for autonomous vehicle sensors' fault diagnosis and correction. First, this study designs a multi-sensor fault identification and correction joint architecture to detect sensor faults and repair fault signals in time. Second, this study integrates the discrete wavelet transform (DWT) algorithm into the deep model to significantly enhance the model's frequency analysis capabilities and suppress frequency aliasing and noise interference. In particular, DWT is deeply integrated into the convolutional modules' parameter updating and optimization process, thereby facilitating mutual learning and promotion. Third, this study proposes an adaptive frequency weighting block for low-frequency features and an adaptive frequency discarding block for high-frequency features to achieve hierarchical frequency filtering in a coarse-to-fine manner and efficiently identify fault categories and restore pure signals. The WavePHF is evaluated on a real autonomous driving dataset. Experiments show that WavePHF has excellent autonomous vehicle sensors fault diagnosis and correction capabilities and competitive performance in different urban scenarios.

Environment adaptive navigation (EAN): A data-driven approach for improved navigation accuracy

The quality of GNSS-based navigation services is highly influenced by the type of operating environment. The urban environments with buildings and structures pose substantial challenges for GNSS navigation accuracy. To address this challenge, we propose a data-driven approach Environment Adaptive Navigation (EAN) because complex mathematical models for GNSS environments are impractical for real-time use due to their processing load. This data-driven approach analyzes real-time GNSS data to understand how the environment affects performance and optimize receiver settings for each scenario. Keeping this in view, raw GNSS data was collected through field trials including the three environments: clear sky, partially degraded, and highly degraded. We then analyzed this data to pinpoint factors affecting accuracy, such as the number of available satellites and standard error measurements. The proposed solution, the EAN algorithm, tackles these limitations of the GNSS due to the urban environments and improves the navigation performance. This data-driven approach analyzes real-time GNSS data to identify the specific environment (clear, partially degraded, or highly degraded). Based on this assessment, EAN dynamically adjusts receiver settings, like tracking loop bandwidth, to achieve optimal performance under those conditions. Integrating the EAN model into GNSS receivers allows for real-time environment detection and adaptive configuration. This EAN-based GNSS receiver holds significant promise for safety-critical applications like Intelligent Transportation Systems (ITS). Precise navigation is crucial for functionalities within ITS, such as route optimization and autonomous vehicle operation. The effectiveness of the EAN-based receiver was validated through a field experiment demonstrating a notable increase in tracked satellites and a substantial reduction in outages within a highly degraded environment.

Modeling an on-demand meal delivery system with human couriers and autonomous vehicles in a spatial market

This paper investigates the impacts of introducing autonomous vehicles (AVs) into an on-demand meal delivery system at a strategic level. The proposed model consists of (i) a microscopic physical model describing the delivery process for bundled orders and (ii) a macroscopic network equilibrium model characterizing the interactions among customers, human couriers (HCs), and AVs, as well as couriers’ repositioning behaviors in the market. A tailored algorithm based on the Alternating Direction Method of Multiplier (ADMM) is developed to solve the platform’s optimal pricing and maximize its profit. To investigate the impact of AV operations, we test three AV distribution rules, i.e., distributing AVs evenly in the space (Rule 0), proportional to demand (Rule 1), and inversely proportional to demand (Rule 2). The numerical experiments show that Rule 2 archives the maximum platform profit, along with the highest service throughput and the hourly earning rate of HCs. Nevertheless, the numerical experiments adopting the parameters calibrated by current market conditions show that the employment of AVs does not show significant benefits to the platform or other stakeholders. It can only generate a higher platform profit when the AV operation cost is lower than HCs’ hourly earnings in a purely labor-based MDS system. As the AV fleet size expands, the improvement of service quality is rather minor meanwhile the hourly earning of HCs drops substantially.

Fully Autonomous Trustworthy Unmanned Aerial Vehicle Teamwork: A Research Guideline Using Level 2 Blockchain

The vast range of possible fully autonomous multiunmanned aerial vehicle (multi-UAV) operations is creating a new and expanding market where technological advances are happening at a breakneck pace. The integration of UAVs in airspaces (not just for military purposes but also for civil, commercial, and leisure use) is essential in realizing the potential of this growing industry. Furthermore, with the advent of 6G, such integration will be cost-effective and more flexible. However, to reach widespread adoption, new models focusing on the safety, efficiency, reliability, and privacy of fully autonomous multi-UAV operations, ensuring that the operation history is trustworthy and can be audited by the relevant stakeholders, need to be developed. Accordingly, this work presents a research guideline for fully autonomous trustworthy UAV teamwork through layer 2 blockchains that provide efficient, privacy-preserving, reliable, and secure multi-UAV service delivery. We show the implications of this approach for an aerial surveillance use case.

End-to-End Target Liveness Detection via mmWave Radar and Vision Fusion for Autonomous Vehicles

The successful operation of autonomous vehicles hinges on their ability to accurately identify objects in their vicinity, particularly living targets such as bikers and pedestrians. However, visual interference inherent in real-world environments, such as omnipresent billboards, poses substantial challenges to extant vision-based detection technologies. These visual interference exhibit similar visual attributes to living targets, leading to erroneous identification. We address this problem by harnessing the capabilities of mmWave radar, a vital sensor in autonomous vehicles, in combination with vision technology, thereby contributing a unique solution for liveness target detection. We propose a methodology that extracts features from the mmWave radar signal to achieve end-to-end liveness target detection by integrating the mmWave radar and vision technology. This proposed methodology is implemented and evaluated on the commodity mmWave radar IWR6843ISK-ODS and vision sensor Logitech camera. Our extensive evaluation reveals that the proposed method accomplishes liveness target detection with a mean average precision of 98.1%, surpassing the performance of existing studies.

Towards a Model of Snow Accretion for Autonomous Vehicles

Snow accumulation on surfaces exposed to adverse weather conditions has been studied over the years due to a variety of problems observed in different industry sectors, such as aeronautics and wind and civil engineering. With the growing interest in autonomous vehicles (AVs), this concern extends to advanced driver-assistance systems (ADAS). Weather stressors, such as snow and icing, negatively influence the sensor functionality of AVs, and their autonomy is not guaranteed by manufacturers during episodes of intense weather precipitation. As a basis for mitigating the negative effects caused by heavy snowfall, models need to be developed to predict snow accumulation over critical surfaces of AVs. The present work proposes a framework for the study of snow accumulation on road vehicles. Existing icing and snow accretion models are reviewed, and adaptations for automotive applications are discussed. Based on the new capabilities developed by the Weather on Wheels (WoW) program at Ontario Tech University, a model architecture is proposed in order to progress toward adequate snow accretion predictions for autonomous vehicle operating conditions, and preliminary results are presented.

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles.

Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry.

Automated Lane Centering: An Off-the-Shelf Computer Vision Product vs. Infrastructure-Based Chip-Enabled Raised Pavement Markers.

Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ±0.016 m-1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle's position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs.

Digital twin‐based multi‐objective autonomous vehicle navigation approach as applied in infrastructure construction

AbstractThe widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation, with path planning emerging as a critical aspect. However, existing road infrastructure confronts challenges due to prolonged use and insufficient maintenance. Previous research on autonomous vehicle navigation has focused on determining the trajectory with the shortest distance, while neglecting road construction information, leading to potential time and energy inefficiencies in real‐world scenarios involving infrastructure development. To address this issue, a digital twin‐embedded multi‐objective autonomous vehicle navigation is proposed under the condition of infrastructure construction. The authors propose an image processing algorithm that leverages captured images of the road construction environment to enable road extraction and modelling of the autonomous vehicle workspace. Additionally, a wavelet neural network is developed to predict real‐time traffic flow, considering its inherent characteristics. Moreover, a multi‐objective brainstorm optimisation (BSO)‐based method for path planning is introduced, which optimises total time‐cost and energy consumption objective functions. To ensure optimal trajectory planning during infrastructure construction, the algorithm incorporates a real‐time updated digital twin throughout autonomous vehicle operations. The effectiveness and robustness of the proposed model are validated through simulation and comparative studies conducted in diverse scenarios involving road construction. The results highlight the improved performance and reliability of the autonomous vehicle system when equipped with the authors’ approach, demonstrating its potential for enhancing efficiency and minimising disruptions caused by road infrastructure development.

Navigating the future: A comprehensive survey of localization systems in autonomous vehicles

<p>This survey paper provides an in-depth analysis of localization systems for autonomous vehicles (AVs), a cornerstone technology crucial for the safe and efficient operation of AVs. The paper encompasses a comprehensive examination of various localization technologies, their comparative analysis, the challenges they face, and the emerging trends shaping their future. We begin by exploring the principal technologies employed in AV localization: GPS, LiDAR, radar, cameras, and ultrasonic sensors. Each technology is scrutinized to understand its strengths, weaknesses, and applicability in different environmental contexts. GPS offers broad geographical positioning but struggles with precision in dense urban areas. LiDAR provides high-resolution mapping but is hindered by adverse weather conditions. Radar ensures reliability in poor visibility but lacks the finer details provided by optical systems. Cameras offer rich visual data but are dependent on lighting conditions, and ultrasonic sensors, while effective for close-range detection, are limited to low-speed applications. The paper then presents a comparative analysis of these technologies against key performance metrics such as accuracy, reliability, latency, scalability, and cost-effectiveness. This analysis is critical in understanding the suitability of each technology for various driving scenarios, from congested urban streets to open highways and in diverse weather conditions. Challenges in AV localization are multifaceted, encompassing technological, environmental, and system integration issues. Technological challenges include sensor limitations and computational constraints, while environmental factors like weather and urban infrastructure significantly impact localization accuracy. System integration poses another significant challenge, necessitating seamless interaction between localization systems and other vehicle control systems for real-time decision-making. Emerging trends and potential solutions to these challenges are discussed, highlighting advancements in sensor technology, AI, machine learning, and 5G communications. These developments promise to overcome existing limitations and enhance the accuracy and reliability of AV localization. The integration of localization systems with smart city infrastructures and the implementation of collaborative technologies like V2X communication are speculated to further augment AV capabilities. In conclusion, the paper emphasizes that the future of AV localization is intrinsically linked to the continuous evolution of technologies and their integration. Addressing current challenges and harnessing emerging trends are pivotal for the advancement of AVs, steering us toward a future of autonomous transportation characterized by increased safety, efficiency, and reliability.<strong></strong></p>

Navigating the future: A comprehensive survey of localization systems in autonomous vehicles

<p>This survey paper provides an in-depth analysis of localization systems for autonomous vehicles (AVs), a cornerstone technology crucial for the safe and efficient operation of AVs. The paper encompasses a comprehensive examination of various localization technologies, their comparative analysis, the challenges they face, and the emerging trends shaping their future. We begin by exploring the principal technologies employed in AV localization: GPS, LiDAR, radar, cameras, and ultrasonic sensors. Each technology is scrutinized to understand its strengths, weaknesses, and applicability in different environmental contexts. GPS offers broad geographical positioning but struggles with precision in dense urban areas. LiDAR provides high-resolution mapping but is hindered by adverse weather conditions. Radar ensures reliability in poor visibility but lacks the finer details provided by optical systems. Cameras offer rich visual data but are dependent on lighting conditions, and ultrasonic sensors, while effective for close-range detection, are limited to low-speed applications. The paper then presents a comparative analysis of these technologies against key performance metrics such as accuracy, reliability, latency, scalability, and cost-effectiveness. This analysis is critical in understanding the suitability of each technology for various driving scenarios, from congested urban streets to open highways and in diverse weather conditions. Challenges in AV localization are multifaceted, encompassing technological, environmental, and system integration issues. Technological challenges include sensor limitations and computational constraints, while environmental factors like weather and urban infrastructure significantly impact localization accuracy. System integration poses another significant challenge, necessitating seamless interaction between localization systems and other vehicle control systems for real-time decision-making. Emerging trends and potential solutions to these challenges are discussed, highlighting advancements in sensor technology, AI, machine learning, and 5G communications. These developments promise to overcome existing limitations and enhance the accuracy and reliability of AV localization. The integration of localization systems with smart city infrastructures and the implementation of collaborative technologies like V2X communication are speculated to further augment AV capabilities. In conclusion, the paper emphasizes that the future of AV localization is intrinsically linked to the continuous evolution of technologies and their integration. Addressing current challenges and harnessing emerging trends are pivotal for the advancement of AVs, steering us toward a future of autonomous transportation characterized by increased safety, efficiency, and reliability.<strong></strong></p>

Editorial: Special Issue on Opportunities and Challenges with Autonomous Racing

In the ever-evolving landscape of autonomous vehicles, competition and research of high-speed autonomous racing emerged as a captivating frontier, pushing the limits of perception, planning, and control. Autonomous racing presents a setup where the intersection of cutting-edge software and hardware development sparks unprecedented opportunities and confronts unique challenges. The motorsport axiom, “If everything seems under control, then you are not going fast enough,” resonates in this special issue, underscoring the demand for algorithms and hardware that can navigate at the cutting edge of control, traction, and agility. In pursuing autonomy at high speeds, the racing environment becomes a crucible, pushing autonomous vehicles to execute split-second decisions with high precision. Autonomous racing, we believe, offers a litmus test for the true capabilities of self-driving software. Just as racing has historically served as a proving ground for automotive technology, autonomous racing now presents itself as the crucible for testing self-driving algorithms. While routine driving situations dominate much of the autonomous vehicle operations, focusing on extreme situations and environments is crucial to support investigation into safety benefits. The urgency of advancing highspeed autonomy is palpable in burgeoning autonomous racing competitions like Formula Student Driverless, F1TENTH autonomous racing, Roborace, and the Indy Autonomous Challenge. These arenas provide a literal testbed for testing perception, planning, and control algorithms and symbolize the accelerating traction of autonomous racing as a proving ground for agile and safe autonomy. Our special issue focuses on cutting-edge research into software and hardware solutions for highspeed autonomous racing. We sought contributions from the robotics and autonomy communities that delve into the intricacies of head-to-head multi-agent racing: modeling vehicle dynamics at high speeds, developing advanced perception, planning, and control algorithms, as well as the demonstration of algorithms, in simulation and in real-world vehicles. While presenting recent developments for autonomous racing, we believe these special issue papers will also create an impact in the broader realm of autonomous vehicles.

Triangle codes and tracer lights based absolute positioning method for terminal visual docking of autonomous underwater vehicles

PurposeDocking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.Design/methodology/approachThis paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.FindingsThe authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.Originality/valueThis fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

Berthing assistance system for autonomous surface vehicles based on 3D LiDAR

Berthing assistance systems (BASs) for berthing operations of autonomous surface vehicles (ASVs) in restricted waters are very important. Existing BASs are primarily designed for crewed vessels and cannot satisfy the intricate demands of ASV berthing tasks due to limited assistance. In this study, a novel ASV-centric BAS comprising the following four distinctive modules is presented: localization, navigable region acquisition, idle berth determination, and berthing status estimation. The localization module employs a simultaneous localization and mapping (SLAM) algorithm that fuses LiDAR and real-time kinematic (RTK) data to achieve precise ship positioning. The navigable region acquisition module involves segmenting point clouds based on horizontal emission angles and identifies the nearest points within each segment to establish the navigable region. The idle berth determination module employs a shoreline-constrained point cloud linear fitting algorithm, thereby extracting docking zones from nearest point clouds and acquiring idle berth information. The berthing status estimation module leverages the pre-established berth lines to compute essential berthing parameters, encompassing the berthing distance, approaching angle, berthing speed, and yaw rate. Experimental validation shows the high accuracy of the proposed BAS, which maintains a 0.438494 m root mean square error for localization, 0.482% relative error in the berthing distance, and 0.493% relative error in the approaching angle. Furthermore, both the navigable region acquisition and idle berth determination modules consistently provide accurate navigable region and idle berth information. Notably, comparative analysis against existing LiDAR-based solutions demonstrates significant performance improvements of 97.86% in the berthing distance accuracy and 98.12% in the approaching angle accuracy.

Parking Infrastructure Location Design and User Pricing in the Prospective Era of Autonomous Vehicle Operations

Parking Infrastructure Location Design and User Pricing in the Prospective Era of Autonomous Vehicle Operations

Pedestrian behavior interacting with autonomous vehicles during unmarked midblock multilane crossings: Role of infrastructure design, AV operations and signaling

One of the main challenges autonomous vehicles (AVs) will face is interacting with pedestrians, especially at unmarked midblock locations where the right-of-way is unspecified. This study investigates pedestrians' crossing behavior given different roadway centerline features (i.e., undivided, two-way left-turn lane, and median) and various AV operational schemes portrayed to pedestrians through on-vehicle signals (i.e., no signal, yellow negotiating indication, and yellow/blue negotiating/no-yield indications). This study employs virtual reality (VR) to simulate an urban unmarked midblock environment where pedestrians interact with AVs as they cross a four-lane arterial roadway. Results demonstrate that both roadway centerline design features and AV operations and signaling significantly impact pedestrians' unmarked midblock crossing behavior, including the waiting time at the curb, waiting time in the middle of the road, and the total crossing time. But only the roadway centerline design features significantly impact the walking time. Participants in the undivided scene spent a longer time waiting at the curb and walking on the road than in the median and TWLTL scenes, but they spent a shorter time waiting in the middle of the road. Compared to the AV without a signal, the design of yellow signal significantly reduced pedestrian waiting time at the curb and in the middle. But yellow/blue signals significantly increased the pedestrian waiting time. Interaction effects between roadway centerline design features and AV operations and signaling are significant only for waiting time in the middle of the road. Other factors, such as demographics, past behaviors, and walking exposure of pedestrians, are also explored. Results indicate that older individuals tend to wait longer before making crossing decisions, and pedestrians’ past crossing behaviors and past walking exposures do not significantly impact pedestrian walking behavior interacting with AV.

A new ELECTRE-based decision-making framework with spherical fuzzy information for the implementation of autonomous vehicles project in Istanbul

This research study keenly employs the ground-breaking theory of ELECTRE method in order to advance the literature with a more competent technique for the frequently implemented and incredibly practical model of spherical fuzzy sets. The eminent characteristics, broader approach and adaptable model of spherical fuzzy set empowers the proposed strategy to captivate each type of the imprecise human judgement (whether in favor, against, neutral or hesitant) using a dependent and three independent membership grades. The remarkable novelty of the proposed approach is its successful application even with no criteria weights knowledge. Furthermore, the proposed technique processes the spherical fuzzy data and examines different types of preferences among alternatives using three threshold values. These preferences contribute to classify the outranking relations among the alternatives within five different levels of dominance. Later on, the iterative approach of distillation is deployed to figure out the final ranking. The proposed strategy is applied to meet the challenges in the implementation of autonomous vehicles in Turkey. After a through evaluation of five corridors via proposed technique in reference to fifteen criteria, metrobus corridor appears as the most appropriate for the autonomous vehicle operation. In the last, the consistency of the proposed approach is delineated via comparative analysis with existing spherical fuzzy CODAS and spherical fuzzy PROMETHEE approaches and other convincing evidences.

Evaluation of autonomous vehicle applications in smart airports using Dombi Bonferroni mean operator based CIVL-BWM-TODIM decision making methodology

Recent advancements on autonomous vehicles (AVs) have revolutionized the aviation industry, providing great potential to enhance sustainability for this fuel-intensive industry. Diverse operation types of AVs at airports have emerged (e.g., for baggage/passenger movement). This study aims to provide a guideline on how to switch from the traditional human-driving vehicle operations to AV operations, to help improve the smartness and sustainability of airport operations. Five alternative operation types are evaluated under ten criteria. We propose a new CIVL-BWM-TODIM with Dombi Bonferroni mean operator (DBM) decision framework. The combination of the Continuous Interval-Valued Linguistic tool (CIVL) and the Best Worst Method (BWM) improves the criterial evaluation consistency, and the integration of CIVL and TODIM increases the evaluation accuracy on human language and psychological feelings. Besides, the DBM operator helps effectively integrate information from different experts. Results suggest that applying AVs for both functional activities and scheduled people delivery is the best alternative to build smart and sustainable airports. Sensitivity analyses are conducted to examine the impacts of various parameters on decision making. Moreover, comparative analysis over existing decision-making methods are carried out to validate the merits of the proposed approach.

An Empirical Study on Development of Traffic Safety Facilities for Safe Autonomous Vehicle Operation in Construction Areas

An Empirical Study on Development of Traffic Safety Facilities for Safe Autonomous Vehicle Operation in Construction Areas