Search

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies. [ABSTRACT FROM AUTHOR]

Autonomous vehicle storage and retrieval systems (AVS/RSs) are widely used in e-commerce warehouses due to their high throughput and flexibility. In such systems, storage and retrieval transactions are performed by lifts and vehicles. This paper focuses on the sequencing retrievals problem in an AVS/RS, which is an important problem for daily operations. We formulate this sequencing problem as a mixed-integer program to determine a retrieval sequence for the lift and the vehicles, one that minimises the makespan. A dynamic programming approach is proposed to solve the sequencing problem to optimality. However, the solution time of the dynamic programming method is exponentially increasing in the number of retrieval requests. To be more practical, we present a beam search heuristic that can solve large-sized instances in reasonable time. Computational experiments verify that near-optimal solutions can be found by the beam search heuristic. Compared to commonly used heuristics and straightforward heuristics, the beam search decreases the makespan by up to 15%. Finally, we analyse how vehicle modes impact the makespan, showing evidence that a small makespan can be achieved when considering a realistic mode of vehicles. [ABSTRACT FROM AUTHOR]

This project convened a series of meetings and workshops to prioritize listening to multi-sector stakeholders from local government, advocacy, and industry in US cities where autonomous vehicles are operating. The objective was to listen and learn from all stakeholders, raise issues surrounding accessibility and equity, and to solicit responses. Key findings from the workshops include a consensus across the three sectors on the need for good channels of multi-stakeholder communication, and voices across all sectors agreed on the importance of disability access and serving diverse populations. Many parties, representing voices from all sectors, recognized that federal regulatory activities appear to be moving too slowly. Preventing any roadway incidents is a priority for many stakeholders, and some suggest a playbook for handling day-to-day roadway issues and common standards for first-responder interactions. Disability access is a high priority across all sectors, and there many see nearer term to accommodations for blind, hearing-impaired riders, but the timeline for providing service to people with non-folding wheelchairs is less clear. There is also ongoing debate surrounding the limits of regulatory purview, the role for cities, and how to actualize equitable expansion into rural areas. There is more work to do to advance a multi-sector dialogue around the role for local governments and community-based organizations in shepherding a safe, equitable and sustainable expansion of autonomous vehicles.

Robotaxi services, or rideshare operated by autonomous vehicles, present an opportunity for independent and convenient transportation for people with disabilities. The proliferation of robotaxis in California has been met with mixed reactions from the disability community. To better understand perceptions of and expectations for robotaxis, this report uses semi-structured interviews with representatives from disability advocacy organizations. For many people with disabilities, especially for people with intellectual, developmental, and/or physical disabilities, robotaxis are inaccessible. Given the intricacies of accommodating a wide audience, not all interviewees were confident that robotaxi design and programming will be inclusive. Some interviewees trusted that autonomous vehicle companies will independently pursue accessibility features in their robotaxis. Other interviewees regarded statewide accessibility standards as essential to ensure accessibility. Overall, interviewees shared that people with disabilities want to be included in the process of designing, testing, and regulating robotaxis. Implications for accessible robotaxi governance in California is presented, with an emphasis on wheelchair access, considering impact on public transportation service, and centering people with disabilities. Interviewees also discussed ideal accommodations that would allow a broad audience to request a ride, board the robotaxi, communicate with the operation system, and exit the vehicle. These findings may be useful to AV companies as they consider how robotaxis can accommodate people with a variety of disabilities.

This report explores opportunities for employing autonomous driving technology to dampen stop-and-go waves on freeways. If successful, it could reduce fuel consumption and emissions. This technology was tested in an on-road experiment with 100 vehicles over one week. Public stakeholders were engaged to assess the planning effort and feasibility of taking the technology to the next level: a pilot involving 1000+ vehicles over several months. Considerations included the possible geographical boundaries, target fleets of vehicles, and suitable facilities such as bridges or managed lanes. Flow smoothing technology may improve the user experience and operations of managed lanes or bridges, however it may require external incentives such as reduced tolls to entice the traveling public to use it. This must be matched with other goals such as verifying vehicle occupancy. It might be possible for some hybrid solution that addresses both challenges to provide a way forward. A concept of operations needs to be developed specifically for a target road geometry and a California partner. This concept should benefit from lessons learned from previous pilot projects and will need to be defined so as to achieve both (1) a penetration rate sufficient to achieve measurable effects; and (2) sufficient quality and quantity of data to confirm benefits.

Autonomous vehicles (AVs) heavily rely on machine learning-based perception models to accurately interpret their surroundings. However, these crucial perception components are vulnerable to a range of malicious attacks. Even though individual attacks can be highly successful, the actual security risks such attacks can pose to our daily life are unclear. Various factors, such as lack of stealthiness, cost-effectiveness, and ease of deployment, can deter potential attackers from employing certain attacks, thereby reducing the actual risk. This research report presents the first quantitative risk assessment for physical adversarial attacks on AVs. The specific focus is on attacks on AV’s perception components due to their highly critical function and representation in existing research. The report defines the daily-life risk as the likelihood that a given type of attack will be employed in real life and the authors develop a problem-specific risk scoring system and accompanying metrics. They perform an initial evaluation of the proposed risk assessment method for all the reported attacks on AVs from 2017 to 2023. They quantitatively rank the daily-life risks posed by each of eight different categories of attacks s and find three attacks with the highest risks: 2D printed images, 2D patches, and coated camouflage stickers, which deserve more focused attention for potential future mitigation strategy development and policy making.

In this article, a compact and high-gain, wide steering range and very low cross-polarization level holographic metasurface for beam steering is presented. The triangular modulated impedance surface is used for radiating the desired wave. The direction of the radiated beam is controlled through the varactor diode by changing the surface impedance of the device. The two prototypes of the designed device have been fabricated with two different varactor diodes. A higher gain of 11.7 dBi with a lower beam steering range of 25° and a radiation efficiency above 70% has been obtained from the device with a varactor diode SMV1405-079LF having lower parasitic resistance and larger capacitance properties. However, another fabricated device with higher parasitic resistance and lower capacitance property varactor diode SMV2201-40LF gives a lower gain of 9.1 dBi, a radiation efficiency below 55%, and a larger beam steering range of 61°. The experimental results are in good agreement with the simulation ones. The proposed device can be used for various applications like autonomous vehicles, toll plazas, and satellite communication. [ABSTRACT FROM AUTHOR]

Due to the cyber-physical nature of robotic vehicles, security is especially crucial, as a compromised system not only. exposes privacy and information leakage risks, but also increases the risk of harm in the physical world. As such, in this paper, we explore the current vulnerability landscape of robotic vehicles exposed to and thus remotely accessible by any party on the public Internet. Focusing particularly on instances of the Robot Operating System (ROS), a commonly used open-source robotic software framework, we performed new Internet-wide scans of the entire IPv4 address space, identifying, categorizing, and analyzing the ROS-based systems we discovered. We further performed the first measurement of ROS scanners in the wild by setting up ROS honeypots, logging traffic, and analyzing the traffic we received. We found over 190 ROS systems on average being regularly exposed to the public Internet and discovered new trends in the exposure of different types of robotic vehicles, suggesting increasing concern regarding the cybersecurity of today’s ROS-based robotic vehicle systems.

Summary: Nowadays, intelligent transportation systems pay a lot of attention to autonomous vehicles it is believed that an autonomous vehicle improves mobility, comfort, safety, and energy efficiency. Making decisions is essential for the development of autonomous vehicles since these algorithms must be able to manage dynamic and complex urban crossings. In this research an optimal deep BiLSTM‐GAN classifier to detect the movement of smart vehicles, initially the preprocessing stage is performed to decrease noise in the received data after that the essential regions are next be extracted in the region of interest (ROI) to make the right decision. The extracted data are forwarded to the GAN for road segmentation as well as the optimized deep BiLSTM classifier, which recognizes the traffic sign, simultaneously making it possible to do a modified Hough line‐based maneuver prediction using the segmented information from the roads. Finally, the GAN determines the lane, and the BiLSTM predicts the traffic sign. The K‐nearest neighbor (KNN)‐based autonomous vehicle movement controllers are used to make the decision based on the predicted traffic sign and information about the lane. The proposed HSO algorithm was developed as the outcome of the common fusion of hawk and swarm optimization. Based on lane detecting achievements, at training percentage (TP) 90, the accuracy is 91.75%, Peak signal‐to‐noise ratio (PSNR) is 64.84%, mean square error (MSE) is 28.78, and mean absolute error (MAE) is 20.20, respectively, similarly based on the traffic sign prediction achievements at TP 90, the accuracy is 93.71%, sensitivity is 95.15%, specificity is 93.91%, and MSE is 28.78%, respectively. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we delve into the intricate problem of lateral control in autonomous vehicles, utilizing adaptive event triggering, dynamic quantizers, and incorporating stochastic sampling. By integrating the Adaptive Event‐Triggering Scheme (AETS) and dynamic quantizer in dual channels—namely the sensor‐to‐controller and controller‐to‐observer channels—we aptly cater to the multifaceted road conditions faced by autonomous vehicles. Moreover, in light of Denial of Service (DoS) attacks, our controllers ensure system stability amidst stochastic sampling. While ensuring an effective reduction in the amount of network communication data, the efficiency of the output feedback controllers is also significantly improved, thus enabling the closed‐loop system to be strictly dissipative performance stabilized. To substantiate the efficacy of our proposed method, simulation experiments were rigorously conducted using the Carsim‐Simulink platform, highlighting the enhanced safety of autonomous vehicles in real‐world operations. [ABSTRACT FROM AUTHOR]

Summary: The primary objective of this paper is to enhance the efficient utilization of communication resources. To achieve this, the paper delves into the disturbance observer based adaptive heading control strategy for Unmanned Marine Vehicles with event‐triggered and input quantization. Furthermore, in order to mitigate the impact of slow time‐varying external disturbances within the control system, the disturbance observer is employed for estimation. Within the context of a networked control, control input is subjected to quantization via an input quantizer, and the process of input quantization is described by using a linear analytical model. Importantly, no foreknowledge of quantization parameters is necessary for the quantized feedback controllers. Subsequently, the sliding mode control method is combined with event‐triggered mechanismss to design quantization feedback control system. The stability of the closed‐loop system is established in line with the fundamental tenets of Lyapunov stability theory, validating the bounded nature of both observation and heading tracking control errors. The effectiveness of the proposed heading control scheme is further underscored through a series of simulation experiments. [ABSTRACT FROM AUTHOR]

Background: The purpose of this work is to develop a method of traffic optimization at intersections using deep reinforcement learning (DRL) by creating models for controlling autonomous vehicles and traffic lights in a simulated environment. The practical value of the research is to compare the performance of existing algorithms at both traffic light control and autonomous vehicle control tasks, compare their results in different scenarios, and create a framework for testing more such methods in the future. Methods: The goal of the research is achieved by using DQN, SAC, A2C, and PPO algorithms for the task of driving vehicles across the intersection and for the task of controlling the traffic lights in a simulated environment. The comparison of the selected algorithms is done using metrics such as average reward, average waiting time (AWT), average queue length (AQL), and training time. For the task of controlling vehicles, the reward function is modeled by taking into consideration of the distance to the goal, i.e., the part of the intersection to be reached by the car, and the agent is penalized for violating traffic rules. The agents that were trained to control the phases of traffic lights have their rewards modeled based on minimizing the AWT and AQL metrics. Additionally, the traffic light control agents are trained with both discrete and continuous action spaces. Results: Most DRL algorithms suffer from the problem of catastrophic forgetting or catastrophic interference, especially DQN. This problem can be remedied by using HER or other replay buffers. The PPO algorithm, especially with a low value of the entropy coefficient hyperparameter, produced the most stable results during training, although it required more iterations to achieve these results. It was found that the dependence of the reward function on the AWT and AQL metrics has a significant impact on the results of the algorithm training. Conclusions: This work compared methods for controlling traffic lights and autonomous vehicles in a custom simulated environment at a signalized intersection. A novel environment was developed, compatible with existing frameworks such as Gym, for the task of training and validating reinforcement learning algorithms. [ABSTRACT FROM AUTHOR]

Road target recognition ability of automotive millimetre-wave (mmWave) radar is crucial in areas such as autonomous driving, advanced driver assistance systems (ADAS), and automated emergency braking systems (AEBS). Current mmWave radar systems are primarily utilized for speed, distance, and angle measurements, with limited capability or unsatisfactory performance in target classification. In response to this limitation, this paper proposes a novel method for road target recognition, using radar range-Doppler spectrum as input data and introducing a global attention mechanism and SCConv module. First, the range-Doppler spectrum is processed by the clutter removal algorithm block to remove background clutter and vibration interference. Second, considering the small features and susceptibility to noise interference in the range-Doppler spectrum, we have devised a deep residual network based on a global attention mechanism, significantly enhancing the model's accuracy in range-Doppler spectrum classification. Finally, we introduce the SCConv module and improve the downsampling module in ResNet to enhance the model's classification accuracy. Experimental results demonstrate that the model achieves an average accuracy of 99.79% in classifying six types of road targets, significantly outperforming other methods. This research is of significant importance in advancing the understanding of road traffic conditions by autonomous driving systems and enhancing system safety. [ABSTRACT FROM AUTHOR]

Consumer electronics (CE) companies have the potential to significantly contribute to the advancement of autonomous vehicles and their accompanying technology by providing security, connectivity, and efficiency. The Consumer Autonomous Vehicles market is set for significant growth, driven by growing awareness and implementation of sustainable practices using computing technologies for traffic flow optimization in smart cities. Businesses are concentrating more on eco-friendly solutions, using AI, communication networks, and sensors for autonomous city navigation, giving safer and more efficient mobility solutions in response to growing environmental concerns. Object detection is a crucial element of autonomous vehicles and complex systems, which enables them to observe and react to their surroundings in real-time. Multiple autonomous vehicles employ deep learning (DL) for detection and deploy specific sensor arrays custom-made to their use case or environment. DL processes sensory data for autonomous vehicles, enabling data-driven decisions on environmental reactions and obstacle recognition. This paper projects a Galactical Swarm Fractals Optimizer with DL-Enabled Object Detection for Autonomous Vehicles (GSODL-OOAV) model in Smart Cities. The presented GSODL-OOAV model enables the object identification for autonomous vehicles properly. To accomplish this, the GSODL-OOAV model initially employs a RetinaNet object detector to detect the objects effectively. Besides, the long short-term memory ensemble (BLSTME) technique was exploited to allot proper classes to the detected objects. A hyperparameter tuning procedure utilizing the GSO model is employed to enhance the classification efficiency of the BLSTME approach. The experimentation validation of the GSODL-OOAV technique is verified using the BDD100K database. The comparative study of the GSODL-OOAV approach illustrated a superior accuracy outcome of 99.06% over present innovative approaches. [ABSTRACT FROM AUTHOR]

This study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane. Based on the delay analysis, the spatiotemporal resource collaborative optimization model of lane setting and signal timing is established to minimize the average delay. Subsequently, in the buffer zone, a graph‐theoretic‐based sorting and platooning model provides a clear and concise representation of the transformation process from the initial state to the target state of vehicles, enabling the platoon formation. Additionally, trajectory optimization is integrated into the collaborative control framework by the optimal control model and car‐following model in the passing zone. Simulation experiments and sensitivity analyses demonstrate the effectiveness of the proposed framework in reducing average vehicle delay, improving fuel consumption, and coping with changing traffic demand at intersections. [ABSTRACT FROM AUTHOR]

Nowadays, the progress of technology covers, among other things, the development of modern techniques and high technologies used in land surveys. Unmanned aerial vehicles (UAVs), as a good alternative to conventional land survey techniques, have currently played an increasing role. The advantages of using unmanned aerial vehicles in photogrammetric measurements include a relatively short mission time for large-area surveys. In addition, photogrammetric products have a wider range of applications compared with conventional geodetic surveys. Many scientific publications delve into the quality of photogrammetric products, but the accuracy of UAVs in the context of geodetic standards has not been investigated in full. In this paper, the authors attempt to fill the observed research gap. The research has analysed the position of objects recorded in geodetic databases referring to their counterparts based on an accurate "true" orthophotomap from a photogrammetry campaign employing an unmanned aerial vehicle. The outcomes were referenced with land survey accuracy standards set out by relevant legislation. To ensure a smooth assessment of the result's accuracy a computing algorithm with a module for selecting comparable points and verifying the results was designed. The tool can be implemented in surveys carried out in any area thanks to open-source GIS software. The analysis showed that a detailed orthophotomap delivered using UAVs can be a valuable data source on objects recorded in geodetic databases covering selected cadastral and topographic objects and land development components. A general verification of the accuracy and validity of a geodetic numerical map and preliminary detection of areas for potential updates can be a particularly useful application of photogrammetry. [ABSTRACT FROM AUTHOR]

RGB-Thermal semantic segmentation is important in widespread applications in adverse illumination conditions, such as autonomous driving and robotic sensing. However, most existing methods ignore the feature differences between the two modalities and do not effectively exploit and handle the features at different levels. In this paper, we present a novel multimodal feature fusion network named HEFANet, which effectively enhances the interaction and fusion of features. Concretely, we propose a Cross-layer and Cross-modal Feature Descriptor module (CCFD) to mitigate differences between different multimodal data and to mine the valuable and correlated features of cross-layers. To effectively fuse multimodal features at different levels, we propose a Multi-modal Interleaved Sparse Self-Attention module (MISSA) to aggregate rich spatial semantic information in the earlier layers. Then, we propose the Spatial Interaction and Channel Selection module (SICS) in the last layer to enhance the representation of rich contextual features and highlight important information by channel communication interactions for optimal sparse feature aggregation selectively. Extensive experiments were carried out on three publicly available datasets (MFNet, PST900, and FMB), and achieved new state-of-the-art results. The code and results are available at https://github.com/shenzw21/HEFANet. [ABSTRACT FROM AUTHOR]

With the continuous development of science and technology, automatic assisted driving is becoming a trend that cannot be ignored. The You Only Look Once (YOLO) model is usually used to detect roads and drivable areas. Since YOLO is often used for a single task and its parameter combination is difficult to obtain, we propose a Taguchi-based YOLO for panoptic driving perception (T-YOLOP) model to improve the accuracy and computing speed of the model in deteching drivable areas and lanes, making it a more practical panoptic driving perception system. In the T-YOLOP model, the Taguchi method is used to determine the appropriate parameter combination. Our experiments use the BDD100K database to verify the performance of the proposed T-YOLOP model. Experimental results show that the accuracies of the proposed T-YOLOP model in deteching drivable areas and lanes are 97.9 and 73.9%, respectively, and these results are better than those of the traditional YOLOP model. Therefore, the proposed T-YOLOP model successfully provides a more reliable solution for the application of panoramic driving perception systems. [ABSTRACT FROM AUTHOR]

The study aims to investigate how much young Hungarian city dwellers would accept potential urban environment changes triggered by AVs. In the full-profile conjoint analysis, 1011 young people expressed their preferences through the evaluation of 18 printed cards in multiple steps during in-person data collection. As a result, the ‘city of the future’ most preferred by youngsters can be described with the help of the partial utility of attribute levels and the constant value. Their ideal city envisions roads exclusively for AVs with new lane allocations, no drones, pedestrian-only sidewalks equipped with sensors, and frequent 5G system to boost connectivity. [ABSTRACT FROM AUTHOR]

ABSTRACT This article investigates the merging control for underactuated connected and automated vehicle (CAV) systems with mismatched uncertainty. The objective is to guarantee the safe and prescribed dynamic performance of the system during multilane merging. For CAV multidimensional motions, a strongly coupled vehicle dynamics with time‐varying uncertainties is constructed. For the nominal system, the control objectives are designed as system constraints, with equality constraints guaranteeing merging and time‐varying inequality constraints guaranteeing dynamic performance bounds. Based on the diffeomorphism method and bounded constraint, the system constraints are reconstructed to a unified representation. Combining system constraints and underactuated structure, the nominal controller is derived based on constraint‐following. For the uncertain system, the uncertainty is decomposed into matched and mismatched portions by orthogonal decomposition. The adaptive robust controller is designed based only on matched uncertainty. Through Lyapunov minimax analysis, the control renders the errors uniformly bounded and uniformly ultimately bounded. Simulation results show that merging and platooning are effectively achieved with the proposed control. The system has excellent transient and steady state performance even in the presence of time‐varying uncertainties. [ABSTRACT FROM AUTHOR]

Ensuring representativeness of collected samples is the most critical requirement of water sampling. Unmanned surface vehicles (USVs) have been widely adopted in water sampling, but current USV sampling path planning tend to overemphasize path optimization, neglecting the representative samples collection. This study proposed a modified A* algorithm that combined remote sensing technique while considering both path length and the representativeness of collected samples. Water quality parameters were initially retrieved using satellite remote sensing imagery and a deep belief network model, with the parameter value incorporated as coefficient Q in the heuristic function of A* algorithm. The adjustment coefficient k was then introduced into the coefficient Q to optimize the trade-off between sampling representativeness and path length. To evaluate the effectiveness of this algorithm, Chlorophyll-a concentration (Chl-a) was employed as the test parameter, with Chaohu Lake as the study area. Results showed that the algorithm was effective in collecting more representative samples in real-world conditions. As the coefficient k increased, the representativeness of collected samples enhanced, indicated by the Chl-a closely approximating the overall mean Chl-a and exhibiting a gradient distribution. This enhancement was also associated with increased path length. This study is significant in USV water sampling and water environment protection. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicles (CAVs) will greatly impact the lives of individuals with visual impairments, but how they differ in expectations compared to sighted individuals is not clear. The present research reports results based on survey responses from 114 visually impaired participants and 117 panel recruited participants without visual impairments, from Germany. Their attitudes towards autonomous vehicles and their expectations for consequences of wide-spread adoption of CAVs are assessed. Results indicate significantly more positive CAV attitudes in participants with visual impairments compared to those without visual impairments. Mediation analyses indicate that visually impaired individuals' more positive CAV attitudes (compared to sighted individuals') are largely explained by higher hopes for independence, and more optimistic expectations regarding safety and sustainability. Policy makers should ensure accessibility without sacrificing goals for higher safety and lower ecological impact to make CAVs an acceptable inclusive mobility solution. IMPLICATIONS FOR REHABILITATION: Autonomous vehicles can significantly enhance the independence of individuals with visual impairments by providing a reliable and convenient mode of transportation. Autonomous vehicles could offer advanced wayfinding capabilities, including indoor navigation in complex environments, which can be particularly beneficial for people with visual impairments. Comprehensive training programs should be available to help individuals with visual impairments become familiar with autonomous vehicle technology, ensuring they can use it effectively and confidently. Regular feedback from individuals with visual impairments should be sought during the design and development phases to ensure that their specific needs and preferences are taken into account. [ABSTRACT FROM AUTHOR]

This article studies a data-driven prescribed performance platooning control method for nonlinear connected automated vehicle systems (CAVs) under aperiodic denial-of-service (DoS) attacks. Firstly, the dynamic linearization technique is employed to transform the nonlinear CAV system into an equivalent linearized data model. Secondly, to improve the system's transient performance, a prescribed performance transformation (PPT) scheme is proposed to transform the constrained output into the unconstrained one. In addition, an attack compensation mechanism is designed to reduce the adverse impact. Combining the PPT scheme and the attack compensation mechanism, the data-driven adaptive platooning control scheme is proposed to achieve the vehicular tracking control task. Lastly, the merits of the developed control method are illustrated by an actual simulation. [ABSTRACT FROM AUTHOR]

The deployment of electric, connected, and autonomous vehicles on public roads presents a significant challenge that can be addressed through previously established frameworks developed globally for implementing these technologies as part of an urban living lab (ULL). This systematic review, based on records from four distinct databases, focuses on projects that have conducted deployments of self-driving technologies in streets within urban environments. The review describes relevant information about various initiatives, including a classification of the stages of development reached according to the urban area covered, safety considerations, and lessons learned for optimal deployment. On-board sensing technology, digital infrastructure, and energy and communication systems emerge as the essential components of a ULL with autonomous vehicles (AVs). A crucial goal for smart cities is ensuring the scalability of large-scale deployments of such ULLs for safe, clean, and future mobility experimentation. This can only be achieved through effective coordination among academia, government, industry, and society to guarantee the successful integration of multiple projects in a unique environment. [ABSTRACT FROM AUTHOR]

With the continuous advancement of autonomous driving technology, an increasing number of high-definition (HD) maps have been generated and stored in geospatial databases. These HD maps can provide strong localization support for mobile robots equipped with light detection and ranging (LiDAR) sensors. However, the global localization of heterogeneous robots under complex environments remains challenging. Most of the existing point cloud global localization methods perform poorly due to the different perspective views of heterogeneous robots. Leveraging existing HD maps, this paper proposes a base-map-guided heterogeneous robots localization solution. A novel co-view context descriptor with rotational invariance is developed to represent the characteristics of heterogeneous point clouds in a unified manner. The pre-set base map is divided into virtual scans, each of which generates a candidate co-view context descriptor. These descriptors are assigned to robots before operations. By matching the query co-view context descriptors of a working robot with the assigned candidate descriptors, the coarse localization is achieved. Finally, the refined localization is done through point cloud registration. The proposed solution can be applied to both single-robot and multi-robot global localization scenarios, especially when communication is impaired. The heterogeneous datasets used for the experiments cover both indoor and outdoor scenarios, utilizing various scanning modes. The average rotation and translation errors are within 1° and 0.30 m, indicating the proposed solution can provide reliable localization support despite communication failures, even across heterogeneous robots. [ABSTRACT FROM AUTHOR]

The vehicular ad-hoc network (VANET) is a new subcategory of mobile ad-hoc networks that has excellent potential application in intelligent transportation systems. The cyberattack on vehicles can lead to privacy leaks and financial losses and could even become a national public safety issue. In this paper, a multi-level Intrusion Detection System based on Machine Learning (ML) algorithms is developed in order to detect multiple types of attacks on VANET. The proposed model combines two feature selection techniques: random forest (RF) and fast correlation-based filter (FCBF) and four tree-based algorithms: extreme gradient boosting decision tree, RF, decision tree, and ExtraTrees, for classification performance. Moreover, the model is evaluated using CICIDS2017 dataset and Python ML libraries in Jupyter Notebook such as scikit-learn and Pandas. Experiment results show that the proposed model using the stacking method achieves 99.86% attack detection accuracy, 99.85% precision with hypo-parameter (HPO), and 99.83% attack detection accuracy without using HPO. The experimental findings on the vehicular network demonstrate the feasibility of the proposed solution and the ability to implement it in real-time environments. [ABSTRACT FROM AUTHOR]

This article reassesses the Uppsala model nearly fifty years after its formulation by Johanson and Vahlne (1977), showing the effect of cooperation among firms in their internationalization process through a graphical analysis of the internationalization mechanism’s risk formula. This research contributes to the field of international business by extending the explanatory power of the Uppsala model to the scope of international alliances and joint ventures, with practical implications for decision-making between these two modes of cooperation, particularly in terms of the leveraging effects of knowledge and resources, either through contractual agreements or by establishing third-party’s autonomous vehicles to the partnering firms. [ABSTRACT FROM AUTHOR]

As autonomous vehicles (AV) are becoming more pervasive in transportation, it is important to consider drivers' perceptions of these vehicles. The existing research has investigated taking over AV control, its safety and acceptance. However, the preferences for self-driving in multiple traffic situations have not been extensively investigated. In Part I, we aim to bridge these gaps by investigating such preferences in high and low traffic complexities. Eighty-eight participants in North America were recruited. They viewed video recordings of driving in the city of Toronto, the regional municipality of Waterloo and highways to answer survey questions. Their responses regarding perceptions and preferences were simply analysed using descriptive statistics and Chi-square test at various traffic situations with two traffic complexities. It showed strong preferences for self-driving in most low complexity situations and certain situations in both complexities. These findings can suggest a few applicable design principles of AV decision system regarding traffic situation-based and biased perceptions-based user preferences. In Part II, we extend our analyses to user preferences for multiple two-stage actions of AVs and suggest additional design principles of the system with a more-in-depth insights. [ABSTRACT FROM AUTHOR]

Introducing automated vehicles into road traffic and robots into urban public environments puts artificial agents in space-sharing conflicts with humans. Humans anticipate, avoid, and smoothly resolve space-sharing conflicts by implicit and explicit communication enabled by uniquely human shared intentionality. Smooth coordination among artificial, partly automated, teleoperated, and human agents needs to be supported by coactive design optimizing observability, predictability, and directability to foster robustness of operation. Available humans should be employed by 'wizard interfaces' to support automation, for example, in recognizing intentions, in anticipating difficulties, and in predicting for foresighted coordination. Human capabilities are not just helpful for efficiency and robustness but necessary to cope with exceptional events and failures. For quickly removing blocks and resuming operation, available humans should be employed and authorized by measures of 'shepherd raising'. Because non-human agents are at a disadvantage in public environments and potentially block operation for extended intervals if they are bullied or denied help, benevolence and cooperation are essential in hybrid societies. Cooperation in public environments is higher in functional societies and can be fostered towards artificial agents in particular by coactive design increasing trust, by influencing mindless and mindful anthropomorphism, and further determinants of reciprocal altruism. RELEVANCE STATEMENT: Embodied technologies moving as artificial agents in public environments entail challenges of coordination with human agents. Artificial agents in public environments are partially or temporarily operated by humans and should ideally function smoothly as partners in joint activities. From the perspective of Ergonomics and Human Factors, the necessity and benefits of employing humans for robustness of operation and handling exceptional events are highlighted. A range of measures for gaining and supporting human contributions are suggested. [ABSTRACT FROM AUTHOR]

With the gradual increase in the number of vehicles on the road, the number of traffic accidents has also increased. Inappropriate lane changing is an important cause of traffic accidents. Based on the above problems, it is of great significance to study vehicle trajectory prediction and collision warning under lane changing conditions. Therefore, this paper proposes a collision warning algorithm based on S-shaped conditions for vehicle trajectory prediction and collision warning. The method can provide collision warnings so that vehicle collisions can be avoided. In this study, the trajectories of the surrounding vehicles in the constructed scenario are predicted by long- and short-term neural networks. At the same time, a B-spline curve is used to plan the lane change path of the vehicle when it encounters an obstacle. Next, a linear quadratic regulator trajectory tracking control algorithm is used to track the planned path. Finally, the predicted trajectories of the surrounding vehicles and the planned trajectories of the experimental vehicles are simulated and analyzed to provide collision warning. The simulation results show that the method can provide a 2s warning of whether a vehicle can change lanes or not. The study combines trajectory prediction and path planning, which is an important reference for collision warning research in autonomous driving. [ABSTRACT FROM AUTHOR]

With the growing development and deployment of automated vehicles (AVs), it is crucial to understand the associated risks and factors contributing to collisions involving AVs. California possesses an immense amount of publicly available data from AV testing due to the requirement laid out by the California Department of Motor Vehicles (DMV), which requires all automated vehicle operators to report collisions for any level of severity. However, this information is reported in specific forms and requires a laborious task to aggregate data from these reports. This study creates an automated data extraction system for these reports and analyses collision characteristics using logistic regression models as well as XGBoost models with SHapley Additive exPlanation (SHAP) interpretation. Additionally, these characteristics are matched with the characteristics of non-automated vehicles (non-AVs) for the same region. The results of the study indicate that rear-end collisions are the most common type of collision observed in currently deployed AVs. The analysis further revealed an increased likelihood of injury-prone rear-end collisions in AVs at intersections compared to non-AVs. Transportation policymakers and researchers should take these safety concerns into account when addressing AV deployment and developing appropriate measures to mitigate collision risks in mixed fleet conditions. [ABSTRACT FROM AUTHOR]

Longitudinal control of autonomous vehicles (AVs) has long been a prominent subject and challenge. A hierarchical longitudinal control system that integrates deep deterministic policy gradient (DDPG) and proportional–integral–derivative (PID) control algorithms was proposed in this paper to ensure safe and efficient vehicle operation. First, a hierarchical control structure was employed to devise the longitudinal control algorithm, utilizing a Carsim‐based model of the vehicle's longitudinal dynamics. Subsequently, an upper controller algorithm was developed, combining DDPG and PID, wherein perceptual information such as leading vehicle speed and distance served as input state for the DDPG algorithm to determine PID parameters and output the desired acceleration of the vehicle. Following this, a lower controller was designed employing a PID‐based driving and braking switching strategy. The disparity between the desired and actual accelerations was fed into the PID, which calculated the control acceleration to enact the driving and braking switching strategy. Finally, the effectiveness of the designed control algorithm was validated through simulation scenarios using Carsim and Simulink. Results demonstrate that the longitudinal control method proposed herein adeptly manages vehicle speed and following distance, thus satisfying the safety requirements of AVs. [ABSTRACT FROM AUTHOR]

Research in the field of dynamic eco-driving has been primarily coupled with connected and automated vehicles which are equipped with automation functions that can accurately execute energy-efficient speed advice. Advisory dynamic eco-driving that entails driver adaptation to energy-efficient speed advice has received lesser attention although mixed traffic is expected to prevail in the forthcoming decades. This study developed a decision tree model based on real-world data collected during the pilot deployment of an advisory speed advice service along an urban arterial corridor to emulate driver adaptation to speed advice. The decision tree model was integrated into the control logic of an enhanced velocity planning algorithm to replicate the behavior of manually driven connected vehicles along dynamic eco-driving service zones in a microscopic traffic simulation environment. The conducted simulation analysis encompassed scenarios with varying penetration rates of advisory dynamic eco-driving technology, automated dynamic eco-driving technology and manually driven unequipped vehicles. Evaluation of simulation scenarios was based on the estimation of several environmental, traffic efficiency and surrogate safety measures. Simulation results indicated that performance of advisory dynamic eco-driving depends on driver adaptation to speed advice and ranges between that of manually driven unequipped vehicles and its automated counterpart. Moreover, geometrical and operational characteristics of intersection approaches comprising dynamic eco-driving service zones can influence driver adaptation to speed advice. Environmental, safety and traffic efficiency benefits are maximized in the case of vehicle fleets fully equipped with automated dynamic eco-driving systems. [ABSTRACT FROM AUTHOR]

Infrastructure-based sensors provide a potentially promising solution to support the wide adoption of connected and automated vehicles (CAVs) technologies at an early stage. For connected vehicles with lower level of automation that do not have perception sensors, infrastructure sensors will significantly boost its capability to understand the driving context. Even if a full suite of sensors is available on a vehicle with higher level of automation, infrastructure sensors can support overcome the issues of occlusion and limited sensor range. To this end, a cooperative perception modeling framework is proposed in this manuscript. In particular, the modeling focus is placed on a key technical challenge, time delay in the cooperative perception process, which is of vital importance to the synchronization, perception, and localization modules. A constant turn-rate velocity (CTRV) model is firstly developed to estimate the future motion states of a vehicle. A delay compensation and fusion module is presented next, to compensate for the time delay due to the computing time and communication latency. Last but not the least, as the behavior of moving objects (i.e., vehicles, cyclists, and pedestrians) is nonlinear in both position and speed aspects, an unscented Kalman filter (UKF) algorithm is developed to improve object tracking accuracy considering communication time delay between the ego vehicle and infrastructure-based LiDAR sensors. Simulation experiments are performed to test the feasibility and evaluate the performance of the proposed algorithm, which shows satisfactory results. [ABSTRACT FROM AUTHOR]

Platooning is one of the promising technologies that maximizes the power efficiency of electric vehicles by decreasing the distances between the vehicles. Along with the development of autonomous driving technology, platooning is expected to be commercialized. Recent studies on the operation of platooning focused on power-efficient maintenance of platooning. However, power-efficient operation strategy is also needed for practical applications. Therefore, this study deals with platooning operations that can maximize the power efficiency of electric vehicles in various operational situations. In order to derive the operation method, a mathematical model structured with an objective function that minimizes power consumption is developed. To derive the solution of the mathematical model, a hybrid genetic algorithm is applied. The numerical experiments on four different operational situations are performed to verify the validity of the model and the solution procedure. The four situations consider overall situation that can happen during the platooning stage. The stages are formation, disassembly, join and breakaway of vehicles of platoon. Those four situations are decided upon since they can represent the general situation that can happen during platooning. As a result, the power-efficient driving patterns of electric vehicles are identified. After the development of electric and systematic technology, operational technology for platooning will collaborate for the further improvement. Therefore, throughout consideration of the formation of platooning, technology will expand the sustainability of technological development. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC. [ABSTRACT FROM AUTHOR]

Taking fast action, and effectively utilizing the available resources, are important when conducting time-critical surveillance missions. In addition, the potential complexity of the search, such as the ruggedness of the terrain or large size of the search region, should be considered. Such issues can be tackled by using a heterogeneous fleet of manned and unmanned vehicles equipped with different sensors by considering speed, coverage and flexibility. This article centres on designing a fast solution approach for planning coverage missions through zoning and routing. The challenge is to cover an area where targets can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of search in sub-areas, where the scores represent the importance of tasks. Heuristic approaches are proposed to automate search processes and plan missions, given a small time budget. [ABSTRACT FROM AUTHOR]

Objective: This study examined the impact of monitoring instructions when using an automated driving system (ADS) and road obstructions on post take-over performance in near-miss scenarios. Background: Past research indicates partial ADS reduces the driver's situation awareness and degrades post take-over performance. Connected vehicle technology may alert drivers to impending hazards in time to safely avoid near-miss events. Method: Forty-eight licensed drivers using ADS were randomly assigned to either the active driving or passive driving condition. Participants navigated eight scenarios with or without a visual obstruction in a distributed driving simulator. The experimenter drove the other simulated vehicle to manually cause near-miss events. Participants' mean longitudinal velocity, standard deviation of longitudinal velocity, and mean longitudinal acceleration were measured. Results: Participants in passive ADS group showed greater, and more variable, deceleration rates than those in the active ADS group. Despite a reliable audiovisual warning, participants failed to slow down in the red-light running scenario when the conflict vehicle was occluded. Participant's trust in the automated driving system did not vary between the beginning and end of the experiment. Conclusion: Drivers interacting with ADS in a passive manner may continue to show increased and more variable deceleration rates in near-miss scenarios even with reliable connected vehicle technology. Future research may focus on interactive effects of automated and connected driving technologies on drivers' ability to anticipate and safely navigate near-miss scenarios. Application: Designers of automated and connected vehicle technologies may consider different timing and types of cues to inform the drivers of imminent hazard in high-risk scenarios for near-miss events. [ABSTRACT FROM AUTHOR]

This study aimed to validate the influence of latent variables on human trust in autonomous vehicles and elucidate the underlying psychological framework. A survey with 114 queries, drawn from previous studies on trust factors, was conducted online, gamering 195 valid responses. Employing correlation and factor analysis, 51 queries were identified as significantly impacting trust levels. A structural equation model incorporating these 51 variables was constructed, revealing five higher-level psychological constructs: "Interpersonal," "System Feature," "Risk Perception," "Behavioral Intention," and "Trust," each comprising sub-factors derived from associated queries. Notably, the "Interpersonal" construct exhibited the greatest influence on "Behavioral Intention" and "Trust," while "Risk Perception" showed a negative correlation with trust, indicating higher perceived risk diminishes trust. The findings highlight the importance Ofunderstanding user interpersonal characteristics in enhancing trust in autonomous vehicles. Unlike previous studies focusing on individual variables, this research unveiled a comprehensive psychological structure by categorizing variables, factors, and constructs hierarchically. The developed model facilitates comprehension, prediction, and enhancement of user trust, guiding the development of tailored trust-fostering strategies and interventions for specific user groups and contexts. [ABSTRACT FROM AUTHOR]

Overtaking is a complicated maneuver that has implications for the operation and safety of road users on two-lane undivided roads. Understanding the factors influencing the overtaking behavior and process of human drivers becomes essential in the wake of developments in driver awareness, vehicular capabilities, and roadway design. This review classifies the factors which govern the overtaking process of drivers into five categories: vehicle, road, traffic, environment, and driver. Post classification, the effect of each factor based on empirical evidence is discussed on the overtaking behavior of drivers. Based on the analysis, a potential framework is suggested to understand the overtaking process better. Furthermore, future research directions are discussed in the context of modeling approaches, generalizability, connected and automated vehicles, and data collection efforts. [ABSTRACT FROM AUTHOR]

Object detection in road scenarios is crucial for intelligent transport systems and autonomous driving, but complex traffic conditions pose significant challenges. This paper introduces Z-You Only Look Once version 8 small (Z-YOLOv8s), designed to improve both accuracy and real-time efficiency under real-world uncertainties. By incorporating Revisiting Perspective Vision Transformer (RepViT) and C2f into the YOLOv8s framework, and integrating the Large Selective Kernel Network (LSKNet), the model enhances spatial feature extraction. Additionally, the YOLOv8s backbone is optimized with Space-to-Depth Convolution (SPD-Conv) for better small object detection. The Softpool-Spatial Pyramid Pooling Fast (SoftPool-SPPF) module ensures precise characteristic information preservation. Z-YOLOv8s improves mean average precision (mAP)@0.5 on the Berkeley Deep Drive 100 K (BDD100K) and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets by 7.3 % and 3.8 %, respectively. It also achieves accuracy increases of 5.7 % and 6.5 % in Average Precision (AP)-Small, and a real-time detection speed of 78.41 frames per second (FPS) on the BDD100K. Z-YOLOv8s balances detection precision and processing speed more effectively than other detectors, as demonstrated by experimental results and comparisons. • Designing a RepViTC2f module, to address occluded object challenges in traffic scenarios. • SoftPool-SPPF enhances small object detection by refining edge details. • Z-YOLOv8s balances speed and precision on BDD100K and KITTI datasets. [ABSTRACT FROM AUTHOR]

Objectives: Currently, pedestrians' road-crossing decisions depend on the traffic at the crossing point, crossing opportunities, and circumstantial elements. Longer wait times on the curb and time pressure raise the number of traffic violations among pedestrians. The era of fully autonomous vehicles (FAVs) promises new interactions. External Human-Machine Interfaces (eHMIs) aim to increase the understanding of FAVs' intentions and will influence pedestrians' intent to cross. Yet, how pedestrian behaviors will change in the FAV era remains unclear. Further, there are no good metrics for evaluating the intricate interaction of crossing-related factors. Methods: In a laboratory study, sixty participants observed crossing scenarios of a typical one-lane road with 10–12 FAVs driving along, two possible eHMI messages ("Cross!"/"Stop!"), and varying crossing opportunities (safe, risky, and unsafe). Participants had to indicate when they intended to cross the road using a designated button. Half of the participants were induced to feel time pressure. We developed index scores to convey the complexity of such crossing situations from the perspective of a pedestrian and used them to analyze the experimental data. Results: In total, 48% of participants' indications were to cross in safe crossing opportunities, 34% risky, and 18% unsafe; 69% were made when the eHMI proposition was "Cross!" and 31% for "Stop!". High, significant, and negative correlations were found for compatible responses, e.g., with a safe crossing gap and an eHMI sign to "Cross!". The opposite was true for incompatibility, e.g., a safe crossing gap and an eHMI sign to "Stop!" or an unsafe crossing gap and an eHMI sign to "Cross!". The time pressure group's median times to cross were shorter, making riskier decisions. Conclusion: Our findings reflect a tendency to comply with the eHMI. In more complex scenarios where the crossing opportunity was uncertain or waiting times were longer, we observed that pedestrians were more inclined to follow the eHMI's guidance to cross. These findings suggest that pedestrians may be more likely to follow the eHMI's suggestion to cross than their self-skill, especially under time pressure. Our indices and metrics reflect the intricate interaction aspects of pedestrian behavior with eHMIs. [ABSTRACT FROM AUTHOR]