Autonomous Environment Research Articles

Microfiber Actuators With Hot-Pressing-Programmable Mechano-Photothermal Responses for Electromagnetic Perception.

Electromagnetic radiation (EMR) is a ubiquitous harm and hard to detect dynamically in multiple scenarios. A mechano-photothermal cooperative microfiber film (MFF) actuator is developed that can synchronously detect EMR with high reliability. The programmable actuation is deployed by a hot-pressing methodology, achieving the MFF with moderate modulus (378MPa) and superior toughness (87.26MJm-3) that ensure superior response (0.068cm-1s-1) and bending curvature (0.63cm-1). A secondary hot-pressing can further program the actuation behavior with black phosphorus local photothermal enhancement patterns to achieve 2D-3D transformable geometries. An amphibious robot with a land-water adaptive locomotion mechanism is designed by programming the MFFs. It can crawl on land and locomote on water with a velocity up to ≈1.8mms-1, and ≈2.39cms-1, respectively. Employing the conductive fabric layer of the actuator with electromagnetic induction effect, the amphibious robot can synchronously perceive environmental EMR with sensitivity up to 99.73%±0.15% during locomotion, with superior adaptability to EMR source intensity (0.1 to 3000W) and distance (≈9m) compared to a commercial EMR detector. This EMR detective microfiber actuator can inspire a new direction of environment-interactive smart materials, and soft robots with multi-scenario adaptivity and autonomous environment perceptivity.

Analyzing Riders’ Behavioral Adaptation to Driving Patterns of Advanced Autonomous Vehicles: A Virtual Reality Simulation Study

The necessity of human supervision and intervention during autonomous driving has long been a topic of controversial discussion. From a developer’s perspective, it is expected that users will readily adapt to well-calibrated autonomous driving systems (ADS) due to their superior performance in dynamic driving tasks (DDT) compared to conventional human-driven vehicles. However, when passengers experience an autonomous vehicle (AV), there may be an adjustment period during which they modify their behavior to accommodate the driving patterns of the ADS. Additionally, some passengers might not adapt to autonomous driving at all, highlighting potential limitations in the current ADS development strategy. This work studies the dynamics of human-automation interaction and introduces an “objective method”, which employs a Virtual Reality (VR)-enabled simulation approach for in-depth behavioral analysis concerning riders’ behavioral adaptation to autonomous driving. Specifically, we examined how participants interacted with and intervened in Level 4 ADS operating under conservative, moderate, and aggressive driving patterns in a fully autonomous environment. A realistic urban road network was recreated in VR, integrated with traffic microsimulation to generate various driving scenarios. Twenty-seven participants completed driving tasks across different AV modes, with their intervention behaviors analyzed in relation to traffic conditions and AV aggressiveness. Key findings include: (1) Participants showed higher intention to intervene but lower actual intervention rates under aggressive AV modes compared to moderate and conservative modes, suggesting quicker adaptation to more challenging driving scenarios. (2) Interventions generally proved unnecessary and sometimes detrimental to overall traffic performance in a full-AV environment. (3) Aggressive AV modes significantly improved traffic efficiency, with a 40% increase in average travel speed and a 53% reduction in waiting time. However, human interventions posed the greatest challenge to achieving optimal traffic conditions. This research provides insights into the complex dynamics of human-AV interaction and adaptation, offering valuable implications for AV interface design, implementation strategies, and public acceptance of autonomous driving technologies.

Pedestrian shuttle service optimization for autonomous intersection management

Autonomous intersection management (AIM) has been widely investigated over the past two decades as a solution for signal-free intersections in a fully connected and autonomous environment. However, existing AIM approaches lack consideration for crossing pedestrians, especially in a way that enables pedestrians to cross streets safely while optimizing upcoming vehicles at the intersection to maintain traffic continuity. To address this issue, this paper proposes an Automated Pedestrian Shuttle (APS) service system to transport pedestrians across the street. Using a coupled space–time network modeling method, an optimization model, which incorporates pedestrian ride strategies and aims to minimize the total crossing time, is first established for the APS operation scheme. A Shuttle-by-Shuttle-Planning (SBSP) algorithm is developed for model solving to determine the APS operation route and the departure time from each station along the route. Then, a timing schedule optimization model is proposed to adjust the departure time of APS at each station and the time of main-lane vehicles entering an intersection to avoid vehicle conflicts. To ensure that the main-lane vehicles can enter the intersection on time, a trajectory optimization model is proposed to provide a specific driving strategy for each main-lane vehicle. A rolling horizon strategy is adopted to bridge the different optimization time windows. Finally, the effectiveness of the proposed APS service system is demonstrated through numerical simulation experiments. The results show that (1) the APS service can effectively help pedestrians cross the street with minor delays to main-lane vehicles; (2) The dynamic-route scheme is adaptable to the needs of randomly arriving pedestrians due to its demand-responsive nature; (3) The service efficiency of the system is related to the number of APSs, and the more APSs, the higher the service efficiency under the premise of unsaturation; (4) The designed SBSP algorithm significantly reduces the model-solving time compared to the commercial solver, making the APS service system potentially applicable for field implementation.

Designing Novel LiDAR-Detectable Plate-Type Materials: Synthesis, Chemistry, and Practical Application for Autonomous Working Environment.

Plate-type hollow black TiO2 (HL/BT) with a high NIR reflectance was fabricated for the first time as a LiDAR-detectable black material. A TiO2 layer was formed on commercial-grade glass by using the sol-gel method to obtain a plate-type structure. The glass template was then etched with hydrofluoric acid to form a hollow structure, and blackness was further achieved through NaBH4 reduction, which altered the oxidation state of TiO2 to black TixO2x-1 or Ti4+ to Ti3+ and Ti2+. The blackness of the HL/BT material was maintained by a novel approach that involved etching prior to reduction. The thickness of the TiO2 layer was controlled to maximize the NIR reflectance when applied as paint. The HL/BT material with a thickness of 140 nm (HL/BT140) showed a blackness (L*) of 13.3 and high NIR reflectance of 23.6% at a wavelength of 905 nm. This is attributed to the effective light reflection at the interface created by the TiO2 layer and the hollow structure. Plate-type HL/BT140 provides excellent spreadability, durability, and thermal stability in practical paint applications compared with sphere-type materials due to the higher contacting area to the applied surface, making it suitable for use as a LiDAR-detectable inorganic black pigment in autonomous environments.

Teachers' Strategies to Enhance Deeper Learning Skills in English Language Classes

The paper focuses on English teachers' teaching strategies in secondary-level classes to enhance students' deeper learning skills. This paper is the outcome of a qualitative study, a narrative inquiry with three secondary-level English teachers. With the help of in-depth interviews and class observation, the study figured out how the different teaching strategies allow teachers to adopt, integrate and ensure deeper learning skills in existing curricular content. The research primarily collected data through interviews and supplemented it with classroom observations. They subsequently analysed the data under three major themes. The first gives theoretical reasoning; in contrast, the second and third themes widely discuss the practical aspects and examples from actual classroom practices aligning with the researcher's observation track record that revolves around the theme. The study concludes that any strategies the teacher introduces must be reinforced by fostering an autonomous environment for learners, creating a school environment supportive of deeper learning, and ensuring the appropriate assessment of deep learning skills.

Diagnosis of the Energy Regulatory Scenario with Emphasis on Smart Energy

The energy management system has evolved into a digitized and autonomous environment, where consumers can manage their own generation, consumption and storage through virtual environments. Smart Energy (SE) understands this decentralized energy management by streamlining and helping in this matter, however, there is a need to regulate this scenario. Considering that the electric energy sector has a solid regulation, efforts need to be concentrated to adapt it to a model that emphasizes the SE and everything that it proposes. Therefore, the objective of this article is to propose a diagnosis of the current energy regulatory scenario directed to the SE. Through a focus group, experts from the energy sector contributed with opinions on the subject for the construction of a Current Reality Tree (CRT), which aimed to identify the root causes that affect and limit the energy regulation scenario SE-oriented. The current situation of this scenario was analyzed and what can be changed. 38 actions that contribute to the development and propagation of SE were suggested. These actions are guiding ways to regulate and enable the regulatory environment to support the insertion of technologies related to the theme.

A Helly Model-Based MPC Control System for Jam-Absorption Driving Strategy against Traffic Waves in Mixed Traffic

Traffic waves in traffic flow significantly impact road throughput and fuel consumption and may even lead to severe safety issues. Currently, in connected and autonomous environments, the jam-absorption driving (JAD) strategy shows good performance in dissipating traffic waves. However, the previous JAD strategy has mostly focused on wave dissipation without adequately assessing traffic efficiency and safety. To address this gap, an optimal control problem for JAD in mixed traffic is proposed to reduce traffic waves. The prediction model is developed using the car-following model within a model predictive control (MPC) framework. The Helly model is selected for the manual vehicle. This is because the Helly model is a linear model that describes the car-following phenomenon accurately without delay effect. In addition, the objective function of the prediction model considers both traffic safety and efficiency while satisfying mechanical and safety constraints. Simulation results indicate that the proposed methodology can effectively reduce traffic jams and improve traffic performance on a one-lane freeway. The optimal method is more applicable to complex traffic wave scenarios, providing a new perspective for reducing traffic jams on the freeway.

Autonomy Acquisition and Performance within Higher Education in Vietnam—A Road to a Sustainable Future?

The Vietnamese Government prioritizes education as a developmental investment within its socioeconomic development programs. Subsequently, Vietnam’s higher education system (HE) is experiencing substantial transformation, emphasizing autonomy, because institutions endowed with self-governance capabilities may allocate societal resources more efficiently for developmental purposes. In this paper, we measured Vietnamese universities’ total factor productivity change (TFPCH) in the autonomy context, using it as a proxy for the sustainable performance of HE institutions. We decomposed TFPCH into Technical Efficiency Change (EFFCH) and Technology Change (TECHCH) and regressed these indices with independent variables to derive their determining factors. Notably, we employed the derived intercept as a proxy for the autonomy context of Vietnam. The DEA found significant advancements in productivity and technology, indicating a positive paradigm shift within Vietnam’s higher education system. The intercepts obtained from these regressions are positive and significant, implying that the autonomous environment supports the sustainable advancement of Vietnam’s higher education system in both components of TFPCH: the catch-up ability (EFFCH) and technological improvement (TECHCH). In addition, we found that investment in vital resources (number of laboratories, research funding, or quality accreditation) improves productivity (TFPCH) via technological improvement. We also observed that private universities experienced higher performance progress than public ones. However, we did not find any significant relationships between the university scale or the location of the main campus and their performance. To further the growth of Vietnam’s higher education system, we propose that the autonomy of institutions continues to be granted.

Commonsense reasoning and automatic generation of IoT contextual knowledge: An Answer Set Programming approach

The increasing interest in searching for intelligent, proactive, and autonomous environments leads to the necessity of accessing contextual knowledge: knowledge about changes that occur in the environment and even about the capabilities of the devices that compose a specific deployment. This knowledge would allow carrying out commonsense reasoning (exhibit human-like comprehension) according to the current situation, enabling decision making and task planning. One of the most used forms of knowledge specification is ontologies, but ontological knowledge modeling is usually a manual process, making it a costly task. In addition, ontologies can be found that have been designed for specifying knowledge about IoT-related concepts, but many are overly complex or do not have an adequate orientation that allows modeling the system’s capabilities. There are many different types of reasoners, but Answer Set Solvers are of particular interest for commonsense reasoning. This paper proposes an ontology to represent contextual knowledge about IoT device capabilities, and an Answer Set Programming-based reasoner for the automatic generation of this knowledge. The main challenge is to demonstrate that contextual knowledge can be generated through commonsense reasoning processes implemented with Answer Set Programming, and that the resulting knowledge can be used for decision making (also through commonsense reasoning). This work shows how the generated knowledge is correct through different use cases, presents an application example that demonstrates the benefits of using the generated knowledge, and analyzes the reasoner performance to demonstrate that the execution time is adequate.

Deep learning-based modulation recognition with constellation diagram: A case study

Automatic modulation recognition is a promising solution for identifying and classifying signals received in heterogeneous wireless networks. In dynamic and autonomous environments, receivers must extract the relevant signal from various modulated signals to enable further communication procedures. Machine learning, including its sub-branches for classification problems, offers promising operational capabilities. This study utilized the ResNet-50 deep learning method for modulation classification. A dataset consisting of eight digital modulation techniques was generated, with constellation diagrams created as image data over the additive white Gaussian noise (AWGN) channel at signal-to-noise ratios (SNR) of 5 dB, 10 dB, and 20 dB. The deep learning algorithm's performance metrics were evaluated using a confusion matrix, and F1 scores were compared to those of the AlexNet deep learning algorithm. The simulation results clearly indicate the superior performance of ResNet-50 over AlexNet. In terms of average F1 scores, ResNet-50 exhibits a significant advantage, surpassing AlexNet by approximately 67%, 29%, and 10% at SNR values of 5 dB, 10 dB, and 20 dB, respectively.

Modelling Yielding of Slow-Moving Vehicles: Application of Drone Data

Overtaking on two-lane undivided highways poses complex challenges, with attention traditionally focused on the overtaking vehicle. Yet, slow-moving vehicle (impeder) behaviour has been largely overlooked. This study investigates the yielding process whereby impeders actively support overtaking vehicles. This phenomenon can have implications for safety in autonomous environments and optimising Traffic control strategies. Empirical data, including drone-captured trajectories, were analyzed to develop predictive models for yielding behaviour. Prediction accuracies up to 82% and 85% for speed and space yielding were achieved, respectively. Integrating these models into microsimulation tools can supplement policy decisions, optimize Traffic flow, and mitigate accidents, yielding significant societal benefits.

„Realizm i rozwaga”. Środowisko Ośrodka Dokumentacji i Studiów Społecznych wobec narodzin „Solidarności” (sierpień – listopad 1980)

The article discusses the relationship of the movement associated with the Center for Documentation and Social Studies to the birth of ”Solidarity“ (August–November 1980). Established in 1967, ODiSS first remained in a close formal-legal relationship with the ”Więź“ team and the Warsaw NCI (as part of the ”Znak“ movement), then in 1974 became independent, creating a separate entity, alternative and competitive to other Catholic circles and groups. ODiSS saw Solidarity as both a labor syndicate and a mass movement for social revictimization and social empowerment. Activists and participants in the movement were actively involved in assisting regional and company structures of the union, providing advice and legal assistance, and applying for workers’ rights in the forum of the People’s Republic of Poland’s parliament. At the same time, they warned against political involvement of ”Solidarity“, demands for system change, etc. ODiSS, representing religious people, remained in close contact with Primate S. Wyszynski and the Episcopate, although it was an autonomous environment, acting on its own responsibility. It also held formal and informal talks with representatives of the communist regime. It considered it crucial for both sides of the August agreements to keep their commitments and guarantees, which was to serve a genuine national understanding and the real democratization of socialism. In its activities, it adopted the ”constructive center“ formula. Being aware of operating within the framework of the legal and political rules in force (a concessionary environment), it balanced between what was optimal and what was possible in certain political realities, hence in its assessment of “Solidarity” it maintained prudent moderation, prudence and realism.

Contemporary pedestrian shapes. From the displaced plane to the complex space

INTRODUCTION: The pedestrian space has evolved along with the needs of the city in which it is implemented, considering the different social, political and cultural phenomena that have influenced urban life. OBJECTIVE: This article develops a classification and characterization of the different forms of the contemporary pedestrian space, in order to infer criteria that allow us to propose a more complex pedestrian structure, the pedestrian network. METHOD: Using a descriptive and qualitative approach, an analysis of various case studies is carried out, to determine common or distinctive characteristics that allow us to understand their structure. RESULTS: The analysis of these cases has allowed to classify them into: multilevel pedestrian spaces –those that have moved the circulation plane to levels other than level 0–, pedestrian areas –that function as autonomous environments for the pedestrian– and pedestrian axes – which are the basic forms of organization of the pedestrian space. These forms contain the fundamental characteristics of the contemporary pedestrian space and the essence of their introduction in the construction of the city. DISCUSSION AND CONCLUSIONS: The evolution of contemporary pedestrian space leads to recognition of the need for a more complex urban structure, extended throughout the urban area, that contemplates and integrates the characteristics of pedestrian spaces: the urban pedestrian network, conceived not in function of isolated elements but of interconnected and continuous elements, which serve the entire city.

Using open educational resources in studio-based flipped classrooms: action research in video production learning

This study explores the use of open educational resources (OERs) in studio-based learning and their influence on learning experiences. The research team conducted action research with 30 bachelor of arts students who were completing a video production subject. Students were required to learn from a website containing open online learning resources under a flipped classroom approach. A teaching schedule and website were designed according to several criteria. Research data were collected through observation, reflective journals, and interviews and were analyzed via thematic analysis. Participating students expressed their perceptions of benefits and hesitation in utilizing OERs in learning. They agreed that the use of OERs as flipped classroom learning materials could positively affect their learning, primarily through competence and learning autonomy as indicated in self-determination theory. This investigation provides teachers with valuable experience and suggestions for teaching and learning approaches that incorporate OERs into studio-based education. Students learn from OERs in which they can gain the most up-to-date technical knowledge in an autonomous environment. This experience indicates that this pedagogy greatly and positively influences students’ subject-learning experiences, learning outcomes, and self-learning skills.

Advancing Sustainable Energy Transition: Blockchain and Peer-to-Peer Energy Trading in India’s Green Revolution

Advancing the sustainable energy transition is a major need in nations that are constantly evolving and developing in terms of their energy economy. India has been chosen for the purpose of analysis due to the heterogenous nature of its polity, topographies, infrastructural capabilities and diverse framework. In accordance with the sustainable development goals proposed by the UN, a metamorphosis is observed within the renewable energy sector of the nation. Blockchain technology that facilitates a transparent transition is incorporated on various upcoming platforms. This is backed up by peer-to-peer trading of energy providing a prosumer with an autonomous environment. The goal of this paper is to highlight the struggles and challenges faced by the energy sector as it takes up unconventional and non-traditional approaches within the country. It also aims to discover potential ways that would help a nation like India facilitate such a transition by studying its ongoing trends. The need is eminent for a practical study that is specific to a developing nation like India in terms of P2P energy trading enabled by blockchain technology to promote the use of open-sourced electricity and achieve a decentralized system.

A Network Traffic Model for the Control of Autonomous Vehicles Acting as Moving Bottlenecks

In this work we present a traffic model to simulate network-level traffic evolution under the impact of controlled autonomous vehicles acting as moving bottlenecks. We first extend the Newell-Daganzo method to track the trajectories of moving bottlenecks and calculate the cumulative number of vehicles passing each moving bottleneck. By integrating the solutions to the cumulative number of vehicles passing moving bottlenecks and link nodes as boundary conditions in the link-transmission model, we can incorporate the impact of moving bottlenecks into the flow of traffic at a network scale. We present numerical simulation results that illustrate the effectiveness of the developed model to track the trajectories of the moving bottlenecks and simulate their impact on freeway traffic. Lastly, we present control applications of the developed model to trajectory optimization. The reduced fuel consumption associated with the careful control of AV trajectories in the moving bottleneck framework indicates the potential to considerably improve the flow of traffic by controlling the AVs in a mixed human and autonomous environment.

Energy-Efficient Spiking Segmenter for Frame and Event-Based Images.

Semantic segmentation predicts dense pixel-wise semantic labels, which is crucial for autonomous environment perception systems. For applications on mobile devices, current research focuses on energy-efficient segmenters for both frame and event-based cameras. However, there is currently no artificial neural network (ANN) that can perform efficient segmentation on both types of images. This paper introduces spiking neural network (SNN, a bionic model that is energy-efficient when implemented on neuromorphic hardware) and develops a Spiking Context Guided Network (Spiking CGNet) with substantially lower energy consumption and comparable performance for both frame and event-based images. First, this paper proposes a spiking context guided block that can extract local features and context information with spike computations. On this basis, the directly-trained SCGNet-S and SCGNet-L are established for both frame and event-based images. Our method is verified on the frame-based dataset Cityscapes and the event-based dataset DDD17. On the Cityscapes dataset, SCGNet-S achieves comparable results to ANN CGNet with 4.85 × energy efficiency. On the DDD17 dataset, Spiking CGNet outperforms other spiking segmenters by a large margin.

Financial sustainability in a marketised and partially autonomous environment: the case of small new public universities in England

ABSTRACT In the context of threats to their financial sustainability, this paper uses Resource Dependency Theory to explore the challenges being faced by a sample of 10 small new public universities in England. It discusses the responses being taken and prospects for the future in this segment of the sector. It concludes that some of the most important elements of income and expenditure are also areas where institutions have amongst the lowest levels of autonomy, including tuition fees, staff salaries and pension costs. It suggests that institutions are proactively seeking ways to both adapt their strategy and influence the environment in which they operate, including the introduction of new organisational forms, models for employing staff, the diversification into new programmes and markets, and greater use of domestic and international partnerships.

Hopf Bifurcation Analysis of Mixed Traffic and Its Implications for Connected and Autonomous Vehicles

Capturing the evolution mechanism of traffic dynamics is of great significance when alleviating traffic congestion and improving traffic efficiency. While several studies have presented stability analyses of various traffic systems, most have relied on linear analysis methods, which cannot capture the complex nonlinear dynamic behavior of heterogeneous traffic flow. To address this shortcoming, this paper derives a generic Hopf bifurcation structure that can be applied to multiclass traffic models. The proposed bifurcation structure is investigated to understand the characteristics of heterogeneous traffic flow in a connected and autonomous environment as case studies. The results, based on selected on-field calibrated traffic models, show that (1) the linear analysis results deviate significantly from the actual instability of the mixed traffic system, which illustrates the necessity of a bifurcation analysis; and (2) connected human-driven vehicles and connected autonomous vehicles have the ability to alleviate the formation and propagation of traffic oscillations. Finally, the theoretical analysis results are verified through simulation experiments.

A smart oyster mushroom cultivation using automatic fuzzy logic controller

Oyster mushroom cultivation is an eco-friendly, profitable agribusiness because of its nutritious and medicinal value but it is labor-intensive, and it grows in a sensitive environment. It must use an autonomous environment control system to optimize and offer the necessary growing conditions. To optimize the temperature and humidity and consequently maximize mushroom production, a smart mushroom watering, monitoring, and controlling system using fuzzy logic controllers (FLC) have been developed. This study suggests installing an Internet of Things (IoT) node that uses DHT22 sensors, an ESP32 board, and relays to control actuators (sprinkler, cooling fan, humidifier, and heater) in a smart oyster mushroom house. The controller on sensing current temperature and humidity values using the fuzzy logic technique switches on/off the actuators automatically. The data collected at each IoT node is collected and visualized on the ThingSpeak platform. The computed response time for actuators using FLC is compared with that calculated using a simple ON-OFF method. The findings demonstrate that response time calculation using fuzzy logic is significantly accurate and faster than a simple ON-OFF approach. Additionally, the graphs created in live on ThingSpeak, demonstrate that the FLC algorithm is more robust and effective than the simple ON-OFF algorithm.