Autonomous Development Research Articles

Autonomous Electric Race Car Inverter Development: Revving up the future with resource efficient drive technology

As the electric drive finds its way progressively into important industry sectors like mobility, transportation, agriculture, production, and supporting services, it becomes increasingly important to have individually optimized technical solutions for the manifold applications. Therefore, a high number of engineers with interdisciplinary competencies will be needed soon to comply with the demand of the worldwide markets. A key component for an often-used variant of the electric drivetrain is the full-bridge inverter, which is subject to a wide spectrum of different requirements. In 2021, the University of Denver (DU), started a cooperation with the University of Applied Sciences Augsburg (UASA) to develop a full-bridge inverter for an autonomous, electrical Formula Student race car, using four permanent magnet synchronous machines as an all-wheel drive. To improve the performance of the race car, the inverter must be lightweight, package optimized, electromagnetic compatibility compliant, safe, and reliable when it distributes a maximum of 80 kW instantaneous power from the battery at a voltage between 420 V and 600 V individually to the four wheels. This article documents the inverter development process using silicon carbide MOSFET power modules, with the goal of using future results in the race car and the knowledge transfer for the education of engineering students. This transatlantic partnership between DU and UASA also serves as a success story for intercontinental collaborative development based on modern communication and decentralized development techniques.

Study Habits Developed by Mexican Higher Education Students during the Complexity of the COVID-19 Pandemic

During the first 700 days of the COVID-19 pandemic in Mexico, higher education institutions adopted different types of technology-supported learning to provide continuity of teaching activities. The pandemic forced students to change their study habits to face the challenges of learning in the distance modality while using technologies for learning and knowledge. In this research, a questionnaire called “Survey of Study Habits for University Students after more than 700 days of the Pandemic in Mexico” was applied to inquire about habits that were strengthened or emerged in undergraduate and graduate university students who participated in non-face-to-face learning environments during the pandemic. The study involved 3000 students from public (n = 1500) and private (n = 1500) universities located in six areas of Mexico (comprising 32 states). The findings indicated that most of the students acquired at least one digital device and expanded their internet service, and perceived an improvement in their self-study skills and greater autonomous learning development.

AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods

Maritime transport faces new safety challenges in an increasingly complex traffic environment caused by large-scale and high-speed ships, particularly with the introduction of intelligent and autonomous ships. It is evident that Automatic Identification System (AIS) data-driven ship trajectory prediction can effectively aid in identifying abnormal ship behaviours and reducing maritime risks such as collision, stranding, and contact. Furthermore, trajectory prediction is widely recognised as one of the critical technologies for realising safe autonomous navigation. The prediction methods and their performance are the key factors for future safe and automatic shipping. Currently, ship trajectory prediction lacks the real performance measurement and analysis of different algorithms, including classical machine learning and emerging deep learning methods. This paper aims to systematically analyse the performance of ship trajectory prediction methods and pioneer experimental tests to reveal their advantages and disadvantages as well as fitness in different scenarios involving complicated systems. To do so, five machine learning methods (i.e., Kalman Filter (KF), Support Vector Progression (SVR), Back Propagation network (BP), Gaussian Process Regression (GPR), and Random Forest (RF)) and seven deep learning methods (i.e., Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (Bi-LSTM), Sequence to Sequence (Seq2seq), Bi-directional Gate Recurrent Unit (Bi-GRU), and Transformer) are first extracted from the state-of-the-art literature review and then employed to implement the trajectory prediction and compare their prediction performance in the real world. Three AIS datasets are collected from the waters of representative traffic features, including a normal channel (i.e., the Chengshan Jiao Promontory), complex traffic (i.e., the Zhoushan Archipelago), and a port area (i.e., Caofeidian port). They are selected to test and analyse the performance of all twelve methods based on six evaluation indexes and explore the characteristics and effectiveness of the twelve trajectory prediction methods in detail. The experimental results provide a novel perspective, comparison, and benchmark for ship trajectory prediction research, which not only demonstrates the fitness of each method in different maritime traffic scenarios, but also makes significant contributions to maritime safety and autonomous shipping development.

Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration.

The current approach to connected and autonomous driving function development and evaluation uses model-in-the-loop simulation, hardware-in-the-loop simulation and limited proving ground use, followed by public road deployment of the beta version of software and technology. The rest of the road users are involuntarily forced into taking part in the development and evaluation of these connected and autonomous driving functions in this approach. This is an unsafe, costly and inefficient method. Motivated by these shortcomings, this paper introduces the Vehicle-in-Virtual-Environment (VVE) method of safe, efficient and low-cost connected and autonomous driving function development, evaluation and demonstration. The VVE method is compared to the existing state-of-the-art. Its basic implementation for a path-following task is used to explain the method where the actual autonomous vehicle operates in a large empty area with its sensor feeds being replaced by realistic sensor feeds corresponding to its location and pose in the virtual environment. It is possible to easily change the development virtual environment and inject rare and difficult events which can be tested very safely. Vehicle-to-Pedestrian (V2P) communication-based pedestrian safety is chosen as the application use case for the VVE in this paper, and corresponding experimental results are presented and discussed. A no-line-of-sight pedestrian and vehicle moving towards each other on intersecting paths with different speeds are used in the experiments. Their time-to-collision risk zone values are compared for determining severity levels. The severity levels are used to slow down or brake the vehicle. The results show that V2P communication of pedestrian location and heading can be used successfully to avoid possible collisions. It is noted that actual pedestrians and other vulnerable road users can be used very safely in this approach.

Safety Challenges and Analysis of Autonomous Electric Vehicle Development: Insights from On-Road Testing and Accident Reports

Autonomous electric vehicles (AEVs) hold great promise for the future of automotive engineering, but safety remains a significant challenge in their development and commercialization. Therefore, conducting a comprehensive analysis of AEV development and reported accidents is crucial. This paper reviews the levels of automation in AEVs, their disengagement frequencies, and on-road accident reports. According to the report, numerous manufacturers thoroughly tested AEVs across a distance of more than 3.9 million miles between 2014 and 2022. Disengagement frequencies vary among manufacturers, and approximately 65% of accidents during this period occurred while AEVs were operating in autonomous mode. Notably, the majority of accidents (90%) were caused by other road users, with only a small fraction (approximately 8%) directly attributed to AEVs. Enhancing AEVs' ability to detect and mitigate safety risks from external sources has the potential to significantly improve their safety. This paper provides valuable insights into AEV safety by emphasizing the importance of comprehensively understanding AEV development and reported accidents. Through the analysis of disengagement and accident reports, the study highlights the prevalence of passive accidents caused by other road users. Future research should concentrate on enabling AEVs to effectively detect and respond to safety risks originating from external sources to enhance AEV safety. Overall, this analysis contributes to the ongoing efforts in AEV development and provides guidance for strategies aimed at improving their safety features.

Language Teachers’ Autonomy in EFL Classroom: A Narrative Inquiry

The study aims to find out practices undertaken by English language teachers for their autonomous development in EFL classrooms. Autonomy has become one of the most problematic and significant issues to be discussed in the field of teacher education in the recent era. This paper deals with the ways to support teachers in their classroom practices, as well as a way of improving professional practices and career development. It is based on qualitative research in general and narrative inquiry in particular. The information is based on four teachers from the community as well as institutional schools. It mainly discusses the perceptions and practices of teachers towards teacher autonomy. From the obtained information, the study found positive feelings of teachers towards employing initiatives taken regarding autonomy, and they note that other stakeholders must encourage the teachers’ efforts for successful implementation. It reveals that most schools have strong and self-empowered human resources but proper encouragement and application parts seem to be weak.

Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities.

Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities.

Prehistory and Ideology in Cold War Southeast Asia: The Politics of Wartime Archaeology in Thailand and the Democratic Republic of Vietnam, 1954–1975

The two decades comprised within the partition of Vietnam and the end of the Indochina Wars surprisingly saw major advances in prehistoric archaeology in the region. This article examines the political context and implications of archaeological investigations conducted in Thailand and the Democratic Republic of Vietnam under the guidance of, respectively, American and Soviet specialists, as an aspect of the cultural Cold War. Archaeological discoveries in both countries debunked colonial archaeology’s account of prehistoric Southeast Asia as a passive recipient of Chinese cultural influence by documenting autonomous technological development. The article argues that the new image of mainland Southeast Asia’ prehistory that formed by the early 1970s reflected the superpowers’ objective of empowering the region’s postcolonial nation-states notwithstanding their political contrasts, yet it was not equally congruent with the nationalist narratives of Thailand and North Vietnam.

Methods for analyzing infant heart rate variability: A preliminary study.

Heart rate (HR) and heart rate variability (HRV) reflect autonomic development in infants. To better understand the autonomic response in infants, reliable HRV recordings are vital, yet no protocol exists. The purpose of this paper is to present reliability of a common procedure for analysis from two different file types. In the procedure, continuous electrocardiograph recordings of 5-10 min are obtained at rest in infants at 1 month of age by using a Hexoskin Shirt-Junior's (Carre Technologies Inc., Montreal, QC, Canada). Electrocardiograph (ECG; .wav) and R-R interval (RRi; .csv) files are extracted. The RRi of the ECG signal is generated by VivoSense (Great Lakes NeuroTechnologies, Independence, OH). Two MATLAB (The MathWorks, Inc., Natick, MA) scripts converted files for analysis with Kubios HRV Premium (Kubios Oy, Kuopio, Finland). A comparison was made between RRi and ECG files for HR and HRV parameters, and then tested with t tests and correlations via SPSS. There are significant differences in root mean squared successive differences between recording types, with only HR and low-frequency measures significantly correlated together. Recording with Hexoskin and analysis with MATLAB and Kubios enable infant HRV analysis. Differences in outcomes exist between procedures, and standard methodology for infant HR analysis is needed.

Exploring the Path of Autonomous Development: the Development Dilemma and Coping Strategies of Sub-Saharan Africa in the Post-epidemic Era

Based on the multidisciplinary research perspectives of economics, politics, and international relations, this paper starts with the analysis of the main characteristics of sub-Saharan African countries, analyzes their economic development difficulties and new challenges in the post-epidemic era, discusses the necessity for sub-Saharan African countries to explore the path of autonomous development at the level of economic structural reform and development model, and proposes coping strategies. Sub-Saharan African countries should seize the opportunity to accelerate economic restructuring under the favorable situation of the formal launch of the African Continental Free Trade Area (AfCFTA); take measures to develop agriculture and food production, manufacturing, digital economy, and the AfCFTA construction; and actively cooperate with China and other emerging countries to enhance their capacity for independent development.

Key determinants of autonomous building development

Key determinants of autonomous building development

Study on Artillery Aiming Control and the Development of Intelligent Autonomous Technology

In recent 70 years, the artillery aiming control technology has developed rapidly, and the control mode has experienced the development process of manual, semi-automatic and full-automatic. The development of the control mode of the new generation artillery aiming control system in the future is still in the stage of research and exploration, with relatively few research results and lack of systematicness. This paper analyzed the development related technologies of aiming control mode, puts forward an intelligent autonomous development mode and basic concept of artillery weapon equipment aiming control, discusses the characteristics and differences between artillery intelligent autonomous and conventional computer automatic control from the aspects of signal perception layer, control strategy layer and drive execution, and analyzes the development trend of artillery weapon equipment aiming control technology.

Application development method of sci-tech journals based on cloud platforms: Taking Changbai Mountain Academic Exchange as an example

Background: To provide an autonomous and rapid application (App) development method for science and technology (sci-tech) journals. Methods: As an example, the selection basis and technical requirements of the App cloud platform are given, and the development process of the sci-tech journal App based on the cloud platform is introduced in detail. Result: Based on the cloud platform, mobile Apps can be developed quickly without writing programs. Conclusion: The App cloud platform can break through the technical bottleneck of App development of sci-tech journals and provide a new way for the integration of sci-tech journals and new media.

A precautionary approach to autonomous vehicles.

In this article, we defend an approach to autonomous vehicle ethics and policy based on the precautionary principle. We argue that a precautionary approach is warranted, given the significant scientific and moral uncertainties related to autonomous vehicles, especially higher-level ones. While higher-level autonomous vehicles may offer many important benefits to society, they also pose significant risks, which are not fully understood at this juncture. Risk management strategies traditionally used by government officials to make decisions about new technologies cannot be applied to higher-level autonomous vehicles because these strategies require accurate and reliable probability estimates concerning the outcomes of different policy options and extensive agreement about values, which are not currently available for autonomous vehicles. Although we describe our approach as precautionary, that does not mean that we are opposed to autonomous vehicle development and deployment, because autonomous vehicles offer benefits that should be pursued. The optimal approach to managing the risks of autonomous vehicles is to take reasonable precautions; that is, to adopt policies that attempt to deal with serious risks in a responsible way without depriving society of important benefits.

The blended future of automation and AI: Examining some long-term societal and ethical impact features

The potential impacts of machine learning and artificial intelligence (AI) on society are receiving increased attention owing to the rapid growth of these technologies during the fourth industrial revolution. Thus, a detailed analysis of the positive implications and drawbacks of AI technology in human society is necessary. The development of AI technology has created new markets and employment opportunities in vital industries, including transportation, health, education, and the environment. According to experts, the rapidly increasing improvements in AI will continue.As part of humankind's continual efforts to create more prosperous technological growth, automation and AI are changing people's lives and are widely considered to be game-changers in a variety of industries. This study presents a review of how automation and AI may affect businesses and jobs. To determine some of the prospective long-term consequences of AI on human civilisation, this study investigates a variety of connected primary impacting potentials, including job losses, employees' well-being, dehumanisation of jobs, fear of AI, and examples of autonomous technology developments, such as autonomous-vehicle challenges. A diverse methodology of narrative review and thematic pattern was used to add to transdisciplinary or multidisciplinary work, particularly in the theoretical development of AI technologies.

Shopping Mall Parking Lot as/is a Meeting Place: Typologies and Strategies for a New Polarity in Architectural and Landscape Design

This article presents the results of a study developed around the general theme of the future of large shopping centers in relation to the current transformations of territory and city in Italy. Specifically, the research deals with outdoor spaces, those enormous technical and service spaces strictly connected to the commercial "big-boxes". The focus of the research is therefore on the large parking areas, the roads, the manoeuvring and service areas for sales: the large asphalt slabs that surround the commercial volumes. The research starts from the specific case study of the large shopping center "Tiare" in Villesse, Gorizia (one of the largest in the Friuli Venezia Giulia Region) and then extends to the development of a more general and scientifically articulated modelling. The objective was to translate the initial retrospective study (origins and development of shopping centers) and the design explorations developed in the academic sphere and through workshops on the potential of the case study into strategies, exportable to other contexts. The spaces involved are considerable, amounting to 2-3 times the sales area, i.e., parking lots of 30-40-60 hectares, the same extension of an entire town. From this it is easy to understand the importance of the architectural design on other disciplines, and the need to question "if and how" these spaces can also become something else, if they can also have a social role due the dimensional-territorial entity, and specifically, recovering the American tradition from Gruen onwards, if they can assume the role and function of a meeting place. The research allowed the identification of methodologies for the transformation of car parks into meeting points: the creation of a landmark, densification and screening. In addition, the results obtained offer possible new developments for these places that can be identified in the following macro-strategies: the re-urbanisation of peripheral commercial nodes, neural development between city and shopping centre, parallel and autonomous development of shopping malls and cities, commercial development and urban erosion.

Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking

This paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtain reliable tracking performance over a wide range of autonomous vehicle speed and road curvature diversities. However, a number of automated vehicles were not considered for fault-tolerant trajectory tracking methods. Motivated by this, the current research study of the Differential Lyapunov Stochastic and Decision Defect Tree Learning (DLS-DFTL) method is proposed to handle fault detection and course tracking for autonomous vehicle problems. Initially, Differential Lyapunov Stochastic Optimal Control (SOC) with customizable Z-matrices is to precisely design the path tracking for a particular target vehicle while successfully managing the noise and fault issues that arise from the localization and path planning. With the autonomous vehicle's low ceilings, a recommendation trajectory generation model is created to support such a safety justification. Then, to detect an unexpected deviation caused by a fault, a fault detection technique known as Decision Fault Tree Learning (DFTL) is built. The DLS-DFTL method can be used to find and locate problems in expansive, intricate communication networks. We conducted various tests and showed the applicability of DFTL. By offering some analysis of the experimental outcomes, the suggested method produces significant accuracy. In addition to a thorough study that compares the results to state-of-the-art techniques, simulation was also used to quantify the rate and time of defect detection. The experimental result shows that the proposed DLS-DFTL enhances the fault detection rate (38%), reduces the loss rate (14%), and has a faster fault detection time (24%) than the state of art methods.

The role of livestock grazing in long-term vegetation changes in coastal dunes: a case study from the Netherlands

The vegetation of coastal sand dunes is characterized by high species diversity and comprises some of the rarest vegetation types in North-Western Europe. Among them are dune grassland communities whose species richness relies on grazing. Those communities are assessed as a priority habitat type under the Natura 2000 legislation. In autumn 1990, Galloway cows and Nordic Fjord horses were introduced in the coastal dunes of Meijendel near The Hague (52°7'N, 4°20'E), The Netherlands, to reduce encroachment of tall grasses and shrubs, to develop bare sand patches, and as such facilitating diverse vegetation structures in the dune grasslands. In the 1950s, decades before the introduction of livestock, 41 permanent plots were installed. On average, they were examined every four years. Our study hypothesised that the livestock grazing in the set densities would halt progressive succession and facilitate regressive succession. Up to 1990, we observed an equilibrium between progressive and regressive succession. After 1990, however, our data showed a pronounced progressive succession contradicting the hypothesized effect of the livestock grazing. We relate the main observed patterns with two factors linked to rabbit populations: (i) the myxomatosis outbreak in 1954 and (ii) the rabbit Viral Haemorrhagic Disease (rVHD-1) outbreak in 1989. In addition to livestock grazing, rabbits block progressive succession by feeding on seedlings of shrub and tree species and digging burrows, creating small-scale mosaics of bare sand and initiate blowout development when collapsing. We state that the substantial decrease in rabbit numbers due to the viral diseases likely caused the observed increase of shrubs and trees in the study area's permanent plots. Climate change might have contributed to the observed increase in autonomous blowout development since 2001, as well as a decrease in atmospheric nitrogen deposition since 1990, after a strong increase the decades before.

Tracing Trails

The creator of a new simulation technology believes it could revolutionise autonomous vehicle development. Chris Pickering reports.

Estruturas desmontadas: a economia política do principal setor de exportação de automóveis do México

ABSTRACT Mexico’s current export-led economy arose from the adoption of a market-driven model based in conditions of technological disarticulation separating dominant transnational firms from the national industrial base. This determined that, in contrast to Verdoorn’s analysis, the crucial manufacturing sector would function not as a motor-force inducing a process of autonomous national development by accelerating productivity growth and promoting product and process innovations, but rather would deepen a fragmented process of production, centered on processing imported components under conditions of declining total factor productivity. Despite the profound reordering of its economic geography over the past 30 years, this process has failed to produce legitimate markers of “upgrading”.