Filippo Ghilotti, Edoardo Palladin, Samuel Brucker
et al.
Safe highway autonomy for heavy trucks remains an open and unsolved challenge: due to long braking distances, scene understanding of hundreds of meters is required for anticipatory planning and to allow safe braking margins. However, existing driving datasets primarily cover urban scenes, with perception effectively limited to short ranges of only up to 100 meters. To address this gap, we introduce TruckDrive, a highway-scale multimodal driving dataset, captured with a sensor suite purpose-built for long range sensing: seven long-range FMCW LiDARs measuring range and radial velocity, three high-resolution short-range LiDARs, eleven 8MP surround cameras with varying focal lengths and ten 4D FMCW radars. The dataset offers 475 thousands samples with 165 thousands densely annotated frames for driving perception benchmarking up to 1,000 meters for 2D detection and 400 meters for 3D detection, depth estimation, tracking, planning and end to end driving over 20 seconds sequences at highway speeds. We find that state-of-the-art autonomous driving models do not generalize to ranges beyond 150 meters, with drops between 31% and 99% in 3D perception tasks, exposing a systematic long-range gap that current architectures and training signals cannot close.
Modern engineering design platforms excel at discipline-specific tasks such as CAD, CAM, and CAE, but often lack native systems engineering frameworks. This creates a disconnect where system-level requirements and architectures are managed separately from detailed component design, hindering holistic development and increasing integration risks. To address this, we present the conceptual framework for the GenAI Workbench, a Model-Based Systems Engineering (MBSE) environment that integrates systems engineering principles into the designer's workflow. Built on an open-source PLM platform, it establishes a unified digital thread by linking semantic data from documents, physical B-rep geometry, and relational system graphs. The workbench facilitates an AI-assisted workflow where a designer can ingest source documents, from which the system automatically extracts requirements and uses vision-language models to generate an initial system architecture, such as a Design Structure Matrix (DSM). This paper presents the conceptual architecture, proposed methodology, and anticipated impact of this work-in-progress framework, which aims to foster a more integrated, data-driven, and informed engineering design methodology.
Highway work zones are critical areas where accidents frequently occur, often due to the proximity of workers to heavy machinery and ongoing traffic. With technological advancements in sensor technologies and the Internet of Things, promising solutions are emerging to address these safety concerns. This paper provides a systematic review of existing studies on the application of sensor technologies in enhancing highway work zone safety, particularly in preventing intrusion and proximity hazards. Following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol, the review examines a broad spectrum of publications on various sensor technologies, including GPS, radar, laser, infrared, RFID, Bluetooth, ultrasonic, and infrared sensors, detailing their application in reducing intrusion and proximity incidents. The review also assesses these technologies in terms of their accuracy, range, power consumption, cost, and user-friendliness, with a specific emphasis on their suitability for highway work zones. The findings highlight the potential of sensor technologies to significantly enhance work zone safety. As there are a wide range of sensor technologies to choose from, the review also revealed that selection of sensors for a particular application needs careful consideration of different factors. Finally, while sensor technologies offer promising solutions for enhancing highway work zone safety, their effective implementation requires comprehensive consideration of various factors beyond technological capabilities, including developing integrated, cost-effective, user-friendly, and secure systems, and creating regulatory frameworks to support the rapid development of these technologies.
Bianca Trinkenreich, Fabio Calefato, Geir Hanssen
et al.
The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range from viewing LLMs as mere productivity tools to considering them revolutionary forces, we argue that the SE research community must proactively engage with and shape the integration of LLMs into research practices, emphasizing human agency in this transformation. As LLMs rapidly become integral to SE research - both as tools that support investigations and as subjects of study - a human-centric perspective is essential. Ensuring human oversight and interpretability is necessary for upholding scientific rigor, fostering ethical responsibility, and driving advancements in the field. Drawing from discussions at the 2nd Copenhagen Symposium on Human-Centered AI in SE, this position paper employs McLuhan's Tetrad of Media Laws to analyze the impact of LLMs on SE research. Through this theoretical lens, we examine how LLMs enhance research capabilities through accelerated ideation and automated processes, make some traditional research practices obsolete, retrieve valuable aspects of historical research approaches, and risk reversal effects when taken to extremes. Our analysis reveals opportunities for innovation and potential pitfalls that require careful consideration. We conclude with a call to action for the SE research community to proactively harness the benefits of LLMs while developing frameworks and guidelines to mitigate their risks, to ensure continued rigor and impact of research in an AI-augmented future.
Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 146 relevant publications from the DBLP Computer Science Bibliography up to the end of 2024, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 64 of the 146 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.
Abstract Nepal is a country known for its diverse and challenging topography, and it relies heavily on a robust road infrastructure network to connect its remote regions and urban centers. This study addresses the critical need for enhanced road safety and infrastructure resilience on the Siddhababa road section of the Siddhartha Highway, Nepal, notorious for its high accident rates and susceptibility to landslides. Given the road's strategic importance in connecting remote regions and its challenging topographical conditions, our research aimed to identify the most suitable pavement type to mitigate these issues. Through a detailed examination incorporating eight different soil tests, alongside evaluations of traffic loads, weather conditions, and existing pavement performance, we adopted a comparative analysis methodology to assess the viability of flexible versus rigid pavements within this unique context. Results revealed that the soil composition and environmental conditions of the Siddhababa section significantly influence pavement performance, with specific gravity, moisture content, and California Bearing Ratio (CBR) tests indicating a nuanced suitability for both pavement types under varying circumstances. Our analysis concluded that, despite the economic and staged reinforcement benefits of flexible pavements, the durability, safety, and maintenance considerations favor the adoption of rigid pavement for the Siddhababa road section. However, acknowledging the economic constraints, a hybrid approach is recommended, emphasizing rigid pavements for the most vulnerable sections and flexible pavements elsewhere. This study contributes to the pavement engineering field by providing a model for pavement type selection in mountainous regions, aiming to enhance road safety and durability amidst challenging environmental conditions.
Introduction. The development of motor transport inevitably leads to an increase in traffic intensity, resulting in challenges related to ensuring the safety and comfort of road users, particularly at uncontrolled intersections and pedestrian crossings, which are areas of heightened risk for road accidents.
Problem statement. The high likelihood of traffic accidents occurring at uncontrolled road intersections and pedestrian crossings necessitates the implementation of measures aimed at ensuring the safety of all road users, particularly pedestrians and cyclists.
Purpose. Ensuring road safety, reducing the likelihood of traffic accidents, and providing comfortable travel conditions for road users.
Materials and methods. The current regulatory acts and normative documents regarding traffic control devices, particularly traffic lights, as well as statistical information on road accidents in Ukraine, have been analyzed.
Results. This article examines the effectiveness of traffic lights as a means of traffic management and their crucial role in ensuring safety for all road users. It also explores the potential implementation of new technologies that can enhance the efficiency of traffic lights in regulating traffic flow and improving road safety.
A flaky test yields inconsistent results upon repetition, posing a significant challenge to software developers. An extensive study of their presence and characteristics has been done in classical computer software but not quantum computer software. In this paper, we outline challenges and potential solutions for the automated detection of flaky tests in bug reports of quantum software. We aim to raise awareness of flakiness in quantum software and encourage the software engineering community to work collaboratively to solve this emerging challenge.
Jasmina Ćetković, Biljana Ivanović, Radoje Vujadinović
et al.
The aim of the study is to analyse the feasibility of the second phase of the construction of the Rožaje (Montenegro) bypass project. The objectives of the construction of this bypass are to eliminate or reduce existing problems by redirecting transit flows to the bypass. Based on the observed economic costs of construction and the expected economic benefits from the project in a 20-year period, by applying Cost-Benefit Analysis (CBA), the indicators of the project economic feasibility were determined. As part of the socio-economic analysis of the project feasibility, the expected benefits for transport users (savings in travel time and savings in the vehicle exploitation costs), as well as external impacts (impacts on safety and impacts on the environment) were assessed. The analysis showed the dominant savings are in travel time and vehicle exploitation costs. The economic net present value (ENPV) of this project is positive and amounts to EUR 55 054 502, the economic internal rate of return (EIRR) is 26.88% (with a discount rate of 5%), while the benefit-cost ratio (B/CR) is 4.96. All scenarios developed within the Project Sensitivity Analysis have confirmed that this project has satisfactory economic justification.
Highway engineering. Roads and pavements, Bridge engineering
Abstract: Recently, the COVID-19 pandemic and the events related to Russia's aggression
against Ukraine, as well as the general increase in tension in international relations, have put
the global, regional and national security systems of individual countries to a severe test.
Experiences from these events provide many conclusions that should be used in the process of
adapting security systems to the new and constantly changing political-military, economic,
social and natural environment. These experiences have highlighted the importance of the
strategic resilience of the state, as well as of alliances. This was reflected in both national and
allied normative documents. In the North Atlantic Alliance, the Resilience Committee was
established as the highest advisory body for strategic resilience and preparing the society to
function in times of crisis and war. In several countries, intensive work is underway to
develop a state resilience strategy linked to a national security strategy. Unlike the latter, state
resilience strategies are designed to limit the risk of disruption to the most basic functions of
the state and society to an acceptable level and ensure that they can be restored in a reasonable
time and at a reasonable price.
Keywords: Critical infrastructure; Strategic Resilience; National security
Highway engineering. Roads and pavements, Bridge engineering
For driving safely and efficiently in highway scenarios, autonomous vehicles (AVs) must be able to predict future behaviors of surrounding object vehicles (OVs), and assess collision risk accurately for reasonable decision-making. Aiming at autonomous driving in highway scenarios, a predictive collision risk assessment method based on trajectory prediction of OVs is proposed in this paper. Firstly, the vehicle trajectory prediction is formulated as a sequence generation task with long short-term memory (LSTM) encoder-decoder framework. Convolutional social pooling (CSP) and graph attention network (GAN) are adopted for extracting local spatial vehicle interactions and distant spatial vehicle interactions, respectively. Then, two basic risk metrics, time-to-collision (TTC) and minimal distance margin (MDM), are calculated between the predicted trajectory of OV and the candidate trajectory of AV. Consequently, a time-continuous risk function is constructed with temporal and spatial risk metrics. Finally, the vehicle trajectory prediction model CSP-GAN-LSTM is evaluated on two public highway datasets. The quantitative results indicate that the proposed CSP-GAN-LSTM model outperforms the existing state-of-the-art (SOTA) methods in terms of position prediction accuracy. Besides, simulation results in typical highway scenarios further validate the feasibility and effectiveness of the proposed predictive collision risk assessment method.
Grégory Andreoli, Amine Ihamouten, Mai Lan Nguyen
et al.
Among the commonly used non-destructive techniques, the Ground Penetrating Radar (GPR) is one of the most widely adopted today for assessing pavement conditions in France. However, conventional radar systems and their forward processing methods have shown their limitations for the physical and geometrical characterization of very thin layers such as tack coats. However, the use of Machine Learning methods applied to GPR with an inverse approach showed that it was numerically possible to identify the tack coat characteristics despite masking effects due to low timefrequency resolution noted in the raw B-scans. Thus, we propose in this paper to apply the inverse approach based on Machine Learning, already validated in previous works on numerical data, on two experimental cases with different pavement structures. The first case corresponds to a validation on known pavement structures on the Gustave Eiffel University (Nantes, France) with its pavement fatigue carousel and the second case focuses on a new real road in Vend{é}e department (France). In both case studies, the performances of SVM/SVR methods showed the efficiency of supervised learning methods to classify and estimate the emulsion proportioning in the tack coats.
In this work, we use the communication of intent as a means to facilitate cooperation between autonomous vehicle agents. Generally speaking, intents can be any reliable information about its future behavior that a vehicle communicates with another vehicle. We implement this as an intent-sharing task atop the merging environment in the simulator of highway-env, which provides a collection of environments for learning decision-making strategies for autonomous vehicles. Under a simple setting between two agents, we carefully investigate how intent-sharing can aid the receiving vehicle in adjusting its behavior in highway merging scenarios.
Environmental protection, one of the most important issues nowadays, forces civil engineers to look for alternative solutions to the known ones. The use of substitute materials as an embankment fill or a ground material under the embankment instead of natural soil follows these trends. A great amount of fly ash is disposed of in landfills. It is a cost-effective material that can be used in construction instead of natural soil. The geotechnical properties of fly ash as a construction material in place of soil need to be examined. It includes laboratory tests to determine the chemical composition and geotechnical characteristics. In the present work, one-dimensional consolidation tests have been conducted to examine the compressibility behaviour of compacted fly ash and fly ash-bentonite mixtures used in the earth structures like road embankments. The analysis of the consolidation phenomenon is useful for predicting the magnitude and rate of settlement of the structure. Materials compacted at OMC to their MDD, according to Standard Proctor, were tested in Rowe-Barden type consolidometer on saturated and non-saturated samples. Coefficients of consolidation have been compared between values derived from log-time and square-root-of-time methods and direct hydraulic conductivity tests. Bentonite amount in fly ash-bentonite mixtures influences the vertical deformation of the sample.
Highway engineering. Roads and pavements, Bridge engineering
Tani Frisda, Muhammad Aswin, Ahmad Perwira M. Tarigan
Silica fume dan abu cangkang sawit merupakan salah satu material sisa atau limbah, dan belum dimanfaatkan secara optimal, baik oleh masyarakat maupun industri. Sementara, bata merupakan bahan bangunan yang masih banyak digunakan. Sampai saat ini, penggunaan bata merah masih menimbulkan isu lingkungan. Untuk itu, pada riset ini akan dibuat bata alternatif yang lebih ramah lingkungan, dimana memanfaatkan silica fume (SF) dan abu cangkang sawit (ACS/PSA), yang selanjutnya disebut dengan bata-ECC (engineered cementitious composites). Mix design dibuat dengan 16 variasi persentase yang berbeda dari SF dan ACS/PSA. Uji konsistensi dan flowability dibuat untuk mencapai kondisi SCC (self-compacting concrete). Terdapat 48 buah benda uji bata dengan ukuran 200x100x50 mm. Uji kuat tekan dilakukan pada umur 3 hari. Nilai tertinggi diperoleh pada variasi SF 10% PSA 10% sebesar 38,42 MPa. Sedangkan kuat tekan tertinggi bata merah dari beberapa panglong yang dipilih adalah 17,67 MPa. Berdasarkan ketentuan SNI 15-2094-2000, pada penelitian ini, bata-ECC tergolong dalam Kelas-150 (Mutu-A).
Highway engineering. Roads and pavements, Engineering (General). Civil engineering (General)
The risk posed by spill and subsequent fire during road transportation of flammable liquid is considered in the paper. Attention is paid to a pool fire than can occur in roadside terrain. Circumstances and road situations increasing the likelihood of a spill and fire accident are analysed. The problem under study is an assessment of thermal radiation induced by a roadside pool fire. This study applied a pool fire model known as a trench fire to a roadside situation. The trench fire is considered to be a likely type of a pool fire due to presence of roadside ditches and other oblong low areas along the road. The estimation of the thermal radiation from trench fires is carried out in the deterministic way due to actual lack of systematic uncertainty modelling related to pool fires. Deterministic models developed for estimating the radiation of pool and trench fires are presented and illustrated by a transportation case study. The case study reveals that the thermal radiation emitted by a trench fire can endanger objects positioned in the intermediate vicinity to the road. Further spread of fire into more distant locations is possible only through the domino effect. Incorporation of the thermal radiation models into a transportation risk assessment is discussed in brief. Findings of this study are viewed as knowledge that can be used for refining the estimation of risk posed by transportation of hazardous materials.
Highway engineering. Roads and pavements, Bridge engineering
Stefan Klikovits, Vincenzo Riccio, Ezequiel Castellano
et al.
Background/Context. The use of automated driving systems (ADSs) in the real world requires rigorous testing to ensure safety. To increase trust, ADSs should be tested on a large set of diverse road scenarios. Literature suggests that if a vehicle is driven along a set of geometrically diverse roads-measured using various diversity measures (DMs)-it will react in a wide range of behaviours, thereby increasing the chances of observing failures (if any), or strengthening the confidence in its safety, if no failures are observed. To the best of our knowledge, however, this assumption has never been tested before, nor have road DMs been assessed for their properties. Objective/Aim. Our goal is to perform an exploratory study on 47 currently used and new, potentially promising road DMs. Specifically, our research questions look into the road DMs themselves, to analyse their properties (e.g. monotonicity, computation efficiency), and to test correlation between DMs. Furthermore, we look at the use of road DMs to investigate whether the assumption that diverse test suites of roads expose diverse driving behaviour holds. Method. Our empirical analysis relies on a state-of-the-art, open-source ADSs testing infrastructure and uses a data set containing over 97,000 individual road geometries and matching simulation data that were collected using two driving agents. By sampling random test suites of various sizes and measuring their roads' geometric diversity, we study road DMs properties, the correlation between road DMs, and the correlation between road DMs and the observed behaviour.
Automatic image-based pavement distress detection and recognition are vital for pavement maintenance and management. However, existing deep learning-based methods largely omit the specific characteristics of pavement images, such as high image resolution and low distress area ratio, and are not end-to-end trainable. In this paper, we present a series of simple yet effective end-to-end deep learning approaches named Weakly Supervised Patch Label Inference Networks (WSPLIN) for efficiently addressing these tasks under various application settings. WSPLIN transforms the fully supervised pavement image classification problem into a weakly supervised pavement patch classification problem for solutions. Specifically, WSPLIN first divides the pavement image under different scales into patches with different collection strategies and then employs a Patch Label Inference Network (PLIN) to infer the labels of these patches to fully exploit the resolution and scale information. Notably, we design a patch label sparsity constraint based on the prior knowledge of distress distribution and leverage the Comprehensive Decision Network (CDN) to guide the training of PLIN in a weakly supervised way. Therefore, the patch labels produced by PLIN provide interpretable intermediate information, such as the rough location and the type of distress. We evaluate our method on a large-scale bituminous pavement distress dataset named CQU-BPDD and the augmented Crack500 (Crack500-PDD) dataset, which is a newly constructed pavement distress detection dataset augmented from the Crack500. Extensive results demonstrate the superiority of our method over baselines in both performance and efficiency. The source codes of WSPLIN are released on https://github.com/DearCaat/wsplin.
Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design and dynamic operation. In particular, the integration of different quantum modules has benefited from hybrid quantum systems, which provide an important pathway for harnessing the different natural advantages of complementary quantum systems and for engineering new functionalities. This review focuses on the current frontiers with respect to utilizing magnetic excitatons or magnons for novel quantum functionality. Magnons are the fundamental excitations of magnetically ordered solid-state materials and provide great tunability and flexibility for interacting with various quantum modules for integration in diverse quantum systems. The concomitant rich variety of physics and material selections enable exploration of novel quantum phenomena in materials science and engineering. In addition, the relative ease of generating strong coupling and forming hybrid dynamic systems with other excitations makes hybrid magnonics a unique platform for quantum engineering. We start our discussion with circuit-based hybrid magnonic systems, which are coupled with microwave photons and acoustic phonons. Subsequently, we are focusing on the recent progress of magnon-magnon coupling within confined magnetic systems. Next we highlight new opportunities for understanding the interactions between magnons and nitrogen-vacancy centers for quantum sensing and implementing quantum interconnects. Lastly, we focus on the spin excitations and magnon spectra of novel quantum materials investigated with advanced optical characterization.
Alexander Felfernig, Stefan Reiterer, Martin Stettinger
et al.
The knowledge engineering bottleneck is still a major challenge in configurator projects. In this paper we show how recommender systems can support knowledge base development and maintenance processes. We discuss a couple of scenarios for the application of recommender systems in knowledge engineering and report the results of empirical studies which show the importance of user-centered configuration knowledge organization.