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This study addresses the critical safety issue of declining retroreflectivity values of road traffic signs, which can lead to unsafe driving conditions, especially at night. The paper aims to predict the retroreflectivity coefficient values of these signs and to classify their status as acceptable or rejected (in need of replacement) using machine learning models. Moreover, logistic regression and survival analysis are used to predict the median lifespans of road traffic signs across various geographical locations, focusing on signs in Croatia and Sweden as case studies. The results indicate high accuracy in the predictive models, with classification accuracy at 94% and an R2 value of 94% for regression analysis. A significant finding is that a considerable number of signs maintain acceptable retroreflectivity levels within their warranty period, suggesting the feasibility of extending maintenance checks and warranty periods to 15 years which is longer than the current standard of 10 years. Additionally, the study reveals notable variations in the median lifespans of signs based on color and location. Blue signs in Croatia and Sweden exhibit the longest median lifespans (28 to 35 years), whereas white signs in Sweden and red signs in Croatia show the shortest (16 and 10 years, respectively). The high accuracy of logistic regression models (72–90%) for lifespan prediction confirms the effectiveness of this approach. These findings provide valuable insights for road authorities regarding the maintenance and management of road traffic signs, enhancing road safety standards. © 2024 Tongji University and Tongji University Press

Purpose: This study aims to explore auto mechanics awareness of repairs and maintenance related to the car’s cybersecurity and provide insights into challenges based on current practice. Design/methodology/approach: This study is based on an empirical study consisting of semistructured interviews with representatives from both branded and independent auto workshops. The data was analyzed using thematic analysis. A version of the capability maturity model was introduced to the respondents as a self-evaluation of their cybersecurity awareness. Findings: Cybersecurity was not found to be part of the current auto workshop work culture, and that there is a gap between independent workshops and branded workshops. Specifically, in how they function, approach problems and the tools and support available to them to resolve (particularly regarding previously unknown) issues. Research limitations/implications: Only auto workshop managers in Sweden were interviewed for this study. This role was picked because it is the most likely to have come in contact with cybersecurity-related issues. They may also have discussed the topic with mechanics, manufacturers or other auto workshops – thus providing a broader view of potential issues or challenges. Practical implications: The challenges identified in this study offers actionable advice to car manufacturers, branded workshops and independent workshops. The goal is to further cooperation, improve knowledge sharing and avoid unnecessary safety or security issues. Originality/value: As cars become smarter, they also become potential targets for cyberattacks, which in turn poses potential threats to human safety. However, research on auto workshops, which has previously ensured that cars are road safe, has received little research attention with regards to the role cybersecurity can play in repairs and maintenance. Insights from auto workshops can therefore shed light upon the unique challenges and issues tied to the cybersecurity of c, CC BY 4.0 LEGAL CODE© 2024, Emerald Publishing Limited.Article publication date: 1 February 2024Correspondence Address: D. Hedberg; School of Informatics, University of Skövde, Skövde, Sweden; email: davidhedberg@hotmail.com

This study investigates the effect of three key firm characteristics (diversification, size, and age) on road freight companies’ early adoption of alternative fuel vehicles (AFVs). While previous studies have identified the determinants of the adoption intention of AFVs, a gap in the literature is research into firm characteristics and the early adopters of AFVs. Because early adopters play an important role in the diffusion of innovation, it is imperative to fill this gap. Based on a survey of 156 Swedish road freight companies and logistic regression analyses, we report that firm size and diversification affect the early adoption of AFVs. In addition, the partial impact of the importance of driver comfort and payload capacity on early adoption of AFVs has been reported. Other factors, including purchase price, fuel cost, and truck refueling efficiency, were not found to discriminate early adopters of AFVs from those who had not yet adopted AFVs. These findings imply to policymakers that the current instruments in use in Sweden, including the greenhouse gas mandate and the relatively high taxation on diesel fuel, have led to the adoption of AFVs by larger road freight companies. For vehicle producers aiming to scale up the production and sales of AFV, the findings suggest the benefit of targeting diversified and larger road freight companies in the first place. © 2024 The Author(s)

Photovoltaic noise barriers (PVNBs) have the potential to contribute to sustainable urban development by increasing the supply of renewable energy to cities while decreasing traffic noise pollution. However, estimating the power generation of PVNBs at the city or national scale remains a challenge due to the complexities of the urban environment and the difficulties associated with collecting data on road noise barriers (RNBs) and radiation. This study used RNBs, 2.5-dimensional (2.5D) buildings, and hourly time resolution radiation data, to estimate the power generation of PVNBs in 52 of China's major cities. First, hourly building shadows were estimated for each day of the year, covering the period from sunrise to sunset, to identify areas of RNB that are shaded at any given time. Second, hourly clear-sky radiation data were collected and corrected using a radiation correction model to simulate real weather radiation. Finally, utilizing an inclined surface radiation estimation model, the photovoltaic (PV) potential both inside and outside RNBs affected by building shadows was assessed. Subsequently, the power generation of PVNB was estimated based on parameters of mainstream PV systems in the market. The results show that the RNB mileage in 52 selected cities represents 87.7% of China's total RNB mileage. Building shadows often result in a radiation loss of approximately 30% for RNBs reception. The installed capacity and annual power generation of PVNBs in all investigated cities are 2.04 GW and 690.74 GWh, respectively. This study estimates the comprehensive PV potential of potentially exploitable PVNBs in China, offering essential scientific insights to inform and facilitate the strategic development of PVNB projects at both the national and municipal levels., Article; Export Date: 06 March 2024; Cited By: 0; Correspondence Address: M. Chen; School of Geography, Nanjing Normal University, Nanjing, No.1, Wenyuan Road, Qixia District, 210023, China; email: chenmin0902@163.com; CODEN: APEND

Spatio-temporal traffic patterns reflecting the mobility behavior of road users are essential for learning effective general-purpose road representations. Such patterns are largely neglected in state-of-the-art road representation learning, mainly focusing on modeling road topology and static road features. Incorporating traffic patterns into road network representation learning is particularly challenging due to the complex relationship between road network structure and mobility behavior of road users. In this paper, we present TrajRNE – a novel trajectory-based road embedding model incorporating vehicle trajectory information into road network representation learning. Our experiments on two real-world datasets demonstrate that TrajRNE outperforms state-of-the-art road representation learning baselines on various downstream tasks.

The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs. The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in Sweden. The study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity coordinates. The study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, respectively. The supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R2 of 97

Vehicle manoeuvre analysis is vital for road safety as it helps understand driver behaviour, traffic flow, and road conditions. However, classifying data from in-vehicle acquisition systems or simulators for manoeuvre recognition is complex, requiring data fusion and machine learning (ML) algorithms. This paper proposes a hybrid approach that combines multivariate multiscale entropy (MMSE) and one-dimensional convolutional neural networks (1D-CNNs). MMSE is utilised for early feature extraction and data fusion, and the extracted features are classified using 1D-CNNs, achieving an impressive 87% test accuracy in multiclass classification. This paper provides insights into improving vehicle manoeuvre classification using advanced ML techniques and data fusion methods to handle complex data sets effectively. Ultimately, this approach can enhance the understanding of driver behaviour, inform policy decisions, and develop more effective strategies to enhance road safety.

The purpose of this exploration of blockchain in vehicle management based on Digital Twins in Vehicular Adhoc Networks (VANETs) is to further improve intelligent transportation in smart cities. In view of the complexity of pedestrians in the real road network, the Digital Twins (DTs) technology is used to map the traffic situation in the real road network to the virtual space. Furthermore, the concrete interaction of vehicle data information is stored and transmitted by using blockchain technology. Finally, the DTs model of vehicle-mounted Ad Hoc network based on blockchain is constructed, and its performance is analyzed by simulation. The results suggest that the model algorithm adopted in this work shows a lower average delay time, its data message delivery rate is basically stable at 80%, the data message leakage rate is basically stable at approximately 10%, and the communication overhead does not exceed 700 bytes. Therefore, the in-vehicle self-organizing network model constructed in this work shows high network security performance while ensuring low latency performance, enabling information to interact more efficiently. Therefore, it can provide an experimental basis for the intelligent development and safety performance improvement of the transportation field of smart cities.

Advances in IoT and IoV technology have made connected autonomous vehicles (CAVs) data sources. Using CAVs as data sources and in incident management algorithms can create faster, more reliable, and more effective algorithms. This paper proposes a modified standard normal deviation (MSND) incident detection algorithm that uses CAVs as data sources and considers multiple traffic parameters. MSND is utilized in conjunction with two other incident detection algorithms, Standard Normal Deviation (SNS) and California (CAL), in a method of incident management known as Variable Speed Limits (VSL). SUMO Traffic Simulation Software is used to evaluate the effectiveness of the proposed method. A 10.4-kilometer road network is developed. Numerous scenarios are simulated on this road network, with variables including traffic demand, autonomous vehicle penetration rate, incident location, incident length, and incident lane. On the effectiveness metrics of detection rate, false alarm rate, and mean time to detect, simulation results demonstrate that the proposed method outperforms the SND and California methods. In terms of detection rate, the MSND algorithm performs the best, with a 12.27% improvement over the SND algorithm and a 21.99% improvement over the California method. After integrating all incident detection algorithms with the VSL traffic management method and simulating each combination, it was determined that the MSND-VSL integration reduced average density in the critical region by 19.73 percent, followed by SND-VSL with a 13.94 percent reduction and CAL-VSL with a 9.9 percent reduction. IEEE, Export Date: 25 September 2022; Article

Objective: To investigate the burden of pedestrian injuries, including pedestrian fall injuries (PFI), compared to other transport-related injuries in Sweden and document their characteristics in terms of demographics, causes, type of injuries, and severity level with a focus on long-term consequences. Methods: Data were retrieved from the national Swedish Traffic Accident Data Acquisition register. A total of 361,531 fatalities and injuries were reported by emergency hospitals during 2010–2019, of which 127,804 were pedestrians (35%). We assessed the magnitude of PFIs and conducted comparative analyses to assess differences compared to other types of road users regarding sex, age, severity level, injury circumstances, hospital care, causes of accidents, and type of injuries. Results: Pedestrians were the second largest group of traffic-related deaths in Sweden after car occupants and accounted for just over a quarter of all fatal accidents in the road traffic environment. Of the total number of pedestrian fatalities, three out of four have been in collision accidents and the others in fall-related accidents. In terms of injuries, pedestrians were the largest group among all road users, regardless of the type of accident. PFIs accounted for a third of all injuries in the road traffic environment and nearly half of all injuries resulting in permanent medical impairment (i.e., 2.2 times more long-term consequences among PFIs compared to injured car occupants). Females (particularly middle-aged and older) and older adults were overrepresented, and most PFIs occurred on urban and municipal roads. The causes were often related to maintenance (e.g., slippery surfaces such as ice, snow, leaves or gravel together with uneven pavements and roads are the cause three out of four of PFIs). Among collision injuries, the representation was almost equal for sex and age. Conclusions: Injuries and fatalities among pedestrians are a considerable issue in the road traffic environment

Shortening inter-vehicle distance can increase traffic throughput on roads for increasing volume of vehicles. In the process, traffic accidents occur more frequently, especially for multi-car accidents. Furthermore, it is difficult for drivers to drive safely under such complex driving conditions. This article investigates multi-vehicle longitudinal collision avoidance issue under such traffic conditions based on the Advanced Emergency Braking System (AEBS). AEBS is used to avoid collisions or mitigate the impact during critical situations by applying brake automatically. Hierarchical multi-vehicle longitudinal collision avoidance controller is proposed to guarantee safety of multi-cars using Vehicle-to-Infrastructure (V2I) communication capability in addition to radar for longitudinal vehicle control. High-level controller is designed to ensure safety of multi-cars and optimize total energy by calculating the target braking force. Vehicle network is used to get the key vehicle-road interaction data and constrained hybrid genetic algorithm (CHGA) is adopted to decouple the vehicle-road interactive system,which can obtain the maximum ground friction through vehicle-road data, and provide key predictive parameters for multi-vehicle safety controller. Lower level non-singular Fractional Terminal Sliding Mode(NFTSM) Controller is built to achieve control goals of high-level controller. Simulations are carried out under typical driving conditions. Results verify that the proposed system in this article can avoid or mitigate the collision risk compared to the vehicle without this system. © 1967-2012 IEEE.

Since vehicles from multiple roads frequently merge at intersections, it formulates a typical traffic bottleneck of modern transportation systems. Proper vehicle coordination and motion plan at road intersections are of importance to guarantee safety as well as improving the traffic throughput, fuel efficiency and so on. In this paper, we try to present a general dedicated intersection coordination framework for autonomous vehicles, where both high- and low-level planners are appropriately designed and integrated. In the high-level planner, two different strategies are formulated to coordinate the autonomous vehicles to generate reference trajectories and feasible ``tunnels'', respectively. Especially, a novel space-time-block based resource allocation scheme is presented to describe the feasible tunnels. Furthermore, to avoid collisions with unexpected obstacles such as pedestrians, bicycles or other vehicles with human drivers, a low-level planner is designed to generate practical trajectories based on the solutions from the high-level planner, according to their local on-board observations. Simulations and practical experiments are carried out, to show that our proposed coordination framework can achieve obvious performance advantages in various traffic metrics, including the throughput, fairness in driving maneuvers and driving comfort, etc. We also find that the high-level planner is effective in eliminating possible deadlocks among autonomous vehicles, which is rarely discussed in existing investigations., QC 20221116

Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a large-scale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks., QC 20221107

Purpose: The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios. Design/methodology/approach: A unified model of ODD monitoring is constructed, which consists of three modules: weather condition monitoring for unusual weather conditions, such as rain, snow and fog; vehicle behavior monitoring for abnormal vehicle behavior, such as traffic rule violations; and road condition monitoring for abnormal road conditions, such as road defects, unexpected obstacles and slippery roads. Additionally, the applications of the proposed unified ODD monitoring framework are demonstrated. The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications. Findings: First, the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework. Second, the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework. Third, the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects. Originality/value: The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments., QC 20230602

22nd International Arab Conference on Information Technology, ACIT 2021 -- 21 December 2021 through 23 December 2021 -- -- 176492, Every year thousands of people lose their life due to road accidents. One of the main reasons for these accidents is driver drowsiness. In driver drowsiness, the driver slept while driving, which causes the road accident, especially on the long routes. Driver fatigue and micro sleep while driving caused the fatal accident and death of human beings. To overcome this problem, we are implementing a technique in which we capture the image of the driver. After capturing the image of the driver, we process driver images to detect driver drowsiness. For the processing of the driver image, we are using two different techniques with each other. In the first technique, we are using the Dlib for image drowsiness detection by detecting that driver’s eyes are closed and the driver is yawning. In the second technique, we used CNN for the detection of yawning and the eyes of the driver are closed or not and predict driver drowsiness. After implementing the two techniques we combine the output of both techniques. After combining both techniques we test the system, and it gives us very good results. © 2021 IEEE.

22nd International Arab Conference on Information Technology, ACIT 2021 -- 21 December 2021 through 23 December 2021 -- -- 176492, Every year thousands of people lose their life due to road accidents. One of the main reasons for these accidents is driver drowsiness. In driver drowsiness, the driver slept while driving, which causes the road accident, especially on the long routes. Driver fatigue and micro sleep while driving caused the fatal accident and death of human beings. To overcome this problem, we are implementing a technique in which we capture the image of the driver. After capturing the image of the driver, we process driver images to detect driver drowsiness. For the processing of the driver image, we are using two different techniques with each other. In the first technique, we are using the Dlib for image drowsiness detection by detecting that driver’s eyes are closed and the driver is yawning. In the second technique, we used CNN for the detection of yawning and the eyes of the driver are closed or not and predict driver drowsiness. After implementing the two techniques we combine the output of both techniques. After combining both techniques we test the system, and it gives us very good results. © 2021 IEEE.

Road noise barriers (RNBs) are important urban infrastructures to relieve the harm of traffic noise pollution for citizens. Therefore, obtaining the spatial distribution characteristics of RNBs, such as precise positions and mileage, can be of great help for obtaining more accurate urban noise maps and assessing the quality of the urban living environment for sustainable urban development. However, an effective and efficient method for identifying RNBs and acquiring their attributes in large areas is scarce. This study constructs an ensemble classification model (ECM) to automatically identify RNBs at the city level based on Baidu Street View (BSV). Firstly, the bootstrap sampling method is proposed to build a street view image-based train set, where the effect of imbalanced categories of samples was reduced by adding confusing negative samples. Secondly, two state-of-the-art deep learning models, ResNet and DenseNet, are ensembled to construct an ECM based on the bagging framework. Finally, a post-processing method has been proposed based on geospatial analysis to eliminate street view images (SVIs) that are misclassified as RNBs. This study takes Suzhou, China as the study area to validate the proposed method. The model achieved an accuracy and F1-score of 0.98 and 0.90, respectively. The total mileage of the RNBs in Suzhou was 178,919 m. The results demonstrated the performance of the proposed RNBs identification framework. The significance of obtaining RNBs attributes for accelerating sustainable urban development has been demonstrated through the case of photovoltaic noise barriers (PVNBs).

The road transport is diverging towards electrical drive solutions from the current IC engine powertrain. The vision for zero CO2 emissions has led to stringent laws that look promising to achieve through electric powertrain compared to the conventional powertrain. The current vehicles can be equipped with an IC engine, electric or hybrid powertrain for lesser emissions, better performance in power, or energy consumption. The automotive industry still upholds the possibility of IC engine powertrain because of available infrastructure, resources, and easy availability. Meanwhile, the electric powertrain commits to zero emissions, zero dependencies on the direct use of fossil fuels, energy regeneration, and less energy consumption. The switching to electrical powertrains will require enough energy generation capability through sustainable mediums like solar, bioenergy and wind generation sources. Whereas the energy generation through conventional ways like fossil fuels, nuclear energy might lead to more emissions compared to the IC engine powertrain emissions on the road. Hence, this research analyses the energy availability and difference in energy requirement for a growing number of electric vehicles in four major markets: EU, China, USA, and India. The research also includes the impact of the electric powertrain on CO2 emissions emitted during the electricity generation required for it. The research shows the difference between the same vehicle's energy consumption at three different modes: conventional, electric, and hybrid, using a Simulink model. The life cycle assessment is also performed for all three modes of the powertrain to understand their impact on greenhouse gas emissions during production, driving, and recycling phases., QC 20220309

This paper proposes a parallel recommendation engine, PRECOM, for traffic control operations to mitigate congestion of road traffic in the metropolitan area. The recommendation engine can provide, in real-time, effective and optimal control plans to traffic engineers, who are responsible for manually calibrating traffic signal plans especially when a road network suffers from heavy congestion due to disruptive events. With the idea of incorporating expert knowledge in the operation loop, the PRECOM system is designed to include three conceptual components: an artificial system model, a computational experiment module, and a parallel execution module. Meanwhile, three essential algorithmic steps are implemented in the recommendation engine: a candidate generator based on a graph model, a spatiotemporal ranker, and a context-aware re-ranker. The PRECOM system has been deployed in the city of Hangzhou, China, through both offline and online evaluation. The experimental results are promising, and prove that the recommendation system can provide effective support to the current human-in-the-loop control scheme in the practice of traffic control, operations, and management., QC 20221031

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

In this paper, we conduct a mathematical analysis of the model of ensuring the road trains movement stability by changing the design of coupling devices to determine the critical characteristic parameters of the road trains, which result in the loss of en-route directional stability under external action. The concept of the model was to separate the process of yawing of the road trains and its elements (due to external perturbing action) on the highway into several typical stages. The main parameters of the stages (the displacement amplitude and rotation angle of the road trains elements in relation to the driving direction) were determined based on the initial conditions of the road trains movement, the force and duration of the external action. The most dangerous areas of external action application to the road trains were determined in this paper. The maximum permissible exposure limit should not exceed 0.5-1.0% of the road trains trailer momentum, with duration having the greater effect than the amount of impact. The results obtained can be used in mechanical engineering to improve the road trains performance. © Published under licence by IOP Publishing Ltd.

Vehicular communications disclose rich information about the vehicles and their whereabouts. Pseudonymous authentication secures communication while enhancing user privacy. To enhance location privacy, cryptographic mix-zones were proposed to facilitate vehicles covertly transition to new ephemeral credentials. The resilience to (syntactic and semantic) pseudonym linking (attacks) highly depends on the geometry of the mix-zones, mobility patterns, vehicle density, and arrival rates. We introduce a tracking algorithm for linking pseudonyms before and after a cryptographically protected mix-zone. Our experimental results show that an eavesdropper, leveraging standardized vehicular communication messages and road layout, could successfully link ≈73% of pseudonyms during non-rush hours and ≈62% of pseudonyms during rush hours after vehicles change their pseudonyms in a mix-zone. To mitigate such inference attacks, we present a novel cooperative mix-zone scheme that enhances user privacy regardless of the vehicle mobility patterns, vehicle density, and arrival rate to the mix-zone. A subset of vehicles, termed relaying vehicles, are selected to be responsible for emulating non-existing vehicles. Such vehicles cooperatively disseminate decoy traffic without affecting safety-critical operations: with 50% of vehicles as relaying vehicles, the probability of linking pseudonyms (for the entire interval) drops from ≈68% to ≈18%. On average, this imposes 28 ms extra computation overhead, per second, on the Road-Side Units (RSUs) and 4.67 ms extra computation overhead, per second, on the (relaying) vehicle side; it also introduces 1.46 KB/sec extra communication overhead by (relaying) vehicles and 45 KB/sec by RSUs for the dissemination of decoy traffic. Thus, user privacy is enhanced at the cost of low computation and communication overhead., QC 20210323

Traction adaptive motion planning and control has the potential to improve the ability of automated vehicles to avoid accidents in critical situations. However, such functionality requires an accurate friction estimate for the road ahead of the vehicle, that is updated in real time. Current state of the art friction estimation techniques include high accuracy local friction estimation in the presence of tire slip, as well as rough classification of the road surface ahead of the vehicle, based on forward looking camera. In this paper, we show that neither of these techniques in isolation yield satisfactory behavior when deployed with traction adaptive motion planning and control functionality. However, we also identify that the two techniques are complementary in terms of the key properties accuracy, availability and foresight, indicating that combining the techniques may be meaningful. To this end, we propose a fusion method based on heteroscedastic gaussian process regression, and present initial simulation based results indicating that near-optimal traction adaptive motion planning and control can be achieved by such a method., QC 20220930Part of proceedings: ISBN 978-172819142-3

The present study exposes an analysis of the suspension system technical state evaluation for civil vehicles that have been subject to armouring processes. Such evaluation is performed through a mechanised revision established by state regulation and is based on the method defined by EuSAMA. The development of this analysis focuses on establishing the existing relation between the ballistic resistance integrated to a vehicle and the dynamic effect exercised for the modification of its mass, according to two reliability/safety driving measurement criteria: (i) tyre-road adhesion index and (ii) tyre excitation phase angle. The study proposes new elements to the current procedure established to evaluate the suspension of civil armoured cars considering the two measurement criteria, which can be acquired by a standard commercial suspension tester machine. Copyright © 2013 Inderscience Enterprises Ltd.

During the last decades, the study of cities has been transformed by new approaches combining engineering and complexity sciences. Network theory is playing a central role, facilitating the quantitative analysis of crucial urban dynamics, such as mobility, city growth, or urban planning. In this work we focus on the spatial aspects of congestion. Analyzing a large amount of real city networks, we show that the location of the onset of congestion changes according to the considered urban area, defining, in turn, a set of congestion regimes separated by abrupt transitions. To help unveiling this spatial dependencies of congestion (in terms of network betweenness analysis), we introduce a family of planar road network models composed by a dense urban center connected to an arboreal periphery. These models, coined as GT and DT-MST models, allow us to analytically, numerically, and experimentally describe how and why congestion emerges in particular geographical areas of monocentric cities and, subsequently, to describe the congestion regimes and the factors that promote the appearance of their abrupt transitions. We show that the fundamental ingredient behind the observed abrupt transitions is the spatial separation between the urban center and the periphery, and the number of separated areas that form the periphery. Elaborating on the implications of our results, we show that they may have influence in the design and optimization of road networks regarding urban growth and the management of daily traffic dynamics.

The idea of applying a dynamic programming strategy to evaluating certain objective functions on trees is fairly straightforward. The road for this idea to develop into theories of width parameters has been not so straight. Hans Bodlaender has played a major role in the process of mapping out that road. In this sentimental journey, we will recount our collective road trip over the past decades., QC 20201214

Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to counteract this. The system consists of three components i.e. localization, road monitoring, and scheduling. The localization of vehicles is performed using a Rao-Blackwellized extended particle filter, combining vehicle mounted sensors with signal strengths of WiFi access points. Two methods for road monitoring are described: a Kalman filter used together with a model of the vehicle suspension system, and a relative condition measure based on the power spectral density. Lastly, a method for taking automatic action on an ill-conditioned road segment is proposed in the form of a rescheduling algorithm. The scheduling algorithm is based on the large neighborhood search and is used to integrate road service activities in the short-term production schedule while minimizing introduced production disturbances. The system is demonstrated on experimental data collected in a Swedish underground mine., QC 20200716

In 2013, Finland introduced legislation increasing gross vehicle weights (GVW) on a number of trucks. Since the actual impact on road damage is nearly impossible to quantify in advance, the present analysis of road surface measurements is intended to provide knowledge regarding the impact on road damage. Rutting is influenced by many conflated phenomena. To indicate and capture the effect on road damage due to the new maximum GVW for certain truck- and trailer combinations, the analysis follows two different but mutually supporting lines of reasoning, empirical and theoretical, respectively. Analysis of measured rut depth is supported by theoretical calculations. Theoretical findings clearly indicate that the relative increase in road damage induced by higher GVW is larger than the increase in relative payload capacity. There was a rapid fleet shift towards heavier trucks after 2013, and statistical (empirical) analysis of road surface measurements shows an increase in rutting after this introduction., QC 20200617

Forest roads are characterized by difficult transport and operating conditions and a considerable distance from sources of traditional road-building materials. Local ash and slag waste of the state district power station is an effective road-building material for the construction of forest roads. The article presents the test results of the building properties of the ash of the Reftinskaya TPP and the physicomechanical characteristics of the ash-mineral mixtures prepared with its use. Based on the results of laboratory studies, technical solutions for the use of ash-mineral mixtures in the design and construction of high-strength coatings of forest roads have been developed: the main design indicators, requirements for the materials used in the composition, approximate compositions of the ash-mineral mixtures are given, technical measures for the organization and quality control of construction work are assigned. © Published under licence by IOP Publishing Ltd.

Rapid advancements in driver assistance technology will lead to the integration of fully autonomous vehicles on our roads that will interact with other road users. To address the problem that driverless vehicles make interaction through eye contact impossible, we describe a framework for estimating the crossing intentions of pedestrians in order to reduce the uncertainty that the lack of eye contact between road users creates. The framework was deployed in a real vehicle and tested with three experimental cases that showed a variety of communication messages to pedestrians in a shared space scenario. Results from the performed field tests showed the feasibility of the presented approach. © 2020 IEEE.

With the growing technological advances in autonomous driving, the transport industry and research community seek to determine the impact that autonomous vehicles (AV) will have on consumers, as well as identify the different factors that will influence their use. Most of the research performed so far relies on laboratory-controlled conditions using driving simulators, as they offer a safe environment for testing advanced driving assistance systems (ADAS). In this study we analyze the behavior of drivers that are placed in control of an automated vehicle in a real life driving environment. The vehicle is equipped with advanced autonomy, making driver control of the vehicle unnecessary in many scenarios, although a driver take over is possible and sometimes required. In doing so, we aim to determine the impact of such a system on the driver and their driving performance. To this end road users' behavior from naturalistic driving data is analyzed focusing on awareness and diagnosis of the road situation. Results showed that the road features determined the level of visual attention and trust in the automation. They also showed that the activities performed during the automation affected the reaction time to take over the control of the vehicle. © 2020 IEEE.

Road trains (RT) are the main transport in agriculture. However, their accident rate is quite high. In the vehicles movement research their design characteristics and operating modes are mainly improved. The main way to reduce the accident rate remains the speed limit. The authors proposed methods (confirmed by the issued patents) for ensuring the stability of the RT movement by changing the design of trailer devices. The purpose of this work is to form a mathematical model for assessing the stability of the RT movement in the presence of side effects (modeling of sharp turns, impacts). Methods of analysis and synthesis, mathematical analysis, and vector geometry were applied. The process of RT yawing on the highway is split into a number of stages, for which the laws of changing its geometry and kinematics are formulated. Side effect leads to increase in the RT impulse (lateral oscillations increase the speed), or to a violation of stability (the movement amplitude goes beyond the highway or tipping). This model can be used to analyze the stability of the RT movement and evaluate methods to influence it in order to reduce the probability of accidents. © 2020 Published under licence by IOP Publishing Ltd.

We present an approach for intention-aware motion planning in an autonomous driving scenario, where a vehicle aims to traverse a road segment as quickly as possible, while constrained by road rules encoded in syntactically co-safe linear temporal logic. We show that by combining the RRTx algorithm with trajectory prediction using Mixed Observable Markov Decision Processes (MOMDP), we can achieve least-violating behavior wrt. mission completion time and the road rules, while ensuring that the likelihood of collisions remains below a user specified threshold. We illustrate the validity of our approach using simulations of a variety of traffic scenarios., Part of proceedings: ISBN 978-1-7281-6904-0QC 20210906

The service life of a road is to great extent controlled by the performance of the unbound layers. Assessing the constitutive behavior of these layers is thus imperative for a sustainable pavement design. Adequate description and measurement of unbound materials resistance to aggregate crushing is an issue, in terms of the measured response coupled to intrinsic properties of the aggregates and unbound materials gradation. In this study, a new Discrete Element Method (DEM)—based model is developed to investigate aggregate damage in unbound road materials. In order to get better insight into micro-mechanics of aggregate crushing, the developed model incorporates granular mechanics-based particle contact and damage laws. By numerical analysis with the DEM, several unbound granular materials have been examined investigating the effect of the materials gradation and aggregates toughness properties on their performance in crushing tests. The capability of the model to capture the effect of unbound material properties on its crushing performance, is evaluated based on comparison with experimental findings., QC 20211002

Road extraction from high-resolution remote sensing images is a challenging but hot research topic in the past decades. A large number of methods are invented to deal with this problem. This article provides a comprehensive review of these existing approaches. We classified the methods into heuristic and data-driven. The heuristic methods are the mainstream in the early years, and the data-driven methods based on deep learning have been quickly developed recently. With regard to the heuristic methods, the road feature model is first introduced, then, the classic extraction methods are reviewed in two subcategories: semiautomatic and automatic. The principles, inspirations, advantages, and disadvantages of these methods are described. In terms of the data-driven methods, the road extraction methods based on deep neural network, particularly those based on patched convolutional neural network, fully convolutional network, and generative adversarial network are reviewed. We perform subjective comparisons between the methods inner each type. Furthermore, the quantity performances achieved on the same dataset are compared between the heuristic and data-driven methods to show the strengthening of the data-driven methods. Finally, the conclusion and prospects are summarized., QC 20210318

When a hazardous situation approaches, the semi-autonomous vehicle opts for the driver as a fallback solution, unaware of the driver's readiness. During such a situation, autonomy misuse can occur when a driver becomes over-reliant on autonomous driving. For handling the hazardous event, controllability is paramount. We postulate that semi-autonomous vehicles decline their consideration in understanding the drivers' focus on the vehicle and the road. To examine the drivers' focus on the vehicle and the road we uphold that the vehicle must initiate exploring the drivers' situation awareness for the readiness, which could feasibly tune the ISO 26262 controllability. In this paper, we propose a physiology-based driver situation awareness for the readiness model through the driver's stress and drowsiness estimation. In addition, we boost the situation awareness for the readiness of the driver by enabling frequent interaction between the driver and the vehicle managing system. © 2020 IEEE., Conference code: 161536; Conference Paper; CODEN: IVTCD

Action intention recognition is becoming increasingly important in the road vehicle automation domain. Autonomous vehicles must be aware of their surroundings if we are to build safe and efficient transport systems. This paper explores methods for predicting the action intentions of road users based on an aware and intelligent 3D camera-based sensor system. The collected data contains trajectories of two different scenarios. The first one includes bicyclists and the second cars that are driving in a road approaching an intersection where they are either turning or continuing straight. The data acquisition system is used to collect trajectories of the road users that are used as input for models trained to predict the action intention of the road users. © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved