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This paper aims to deal with the development of a software tool to simulate and study vehicle – road interaction (VRI) to quantify the forces induced and energy released from vehicles to the road pavement, in different vehicle motion scenarios, and the energy absorbed by the road surface, speed reducers or a specific energy harvester surface or device. The software tool also enables users to quantify the energetic efficiency of the process.

Existing software tools were analysed and its limitations were identified in terms of performing energetic analysis on the interaction between the vehicle and the road pavement elements, such as speed reducers or energy harvest devices. The software tool presented in this paper intends to overcome those limitations and precisely quantify the energy transfer.

Different vehicle models and VRI models were evaluated, allowing to conclude about each model precision: bicycle car model has a 60 per cent higher precision when compared with quarter-car model, and contact patch analysis model has a 67 per cent higher precision than single force analysis model. Also, a technical study was performed for different equipment surface shapes and displacements, concluding that these variables have a great influence on the energy released by the vehicle and on the energy harvested by the equipment surface.

The developed software tool allows to study VRI with a higher precision than existing tools, especially when energetic analyses are performed and when speed reduction or energy harvesting devices are applied on the pavement.

Realistic composite vehicles with 2, 3, 5 and 9 axles, consisting of a truck with one or two trailers, are addressed in this paper by computational models for vehicle–bridge interaction analysis.

The vehicle–bridge interaction (VBI) models are formed by sets of 2-D rigid blocks interconnected by mass, damping and stiffness elements to simulate their suspension system. The passage of the vehicles is performed at different speeds. Several rolling surface profiles are admitted, considering the maintenance grade of the pavement. The spectral density functions are generated from an experimental database to form the longitudinal surface irregularity profiles. A computational code written in Phyton based on the finite element method was developed considering the Euler–Bernoulli beam model.

Several models of composite heavy vehicles are presented as manufactured and currently travel on major roads. Dynamic amplification factors are presented for each type of composite vehicle.

The VBI models for compound heavy vehicles are 2-D.

This work contributes to improving the safety and lifetime of the bridges, as well as the stability and comfort of the vehicles when passing over a bridge.

The structural response of the bridge is affected by the type and size of the compound vehicles, their speed and the conservative grade of the pavement. Moreover, one axle produces vibrations that can be superposed by the vibrations of the other axles. This effect can generate not usual dynamic responses.

The purpose of this paper is to accurately capture the risks which are caused by each road user in time.

The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.

The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.

This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.

Existing three-dimensional (3D) road-surface models use approximation methods such as a set of discrete triangular patches and cannot accurately describe changes in the geometrically designed elements along the road. This paper aims to construct a 3D road-surface model with combinations of geometric design invariants and apply the proposed model to analyse the state of motion of a wheel’s centre.

In this paper, the 3D road surface is modelled as a continuous function with combinations of geometric design invariants. By introducing the theories of differential geometries and rigid body dynamics, a wheel-road model wherein a wheel fixed to a Darboux frame moves along a curved road surface is constructed, and the wheel time-dependent properties of the velocity, angular velocity and acceleration at an arbitrary point of the surface are described using road geometry design invariants.

This paper adopts the Darboux frame to study the instantaneous spin-rolling motion of a wheel. It is found that the magnitudes of the spin-rolling velocity, the acceleration and the geometric invariants of the road surface, including the geodesic curvature, the normal curvature and the geodesic torsion, determine the instantaneous states of motion of a wheel.

This work provides a theoretical foundation for future studies of wheel motion states, such as the relationship between road geometry design invariants and driving safety, vehicle lane changing and other vehicle microbehaviours. New insights are gained in the areas of road safety and vehicles incorporating artificial intelligence.

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.

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.

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.

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.

This study aims to propose an improved linear quadratic regulator (LQR) based on the adjusting weight coefficient, which is used to improve the performance of the vehicle direct yaw moment control (DYC) system.

After analyzing the responses of the side-slip angle and the yaw rate of the vehicle when driving under different road adhesion coefficients, the genetic algorithm and fuzzy logic theory were applied to design the parameter regulator for an improved LQR. This parameter regulator works according to the changes in the road adhesion coefficient between the tires and the road. Hardware-in-the-loop (HiL) tests with double-lane changes under low and high road surface adhesion coefficients were carried out.

The HiL test results demonstrate the proposed controllers’ effectiveness and reasonableness and satisfy the real-time requirement. The effectiveness of the proposed controller was also proven using the vehicle-handling stability objective evaluation method.

The objective evaluation results reveal better performance using the improved LQR DYC controller than a front wheel steering vehicle, especially in reducing driver fatigue, improving vehicle-handling stability and enhancing driving safety.

Advances in technology enable companies to collect and analyse data, which were previously not accessible, to either enhance existing business processes or create new ones. The purpose of this paper is to document the role and impact of Big Data Analytics (BDA), and the Internet of Things (IoT), in supporting a large logistics firm’s strategy to improve driver safety, lower operating costs, and reduce the environmental impact of their vehicles.

A single case with embedded units intrinsic case study method was adopted for this research and data were collected from a “real-life” situation, to create new knowledge about this emerging phenomenon.

Truck telematics were utilised in order to better understand, and improve, driving behaviours. Remote control centres monitor live sensor data from the company’s fleet of vehicles, capturing the likes of speed, location, braking, and engine data, to inform future training programs. A combination of truck telematics and geo-information are being used to enable proactive alerts to be sent to drivers regarding possible upcoming hazards. Camera-based technologies have been adopted to improve driver safety, and fatigue management, capturing evidence of important driving events and storing data directly to the cloud, and BDA is also being used to improve truck routing, recommend optimal fuel purchasing times/locations, and to forecast predictive and proactive maintenance schedules.

The type of data collected by Company A, and similar logistics companies, has the potential to greatly inform researchers investigating autonomous vehicles, smart cities, and the physical internet.

Eco-driving, a practice informed/improved by BDA at Company A, has been linked to reductions in fuel consumption and CO₂ emissions, which bring both economic and environmental benefits. Technologies similar to Truckcam are growing in popularity in some parts of the world, to the point where it is now common practice to use dashcam assess of accidents to establish liability. This has implications for logistics firms, in other parts of the world, where such practices might not yet be so commonplace, and for drivers and society more broadly.

Improvements in utilisation and routing have the potential to reduce traffic congestion, which is responsible for losses in productivity, increases in fuel consumption, air pollution and noise, and can incite stress, aggression, anger and unsafe behaviours in drivers. Predictive analytics, which generate refuelling and maintenance schedules, have the potential to be adopted by all vehicle manufacturers, and could generate reductions in customer fuel costs, whilst improving the performance, efficiency, and life expectancy of future motor all vehicles. The high probability of occupations in the logistics industry being replaced by computer automation in the near future is also discussed.

The findings from this research serve as a valuable case example of a real-world deployment of BDA and IoT technologies in the logistics industry, and present implications for practitioners, researchers, and society more widely.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

Supplying remote wireless sensors is not an easy task if the site where the device is located is not easily accessible. In order to obtain direct measurements of the road-vehicle interactions, sensors must be placed inside the tyre environment thus a power supply must be available for their working there without any wire connection with the car main power. The paper aims to discuss these issues.

An electro-mechanical energy harvester has thus been developed for supplying an automotive wireless sensor of pressure, temperature and acceleration to be placed on the inner line of a tyre. The primary energy source is the vibrations or variable accelerations imposed to the device and induced in the tyre by the wheeling.

The harvester has been designed by means of a multi-physics optimisation based on an integrated electromagnetic-mechanical circuit simulator. Thus an automated optimisation of the device with respect to volume constraints, magnets dimensions, induction coils placement and size have been performed to increase the average power extracted from the device at different wheeling speeds.

The use of the multi-physics environment together with automated optimisation technique has been tested for the first time on the electromagnetic harvester structure.

This paper aims to propose a framework investigating the diffusion and adoption process of big data (BD) in the supply chain (SC) as a tool to manage process innovation at technological, operational and strategical levels.

A comprehensive systematic literature methodology is used to develop the theoretical conceptual framework, which comprehensively describes and captures the innovative stages of BD technology adoption process in SC with a multilevel perspective.

Results show that BD has modified the supply network concept, starting from the dyadic relationships, triads up to the creation of a streamlined and integrated network. These changes are reflected in a novel integrated vision including both benefits and barriers.

The proposed framework supports companies in redesigning the processes affected by the adoption of BD, helping them in identifying the critical elements, barriers, benefits and expected performance. One limitation is the focus of the study on the analysis of the processes of adoption of BD technology in the SC considering a particular structure of SC characterized by only two levels of supply and by a reduced number of members.

Although the role of BD in supply chain operations management (SCOM) is well acknowledged in the literature, its adoption and diffusion process from an interorganizational perspective is still missing. Specifically, the adoption stages of BD in SC have been defined at a strategic level, and successively the SC operations and technological perspective have been integrated to depict the operationalization of BD implementation and diffusion.