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This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline.

Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms.

Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles.

To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

This paper aims to investigate vulnerability factors that influence thermal comfort in residential buildings in the context of climate change and variability, as well as adaptive strategies that can be adopted. There is a need for research that systematically addresses factors influencing thermal comfort in the context of climate change.

Using a vulnerability framework, this paper reviews existing literature to identify factors driving impacts to comfort, as well as strategies to increase adaptive capacity in buildings. Data were collected from several sources including international organizations, scientific journals and government authorities, following an initial Web-based subject search using Boolean operators.

Significant impacts can be expected in terms of thermal comfort inside buildings depending on four vulnerability factors: location; age and form; construction fabric and occupancy and behaviour. Despite the fact that the majority of the existing studies are technically driven and spatially restricted, there is strong evidence of interdependencies of scales in managing vulnerability and adaptive capacity.

Results from this review emphasise the importance of balance mitigation with adaptation regarding new building design and when retrofitting old buildings. The factors identified here can also be used to assist in construction of simplified tools such as a vulnerability index that helps in identifying the most vulnerable buildings and dwellings and assist in retrofit decisions.

The paper offers critical insight regarding implications in building design and policy in a vulnerability framework.

This study aims to propose a novel subjective assessment (SA) method for level 2 or level 2+ advanced driver assistance system (ADAS) with a customized case study in China.

The proposed SA method contains six dimensions, including perception, driveability and stability, riding comfort, human–machine interaction, driver workload and trustworthiness and exceptional operating case, respectively. And each dimension subordinates several subsections, which describe the corresponding details under this dimension.

Based on the proposed SA, a case study in China is conducted. Six drivers with different driving experiences are invited to give their subjective ratings for each subsection according to a predefined rating standard. The rating results show that the ADAS from Tesla outperforms the upcoming electric vehicle in most cases.

The proposed SA method is beneficial for the original equipment manufacturers developing related technologies in the future.

The need for better performance, safety and comfort is the driving force behind the dramatic growth in car electronics. The key to success depends mainly on system reliability and cost which in turn are related to the choice of technological approach. The basic topics to be investigated are, on the one hand, system architecture and, on the other, the implementation techniques involved. Large scale integration of a consistent portion of the system will be necessary but other factors such as interconnections, packages and so on must also be taken into account. The variety of boards, components and assembly techniques currently available are just adequate for present needs, but for the 1990s the continuously increasing complexity of automotive electronic systems will probably necessitate better use of what is available and possibly new ideas.

To significantly adopt and improve indoor energy efficiency in building infrastructure in developing countries can be a challenging venture. Thus, this study aimed to assess the satisfaction of indoor environmental quality and its effect on energy use intensity and efficient among student housing.

The study is quantitative and hinged on the contrast theory. A survey of 1,078 student residents living in purpose-built student housing was contacted. Using Post-Occupancy Evaluation and Multiple Linear Regression, critical variables such as thermal comfort, visual comfort and indoor air quality and 21 indicators were assessed. Data on annual energy consumption and total square metre of the indoor area were utilised to assess energy use intensity.

The study found a direct relationship between satisfaction with indoor environmental quality and energy use intensity. The study showed that students were more satisfied with thermal comfort conditions than visual and indoor air quality. Overall, these indicators contributed to 75.9% kWh/m² minimum and 43.2% kWh/m² maximum energy use intensity in student housing in Ghana. High occupancy and small useable space in student housing resulted in high energy use intensity.

Inclusions of sustainable designs and installation of smart mechanical systems are feedback to student housing designers. Again, adaptation to retrofitting ideas can facilitate energy efficiency in the current state of student housing in Ghana.

Earlier studies have argued for and against the satisfaction of indoor environmental quality in student housing. However, these studies have neglected to examine the impact on energy use intensity. This is novel because the assessment of energy use intensity in this study has a positive influence on active design incorporation among student housing.

This paper presents the development of personal thermal comfort models for older adults and assesses the models’ performance compared to aggregate approaches. This is necessary as individual thermal preferences can vary widely between older adults, and the use of aggregate thermal comfort models can result in thermal dissatisfaction for a significant number of older occupants. Personalised thermal comfort models hold the promise of a more targeted and accurate approach.

Twenty-eight personal comfort models have been developed, using deep learning and environmental and personal parameters. The data were collected through a nine-month monitoring study of people aged 65 and over in South Australia, who lived independently. Modelling comprised dataset balancing and normalisation, followed by model tuning to test and select the best hyperparameters’ sets. Finally, models were evaluated with an unseen dataset. Accuracy, Cohen’s Kappa Coefficient and Area Under the Receiver Operating Characteristic Curve (AUC) were used to measure models’ performance.

On average, the individualised models present an accuracy of 74%, a Cohen’s Kappa Coefficient of 0.61 and an AUC of 0.83, representing a significant improvement in predictive performance when compared to similar studies and the “Converted” Predicted Mean Vote (PMVc) model.

While current literature on personal comfort models have focussed solely on younger adults and offices, this study explored a methodology for older people and their dwellings. Additionally, it introduced health perception as a predictor of thermal preference – a variable often overseen by architectural sciences and building engineering. The study also provided insights on the use of deep learning for future studies.

Over-limit transportation has the characteristics of large axle load, large number of axles and lateral distribution width. Under the action of over-limit load, the coupling vibration effect of vehicle–bridge is more obvious, and the deformation of bridge components is large. Thus, research and analysis of the vehicle–bridge coupling dynamic response of long-span bridges under over-limit transportation has practical engineering significance.

Based on the principle of invariable elastic potential energy, this paper derives dynamic model of over-limit transportation n-axis flat vehicle. The numerical simulation method is used to model and calculate a cable-stayed bridge, and the static effect of the cable-stayed bridge and the dynamic response of vehicle–bridge coupling under different parameters are compared and analyzed.

The focus is on the influence of vehicle load and vehicle velocity parameters on the stress and amplitude of different cables under over-limit transportation, and the corresponding variation law is obtained.

The research on the coupled dynamic response of cable-stayed bridges has attracted the attention of many scholars, but there are relatively few studies on the coupled vibration of out-limit vehicles and bridges. In this paper, based on finite element software, a vehicle–bridge coupling model under bulk transportation is established.