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The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver’s distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers’ cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers’ vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction.

We investigated the effect of driver posture on driving control following a takeover request (TOR) from autonomous to manual driving in level 3 autonomous vehicles. When providing a TOR, driving behaviors need to be investigated to develop driver monitoring systems, and it is important to clarify the effect of driver postures. Experiments were conducted using driver postures that are likely to be adopted in autonomous driving. Driver postures were set based on combinations of two types of upper-body posture and three types of foot posture. The driver’s upper body and head were set to either a forward or sideways orientation. For each of these there were three types of foot posture: both feet on the floor, crossed legs, and cross-legged sitting. Following a TOR, we compared the braking and steering maneuvers of subjects driving normally and examined the effects of posture on driver reaction time. The results show that both the upper-body and foot postures of the driver affect the steering and braking reaction time. The driver monitoring system should be able to detect posture and activate a TOR warning, and detection times up to 2 and 1.3 times faster than those for normal postures should be considered for different upper-body and foot postures, respectively.

China is a major energy-consuming country and is under great pressure to improve its energy efficiency as well as reduce its carbon emissions. Hybrid electric vehicles (HEVs), as an energy-efficient transport innovation, have the potential to reduce gasoline consumption, carbon emissions and alleviate environmental problems. Diffusion of HEVs’ adoption is a significant initiative. A sample of 433 respondents has been collected in China to predict the customers’ intention to adopt HEVs, using an extended model of the theory of planned behavior (TPB). The empirical results show that the attitude toward HEVs, subjective norm, perceived behavioral control (the three primary elements of the TPB model) and personal moral norm partially mediate the effect of consumers’ environmental concern on their intention to adopt HEVs. Consumers’ environmental concern affects the adoption intention indirectly and is significantly positively related to the attitude toward HEVs, subjective norm, perceived behavioral control and personal moral norm, which in turn influence the adoption intention positively. The results confirm the appropriateness of the TPB model and verify that the extended TPB model has good explanatory power in predicting consumers’ intention to adopt HEVs. Based on the empirical results, we discuss the implications for promoting the adoption of HEVs and provide suggestions for future study.

Climate change mitigation strategies are often technology-oriented, and electric vehicles (EVs) are a good example of something believed to be a silver bullet. Here we show that current US policies are insufficient to remain within a sectoral CO2 emission budget for light-duty vehicles, consistent with preventing more than 2 °C global warming, creating a mitigation gap of up to 19 GtCO2 (28% of the projected 2015–2050 light-duty vehicle fleet emissions). Closing the mitigation gap solely with EVs would require more than 350 million on-road EVs (90% of the fleet), half of national electricity demand and excessive amounts of critical materials to be deployed in 2050. Improving average fuel consumption of conventional vehicles, with stringent standards and weight control, would reduce the requirement for alternative technologies, but is unlikely to fully bridge the mitigation gap. There is therefore a need for a wide range of policies that include measures to reduce vehicle ownership and usage.Electric vehicles (EV) are often considered as the best chance for reducing light-duty transport emissions. Analysis of US policies shows that required emission reductions exceed feasible EV deployment, and technology alongside behaviour change is needed.

Being a major energy consumer, India is under intense pressure to reduce its energy requirements and greenhouse emissions. Electric vehicles (EVs), a sustainable form of automobile transportation, can reduce the country’s dependence on gasoline while greatly reducing its carbon footprints. The study uses an extended TPB model in order to predict adoption intention of 326 customers towards the purchase of EVs. The sample respondents have been taken from 57 dealerships of five different automobile companies. The empirical analysis of the study shows that attitude, subjective norm, perceived behavioural control, moral norm, and environmental concern have a positive relation with adoption intention of buyers. The findings of study also suggest that extended TPB model is appropriate in predicting the adoption intention of the customers towards the EVs. Based on the results, the study discusses the implications for EVs adoption in India and also provides suggestions for future research.

Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration.

Strategies to mitigate climate change often center on clean technologies, such as electric vehicles and solar panels, while the mitigation potential of a quality educational experience is rarely discussed. In this paper, we investigate the long-term impact that an intensive one-year university course had on individual carbon emissions by surveying students at least five years after having taken the course. A majority of course graduates reported pro-environmental decisions (i.e., type of car to buy, food choices) that they attributed at least in part to experiences gained in the course. Furthermore, our carbon footprint analysis suggests that for the average course graduate, these decisions reduced their individual carbon emissions by 2.86 tons of CO2 per year. Surveys and focus group interviews identify that course graduates have developed a strong personal connection to climate change solutions, and this is realized in their daily behaviors and through their professional careers. The paper discusses in more detail the specific components of the course that are believed to be most impactful, and the uncertainties associated with this type of research design. Our analysis also demonstrates that if similar education programs were applied at scale, the potential reductions in carbon emissions would be of similar magnitude to other large-scale mitigation strategies, such as rooftop solar or electric vehicles.

With the rising concern about climate change, there has been an increased public awareness that has resulted in new government policies to support scientific research for mitigating these problems. Malaysia is among the major energy-intense countries and is under an excessive burden to advance its energy efficiency and to also work towards the reduction of its carbon emission. Plug-in hybrid electric vehicles (PHEVs) have the potential to lessen the carbon emission and gasoline consumption in order to alleviate environmental problems. Most of the energy problems linked to the increasing transportation pollution are now being reduced with the solution of the adoption of PHEVs. PHEVs are seen as a solution to cut carbon emission, which prevents environmental damages. Furthermore, PHEVs’ driving range and performance can be comparable to the other hybrid vehicles as well as the conventional IC engines that have gasoline and diesel tanks. Thus, many efforts are being initiated to promote the use of PHEVs as an innovative and affordable transportation system. In order to achieve making the consumers aware of the adoption of PHEVs, we used a model which is based on the extended theory of planned behavior (TPB). This review is based on the factors affecting the adoption of PHEVs among Malaysian consumers. The model takes into account the ten key features that influence the adoption of PHEVs, such as environmental concern, personal norm, attitude, vehicle ownership costs, driving range, charging time, intention, subjective norm, perceived behavioral control, and personal norm. All these constructs are drivers towards the adoption of PHEVs. These factors affect the relationship between the adoption of PHEVs and how consumers intend to protect the environment. This review is based on improving how the “attitude-action” gap is understood as it is an important element for further studies on PHEVs. The aim of the research is to come up with a framework that examines how to modify the consumer’s environmental concerns in acquiring PHEVs. This will pave the way for more academic research and future works that can emphasize how to obtain empirical results. The authors’ recommendation is that, before a consumer’s behavior is assessed and considered, an observation of the current technology is needed with methods and knowledge of the existing technology adoption aspect.

This paper provides an in-depth analysis of the current research landscape in the field of UAV (Unmanned Aerial Vehicle) swarm formation control. This review examines both conventional control methods, including leader–follower, virtual structure, behavior-based, consensus-based, and artificial potential field, and advanced AI-based (Artificial Intelligence) methods, such as artificial neural networks and deep reinforcement learning. It highlights the distinct advantages and limitations of each approach, showcasing how conventional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization capabilities. This review underscores the critical need for innovative solutions and interdisciplinary approaches combining conventional and AI methods to overcome existing challenges and fully exploit the potential of UAV swarms in various applications.