Automobile manufacturers, alongside technology providers, researchers, and public agencies, are conducting extensive testing to design autonomous vehicles (AVs) algorithms that will provide a complete understanding of road users, specifically pedestrians. Pedestrian behavior and actions determination are highly unpredictable depending on behavioral beliefs, context, and socio-demographic variables. Context includes everything that potentially affects one’s behavior; in AVs–pedestrian interaction, context may consist of weather conditions, road structure, social factors norms, and traffic volume. These influencing elements, therefore, need to be focused on during the development of pedestrian interaction algorithms. For this purpose, the pedestrian behavior questionnaire for FAVs (PBQF) is designed based on the theory of planned behavior (TPB). A total of almost 1000 voluntary participants completed this multilingual survey. As socio-demographic values and physiological perception varies with local norms, regions, and ethnicity, participants from 27 countries were therefore chosen to account for this variation. One of the key findings of this study is the influence of pedestrian attributes and the context on pedestrian behavior. Pedestrian action cannot be understood without visual observation of the pedestrian themselves and their context. The findings showed that pedestrians build communication with vehicles based on their driving styles. The vehicle’s driving style leads pedestrians to think that the vehicle is human-driven or autonomous. The results also revealed that pedestrians use several cues to show their intention. The general perception of AVs was also analyzed, and the communication between AVs and pedestrians with different displaying options was investigated.

The emergence of Autonomous Vehicles (AVs) marks a significant advancement in the automotive industry, transitioning from driver-assistance technologies to fully autonomous systems. This change is particularly impactful in the Gulf Cooperation Council (GCC) region, which is a significant automotive market and technological hub. However, the adoption of AVs in the GCC faces unique challenges due to the influence of cultural norms and geographical characteristics. Our research utilizes a customized framework of the Unified Theory of Acceptance and Use of Technology (UTAUT), which is adapted to include cultural and geographical factors. This approach fills a gap in the existing literature by identifying and analyzing the key factors affecting the adoption of AVs in the GCC. Our findings indicate a difference in the receptiveness towards AVs among different demographics. Younger participants displayed a more favorable attitude towards AVs compared to older individuals. Additionally, gender and educational attainment play significant roles in the acceptance of AVs. Specifically, our results suggest that there are variations in acceptance rates among genders and individuals with varying levels of education. The United Arab Emirates (UAE) has a relatively high acceptance rate of AVs due to its advanced infrastructure and openness to technological innovations. Our study identifies facilitating conditions and performance expectancy as crucial determinants of intention to use AVs in the GCC. It emphasizes the importance of infrastructure readiness and the perceived advantages of AVs in promoting their adoption.

With respect to battery management and safe operation and maintenance scheduling of electric vehicles (EVs), it is very important to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). Accurate prediction of RUL can bring secure working conditions, avert internal and external failure, and, last, avoid any undesirable consequences. However, achieving accurate prediction of RUL is complicated for EV applications due to various reasons such as the complex operational characteristics, dynamic changes in the model parameters during the aging process, extraction of battery parameters, data preparation, and hyper-parameter tuning of the predictive model. This research proposes a novel approach that integrates Particle Swarm Optimization (PSO) with a multi-model technique for RUL prediction. The framework integrates many machine learning (ML) models and deep learning (DL) models. Combining domain knowledge, advanced optimization techniques, and learning models to make high-accuracy RUL predictions reduces maintenance costs and improves battery management systems. This study uses domain-driven feature engineering to extract battery-specific indicators, including voltage drops, charging time, and temperature fluctuations, to increase model accuracy. Among the evaluated models, LSTM demonstrates superior performance, achieving a mean absolute error (MAE) of 0.34, a root mean square error (RMSE) of 0.76, and an R² of 0.93, providing the best results in RUL prediction. The proposed research uniquely integrates PSO-based optimization with domain-driven feature engineering across multiple machine learning and deep learning models, demonstrating a unified and novel approach that significantly improves the prediction accuracy of RUL in LIBs.

For efficient grid operation and energy management, accurate forecasting of solar radiation is essential. The unpredictable nature of weather makes this task challenging to accomplish. Existing forecasting models fail to deliver accurate results under these conditions, which results in decreased operational efficiency for renewable energy systems. We are proposing a novel methodology that combines feature engineering, machine learning, and Bayesian Optimization (BO) to obtain optimal performance. First, time frequency characteristics are extracted using a Fast Fourier Transform (FFT)-based feature engineering approach to capture dominant patterns from meteorological data. The FFT features reveal essential periodic patterns, which describe solar irradiance and its associated variables, enabling models to perform better over different time periods. The model hyperparameter tuning process, which uses Bayesian Optimization, improves prediction results. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². The results show clear improvements across Random Forest (RF), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) models, with the MLP model achieving the strongest overall performance. Specifically, the MLP achieved an R² value of 0.92, with MAE and RMSE values of 1.78 and 2.75, respectively. The proposed method also demonstrates robustness under varying weather conditions and time-series cross-validation (TSCV). Overall, the combined effects of frequency-domain feature engineering and Bayesian Optimization enable robust and adaptive forecasting of solar radiation resources.