A few decades ago, the idea of a car driving without human assistance was something inconceivable. With the advent of deep learning-based machine learning in artificial intelligence, this imaginary idea has become part of our life. Like in other fields, these technological revolutions have brought drastic changes to the field of automated driving systems. The autonomous vehicle is in the transition state between level 3 and level 4 of automation, but many mysteries are still waiting to be solved. Understanding the environment as precisely as a human driver is still far in the future. To attain human perception requires the capturing of extensive surrounding information that depends on the onboard sensors installed on the vehicle. Because the recent autonomous vehicle is equipped with several sensors, it captures surrounding information in diverse forms. Combining these multi-domain data with sensor fusion is the open area of research that is considered in this paper. Along with sensor fusion, another area of prime importance that is necessary to be explored is the prediction of pedestrian intentions. Though the study of the prediction of a pedestrian’s intentions started approximately fifteen years ago, most of the research is based on detection rather than intention. Furthermore, this paper also discusses related research in the field of prediction of the pedestrian’s intentions. At the end of the article, this review paper includes open questions, challenges, and proposed solutions.

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.

Road accident data includes detailed information about incidents that occurred, such as where they happened, the severity of the accident, and the number of people on the road at the time. Such information is useful in determining the causes of accidents and developing potential countermeasures. This research aims to determine the factors that contribute to pedestrian fatalities and injuries in traffic accidents. This study examined 150 pedestrian-vehicle accidents that took place between 1990 and 2021 in forty countries. Eleven factors have been identified as the major causes of accidents. The categorical principal component analysis (CATPCA) technique is used to reduce the number of dimensions and identify the elements that contribute to accidents. The eleven variables are classified into three groups: human factors, roadway environment, and vehicle attributes. The study found that car speed, weather, lighting, traffic conditions, area types, accident locations, and road conditions all had a significant impact on pedestrian accidents and fatalities. The findings show that a pedestrian's state (walking, running) and intention significantly increase the risk of serious injuries and death. The analysis of the driver's status suggests that the driver's intentions may also play a role in car accidents.

This paper is based on study and implementation of the pilot project for customized, automated, highly secured class room management system known as JIC CMS (Jubail Industrial College Class room management System). In this research paper, the strategies, design, development and implementation of a pilot project for class room attendance management systems is explained in detail. As a pilot project, mobile app is built on Android platform using Xamarin programming. Prototype of proposed system provides high level of authentication by embedding face recognition and biometric verification together with RFID system. Though given model features are specially designed for our college needs, but this model can be modified for general usage and can be applied for all academic levels i.e. school, college, universities. Among the other features of system, one important feature is selection of system to work in smart mode in which system closes after predefined time and uploaded automatically to the web server.

Sensing environmental measuring parameters has a pivotal role in our everyday lives. Most of our daily life activities depend upon environmental conditions. Accurate information about these parameters also helps in several industrial applications like ventilation rate calculation, energy prediction, stock maintenance in warehouses, and saving from harmful conditions. The emergence of machine learning can make it easy to predict such time series problems. This paper describes the design of a remotely controlled robotic car for measuring and predicting humidity and temperature. A customized app for accessing the robotic car is designed to indicate predicted and realtime measured values of humidity and temperature. A sensor installed builtin helps in the measurement. The recurrent neural network (RNN) model is used to predict humidity and temperature. For this purpose, experiments are carried out in both outdoor and indoor settings. Accuracy of 85% and 90% is achieved in an outdoor environment and indoor settings.