An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems.

Vision is, no doubt, one of the most important and precious gifts to humans; however, there exists a fraction of visually impaired ones who cannot see properly. These visually impaired disabled people face many challenges in their lives—like performing routine activities, e.g., shopping and walking. Additionally, they also need to travel to known and unknown places for different necessities, and hence, they require an attendant. Most of the time, affording an attendant is not easier and inexpensive, especially when almost 2.5% of the population of Pakistan is visually impaired. There exist some ways of helping these physically impaired people, for example, devices with a navigation system with speech output; however, these are either less accurate, costly, or heavier. Additionally, none of them have shown perfect results in both indoor and outdoor activities. Additionally, the problems become even more severe when the subject/the people are partially deaf as well. In this paper, we present a proof of concept of an embedded prototype which not only navigates but also detects the hurdles and gives alerts—using speech alarm output and/or vibration for the partially deaf—along the way. The designed embedded system includes a cane, a microcontroller, Global System for Mobile Communication (GSM), Global Positioning System (GPS) module, Arduino, a speech output module speaker, Light-Dependent Resistor (LDR), and ultrasonic sensors for hurdle detection with voice and vibrational feedback. Using our developed system, physically impaired people can reach their destination safely and independently.

With the development of intelligent transportation systems, research into intelligent traffic signal control has received considerable attention. To date, many traffic signal control models have been studied, where most of the models concentrate on how to minimize travel time, vehicle delay, and the number of stops or how to maximize capacity. This study introduces the Garra Rufa–inspired (GRI) algorithm, which is used to optimize traffic signal control modelling considering the number of vehicles in a queue. GRI has the characteristics of using the decision variables of the code as the operation object, directly using the objective function value for the search information, using multiple search points at the same time, and using probability search technology. Theoretical analysis of intelligent optimization and research into application methods were carried out to resolve the problem of traffic signal optimization control. The output of the GRI algorithm was compared, calibrated, and validated with SIDRA. Furthermore, to obtain more comprehensive results, the genetic algorithm (GA) and particle swarm optimization (PSO) were also compared. The results of the analysis show that the GRI decreases by 10.1% (intersection A) and 16.5% (intersection B) in the number of vehicles in the queue.

Purpose: The success or failure of an organization depends largely on the behavior of its customers towards its brand, which influences their intention to purchase. In this study, the researcher investigated the relationship between customers' lifestyle and their trust in a brand, and how this relates to their online purchase intentions. The study also examined how customers' attitudes towards the brand mediate this relationship. Methodology: The theoretical framework used was based on the theory of planned behavior, and data was collected through an online survey completed by 223 participants. Findings: The results showed that customers' lifestyle and trust in a brand have a direct impact on their online purchase intentions, and that attitude towards the brand partially mediates this relationship. The data was analyzed using SPSS and Smart PLS. Implications: Overall, these findings offer meaningful insights for telecom companies in Pakistan, enabling them to develop effective marketing strategies to augment their online sales.