Navigation is a critical challenge faced by people with visual impairment, often limiting their independence and safety in unfamiliar environments. This paper presents Aurora, a novel deep learning-based smartphone navigation assistant designed specifically to aid visually impaired users in real-time navigation and obstacle avoidance. Aurora leverages convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to accurately interpret visual data captured through a smartphone camera, enabling robust scene understanding and path planning. The system integrates semantic segmentation to detect sidewalks, pedestrian crossings, obstacles, and signage, while simultaneously providing audio and haptic feedback to guide users safely and efficiently. In this study, we detail the architecture of Aurora, including the dataset preparation, model training, and optimization techniques applied to ensure real-time performance on mobile devices. The assistant also incorporates GPS data and inertial sensors to enhance localization accuracy and context awareness. Extensive experiments were conducted under various environmental conditions to validate the system’s effectiveness, achieving a high accuracy rate in obstacle detection and route guidance. Aurora’s user-centric design includes customizable feedback modes to cater to different user preferences and needs, ensuring accessibility and ease of use. This paper further discusses the usability evaluation involving visually impaired participants, highlighting improvements in navigation confidence and reduction in travel time. The results demonstrate that Aurora can significantly improve independent mobility for visually impaired individuals.
K. C, N. O, Shruthi et al.· International Journal of Res...· 0 citations
Road traffic accidents on national highways pose a significant public health and economic challenge in Bangladesh, necessitating systematic safety assessment. This study analyzes accident trends, contributing factors, and spatial patterns by identifying accident-prone locations (blackspots) along the Kushtia–Jhenaidah National Highway (N704). Accident data for the period 2017–2021 were obtained from nearby police stations. In addition, a cluster random sampling approach was used to conduct a questionnaire survey involving 100 participants, including drivers and general road users, to capture behavioural insights related to accident occurrence. The study integrates descriptive statistical methods, such as trend analysis and frequency distribution, with spatial techniques including severity index evaluation, Kernel Density Estimation (KDE), and hotspot analysis.The findings indicate a decline in overall accident frequency from 2018 to 2021, while fatality rates increased in 2021. Heavy vehicles, particularly trucks, were identified as major contributors to accidents, and head-on collisions emerged as the most common crash type. Key risk factors include driver inexperience, mobile phone usage while driving, overspeeding, inadequate training, and nighttime driving conditions. The analysis further reveals that individuals aged 20–40 are the most affected group, with higher fatality rates among males and higher injury rates among females.A total of 35 accident-prone locations were identified, with several segments classified as blackspots based on accident frequency, injury severity, and fatality occurrence. The study recommends targeted interventions such as driver training, infrastructure improvement, enhanced enforcement, and coordinated policy actions to improve highway safety and reduce accident risks.
N. O, Bhagyalakshmi, Surendrababu M S et al.· International Journal of Res...· 0 citations