Publication

Abstract : Ensuring passenger safety, particularly for women, and addressing driver behavior and poor driving techniques are critical concerns in today's transportation industry. Incidents of driver drowsiness leading to collisions further exacerbate these issues. Moreover, passengers often encounter various challenges during cab travel, including forced cancellations by drivers. These factors have significantly deterred passengers from utilizing taxi services. Unfortunately, the current market lacks comprehensive solutions that can simultaneously address these problems. However, several effective methods, such as facial detection and head movement analysis, can successfully identify drowsy driving. Despite this, there is a lack of systems that guarantee passenger safety and effectively detect instances of canceled rides. The real-time nature of driving behavior presents challenges in early identification and categorization. To address these concerns, a proposed solution involves the development of a mobile application system. The application continuously monitors the driver's voice and activity and promptly notifies management of any abnormalities. Initially, the driver's voice is continuously recorded and analyzed. If the app detects a cancellation initiated by the driver, appropriate notifications are generated. In the subsequent phase, continuous capture and analysis of the driver's facial images enable the detection of drowsiness levels and eye movements, ensuring passenger safety. Overall, this application enhances passenger safety by actively monitoring the behavioral and psychological aspects of the driver.