AI IoT Enabled Multi-Sensor Smart Bus Door Safety System for Accident Prevention

Abstract

Safety issues are common on public transportation systems, especially in areas with high passenger density, like around the entry doors or footboard areas, where a lack of driver visibility, the sudden opening and closing of doors and the presence of many passengers can cause accidents. To overcome these problems, an artificial intelligence (AI)-Internet of Things (IoT) based multi-sensor smart bus door safety system for real-time accident prevention and intelligent door control is proposed in this paper. The proposed system will be based on the installation of sensors combining infrared (IR), ultrasonic and load sensors, which will constantly monitor the presence of passengers, spatial occupancy and the boarding status of the critical areas. To make the system more robust in the presence of noisy and variable environmental parameters such as the fluctuation of crowd density and uncertainty of motion, sensor fusion techniques are used. An ESP32 microcontroller is used at the edge to process the data at low latency and minimize the reliance on the cloud computing. The system uses preset safety parameters to assess whether the conditions are safe or unsafe, and then to actuate a motor-driven door operation mechanism to prevent unsafe operations. Under unsafe conditions, doors can only move in a limited way to reduce the risk of injury to passengers. The system also features IoT connectivity to enable real-time monitoring by transport authorities, maximizing transparency in operations and remote supervision of safety. Controlling communication protocols and data integrity mechanisms are employed to consider security issues and strengthen system reliability. In addition, a system of sound and visual warning signals is activated in order to increase driver and passenger awareness in critical situations and, as a consequence, minimize driver intervention. The proposed architecture provides a cost-effective, scalable, and energy-efficient solution to improve the safety of public transport systems using the concept of edge intelligence and integration of multiple sensors.