SmartCane

Individuals with visual impairments rely heavily on traditional white canes for mobility and environmental awareness. While conventional canes are effective for detecting ground-level obstacles, they provide limited protection against elevated hazards or fast-moving lateral threats such as cyclists or vehicles. As urban environments become increasingly dynamic, there is a growing need for assistive devices that enhance spatial awareness without compromising portability, affordability, or ease of use. Existing smart cane technologies often suffer from wide detection beams, false alerts, limited range, or bulky hardware that reduces practicality. Many systems also lack reliable methods for distinguishing between harmless nearby motion and true hazards approaching the user. This gap highlights the need for a compact sensing system capable of targeted object detection and velocity-based hazard classification. The Smart Cane project aims to develop a lightweight assistive device that integrates narrow-beam ultrasonic and LiDAR sensors with a microcontroller-based filtering algorithm. The system continuously measures distance and estimates object velocity to differentiate between routine pedestrian motion and fast-moving hazards. A statistical thresholding approach minimizes false positives while maintaining timely alerts. By combining focused sensing, real-time processing, and ergonomic design, this project seeks to enhance user safety and mobility while maintaining the simplicity and reliability of a traditional cane.