Student Research Track: Ambient Lighting Based, Low Cost Blind Spot Monitoring System

Abstract

Current development in passenger vehicles has seen a wide integration of Advanced Driver Assistance Systems (ADAS) in modern vehicles. However, many drivers still operate vehicles lacking these driver assistance features such as blind spot monitoring, particularly the elderly, learners or beginners and economically constrained users. This project presents a retrofittable, human-centered ambient visual interface that provides lane specific blind spot feedback using a low-cost light bar system and computer vision-based proximity detection. Our virtual prototype divides the rear view into left and right zones and informs proximity of vehicles in blind spots through subtle, pillar to pillar placed light gradients. This design supports glanceable, non-intrusive feedback to reduce cognitive and visual demands associated with frequent mirror checks. Grounded in research drawing from robotic-human-machine interface (RHMI), ambient displays and situational awareness, this system promotes co-perception over automation that aligns with findings on trust, usability and inclusive safety design. This approach offers an inclusive, scalable retrofit solution for enhancing driver safety in under-supported vehicles and contributes to the broader HCI discourse on retrofit interaction design and cognitive ergonomics in driving.