Key points are not available for this paper at this time.
This paper presents a connected perception framework for blind-spot awareness by connecting an external camera system with a lightweight autonomous robot. The proposed system combines real-time object detection, localization, position prediction, and collision avoidance to enhance environmental perception beyond the limitations of onboard sensing. A YOLOv11-based detection model is employed for obstacle detection, achieving high accuracy with a mean average precision (mAP@0.5) of 0.991. For obstacle localization, the external camera system achieves centimeter-level accuracy, which is further improved using Multiple Linear Regression (MLR)-based correction, reducing the localization error by approximately 75.77%. In addition, position prediction models for both camera-based and autonomous vehicle systems demonstrate strong performance, with coefficients of determination (R2) exceeding 0.98. The system also achieves effective collision avoidance, successfully stopping in all tested scenarios with response times ranging from 0.2 to 0.45 s. The integration of external and onboard perception enables effective blind-spot mitigation and improves situational awareness within simulated blind-spot corner scenarios representing real-world occlusion challenges. The results validate the system-level integration of these modules as a practical framework for addressing sensing limitations in autonomous robotic applications.
Tantrairatn et al. (Wed,) studied this question.