Improved blind-spot object estimation via camera–LiDAR sensor fusion with IMM‐KF incorporating error characteristics

Abstract Extending sensor detection range and improving perception accuracy are critical for achieving high levels of safety and reliability in autonomous driving systems. Minimizing sensor blind-spots is therefore essential. In this paper, we propose a camera–3D light detection and ranging (LiDAR) sensor fusion method that leverages road convex mirrors to achieve high-accuracy estimation of blind-spot objects that cannot be directly perceived by onboard sensors. The approach begins with sensor calibration, followed by the use of a segmentation-based deep learning detector to identify blind-spot objects and a data association process to refine detection results. To address distortion and estimation errors caused by convex mirror reflections, we incorporate an interacting multiple model–Kalman filter (IMM-KF) based on the error characteristics derived from the association process. The proposed method was validated through scenario-based experiments. Experimental results demonstrate that the proposed sensor data fusion method outperforms conventional methods in object estimation under the complex maneuvers of the blind-spot object.