Sentinel Mode Using Sensor Fusion for Improving Security in Shared Vehicles

This paper presents a dual-mode sentinel surveillance architecture for shared vehicles that combines low-power inertial event triggering with low-rate fisheye vision and temporal decision logic. Unlike conventional sentry systems based on continuous vision or motion-only alarms, the proposed approach prioritizes energy efficiency while preserving contextual awareness. We introduce SentinelCAR, a real-world fisheye dataset comprising 18308 images annotated for activity-aware human presence around parked vehicles. A comprehensive benchmark of state-of-the-art detectors (YOLOv8-v12 and RT-DETR variants) shows that YOLOv11m provides the best accuracy-efficiency trade-off, achieving mAP[.5:.95]≈ 0.90. The selected model is deployed on an NVIDIA Jetson Orin Nano and evaluated under multiple backends and precision formats (FP32, FP16, INT8). Experimental results demonstrate real-time performance of up to 70.4 FPS with (0.92-0.94) J per inference under TensorRT INT8 execution. In addition, an onboard post-processing pipeline based on spatial masking, motion consistency, false-positive suppression zones, and temporal hysteresis significantly improves state stability by reducing spurious activations caused by reflections and static background elements. These results provide a practical foundation for energy-aware, privacy-preserving sentinel surveillance in shared mobility scenarios.