<![CDATA[The reliability of autonomous engineering platforms depends fundamentally on the seamless integration of high-bandwidth, multi-modal sensory data to perceive dynamic environments. This research addresses the persistent challenge of computational latency and perception failure in traditional sensor fusion architectures when operating under adverse conditions. The study aims to evaluate a novel hybrid signal processing architecture that optimizes the balance between edge-level feature extraction and centralized semantic synthesis. Utilizing a “Hardware-in-the-Loop” methodology, the proposed framework was tested on an embedded GPU testbed using synchronized LiDAR, RADAR, and camera datasets across 500 diverse navigational scenarios. Results demonstrate that the hybrid architecture achieves a 56% reduction in processing latency, maintaining a mean response time of 12.4 milliseconds without compromising positional accuracy, which remained stable at 0.11 meters RMSE. Furthermore, the implementation of an entropy-driven weighting mechanism allowed the system to maintain 99.2% anomaly detection accuracy during simulated sensor failures. This research concludes that decentralized feature processing is essential for the operational continuity of energy-constrained autonomous systems. The findings provide a scalable blueprint for developing resilient, low-power perception modules, asserting that hardware-aware signal processing is a prerequisite for achieving Level 5 autonomy in complex, real-world engineering applications.]]>
