A Real-Time Hazard Detection AI Accelerator for Autonomous Vehicles Based on Convolutional Neural Networks

As autonomous driving technology advances, real-time hazard detection is becoming critical for safety. Convolutional Neural Networks (CNNs), known for their powerful image processing capabilities, play a key role in autonomous vehicle perception systems, excelling in tasks such as object detection, recognition, and classification. However, existing CNN models face a trade-off between accuracy and processing speed, particularly in complex traffic scenarios. This research proposes a novel CNN architecture paired with a dedicated hardware accelerator to achieve high-precision hazard detection without compromising real-time performance. The model features an attention-based dynamic CNN structure that adjusts convolutional kernel sizes and quantities based on image complexity, optimizing resource allocation. A cross-layer feature fusion strategy improves detection of small and distant hazards like pedestrians and obstacles. Additionally, a custom AI accelerator using FPGA technology incorporates sparse matrix operations and dynamic data flow management to enhance inference efficiency and reduce power consumption. Experimental results in real-world traffic environments demonstrate high accuracy, low latency, and energy efficiency, confirming the system's robustness and potential for deployment in autonomous vehicles.