Advanced Driver Assistance System (ADAS) on FPGA

Advanced Driver-Assistance Systems (ADAS) can help drivers in the driving process and increase safety by automatically detecting objects, doing basic classification, implementing safeguards, etc. ADAS integrates multiple subsystems, including object detection, scene segmentation, lane so on. In this paper, we establish a framework for computer vision features, i.e., distance estimation traffic sign recognition of ADAS. Modern machine learning algorithms like Canny edge detection CNN-based approach are used detection. The system deployed aims to achieve higher (Frames Per Second) FPS one channel 55 FPS. performance FPGA is optimized software hardware co-design. Realization on DE-10 Nano board with Cyclone V dual-core ARM Cortex A9, which meets real-time processing requirements. An increasing amount automotive electronic involves significant changes modern automobile design address convergence conflicting goals - increased reliability, reduced costs, shorter development cycles. prospectus tackle car accident occurrences making even more critical. This paper proposes an efficient solution FPGA.