Advanced Driver Assistance System (ADAS) on FPGA
| International Journal of VLSI & Signal Processing |
| © 2023 by SSRG - IJVSP Journal |
| Volume 10 Issue 2 |
| Year of Publication : 2023 |
| Authors : Mayank Kumar, A. Niharika, Kethireddy Anjali Reddy, Harsh Gupta, K. N. Pushpalatha |
10.14445/23942584/IJVSP-V10I2P104
How to Cite?
Mayank Kumar, A. Niharika, Kethireddy Anjali Reddy, Harsh Gupta, K. N. Pushpalatha, "Advanced Driver Assistance System (ADAS) on FPGA," SSRG International Journal of VLSI & Signal Processing, vol. 10, no. 2, pp. 22-26, 2023. Crossref, https://doi.org/10.14445/23942584/IJVSP-V10I2P104
Abstract:
Advanced Driver-Assistance Systems (ADAS) can help drivers in the driving process and increase driving safety by automatically detecting objects, doing basic classification, implementing safeguards, etc. ADAS integrates multiple subsystems, including object detection, scene segmentation, lane detection, and so on. In this paper, we establish a framework for computer vision features, i.e., lane detection, object detection, object distance estimation and traffic sign recognition of ADAS. Modern machine learning algorithms like Canny edge detection for lane detection and a CNN-based approach are used for object detection. The system deployed aims to achieve higher (Frames Per Second) FPS for one channel of 55 FPS. The performance of FPGA is optimized by software and hardware co-design. Realization on the DE-10 Nano board with Cyclone V FPGA and a dual-core ARM Cortex A9, which meets real-time processing requirements. An increasing amount of automotive electronic hardware and software involves significant changes in the modern automobile design process to address the convergence of conflicting goals - increased reliability, reduced costs, and shorter development cycles. The prospectus to tackle car accident occurrences is making ADAS even more critical. This paper proposes an efficient solution for ADAS on FPGA.
Keywords:
ADAS, CNN, FPGA, FPS, Machine Learning.
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