TY - JOUR AU - David Castells AU - Vinh Ngo AU - Juan Borrego-Carazo AU - Marc Codina AU - Carles Sanchez AU - Debora Gil AU - Jordi Carrabina PY - 2022// TI - A Survey of FPGA-Based Vision Systems for Autonomous Cars T2 - ACESS JO - IEEE Access SP - 132525 EP - 132563 VL - 10 PB - IEEE KW - Autonomous automobile KW - Computer vision KW - field programmable gate arrays KW - reconfigurable architectures N2 - On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges. UR - http://dx.doi.org/10.1109/ACCESS.2022.3230282 N1 - IAM; 600.166 ID - David Castells2022 ER -