FPGABased Object Detection for Autonomous Driving System Autonomous driving systems require a realtime detection of the objects including pedestrians and obstacles on the roads. Object detection by image processing is a popular approach for autonomous driving systems. However, there are complex reflections caused by multiple light sources and objects on the roads, which disturb the robust and realtime object detection. This paper describes an FPGAbased object detection method for autonomous driving systems using multiple CMOS cameras. Our system is implemented on Xilinx Zynq7020 SoCFPGA, in which realtime processing of tracking whitelines for lanekeeping and obstaclespedestrians detection on the roads are executed by the hardware on the Programmable Logics, and the whole system is controlled by a software on the Processing System (CPU) written in Python language.

Autonomous Vehicle Driving Using the StreamBased RealTime Hardware Line Detector To achieve the level 5 autonomous driving, which enables a totally driverless vehicle, image recognition ability that is close to the human level is essential, since most information required for safe driving is currently provided as visual information, such as traffic lanes and signs. Though the image recognition includes various technologies, we focus on line detection in this paper, which can be used especially for lane keeping. To achieve realtime line detection with lower latency and power consumption, we prefer streambased hardware implementation using an FPGA. A line segment detector (LSD) is an algorithm for line detection based on intensity gradient, and is better than the wellknown Hough transform in terms of processing speed and accuracy. However, to implement the LSD on FPGAs in a stream manner is difficult due to its iterative approach. Therefore, we propose a simple and streamfriendly line detection algorithm based on the LSD. Evaluation results reveal that the implemented system is compact while maintaining 60 fps throughput for VGA moving images. We also introduce other components to be used to build an autonomous driving system in this paper.