Autonomous Vehicle Driving Using the Stream-Based Real-Time Hardware Line Detector

Abstract

To achieve the level 5 autonomous driving, which enables a totally driver-less 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 real-time line detection with lower latency and power consumption, we prefer stream-based 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 well-known 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 stream-friendly 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.