A Portable Vision-Based Real-Time Lane Departure Warning System: Day and Night

Abstract

Lane departure warning systems (LDWS) are an important element in improving driving safety. In this paper, we propose an embedded advanced RISC machines (ARM)-based real-time LDWS. As for software development, an improved lane detection algorithm based on peak finding for feature extraction is used to successfully detect lane boundaries. Then, a spatiotemporal mechanism using the detected lane boundaries is designed to generate appropriate warning signals. As for hardware implementation, a 1-D Gaussian smoother and a global edge detector are adopted to reduce noise effects in the images. By using the developed data transfer channel (DTC) in the reconfigurable field-programmable gate array (FPGA) module, the data transfer rate among the complementary metal-oxide-semiconductor (CMOS) imager module, liquid-crystal display (LCD) display module, and central processing unit (CPU) bus is about 25 frame/s for an image size of 256 times 256. In addition, the proposed departure warning algorithm based on spatial and temporal mechanisms is successfully executed on the presented ARM-based platform. The effectiveness of our system concludes that the lane detection rate is 99.57% during the day and 98.88% at night in a highway environment. The proposed departure mechanisms effectively generate effective warning signals and avoid most false warnings.