Autonomous Vehicle Driving Using the Stream-Based Real-Time Hardware Line Detector
Autonomous Vehicle Driving Using the Stream-Based Real-Time Hardware Line Detector
复制标题
使用基于流的实时硬件线路检测器的自动驾驶汽车
DOI：
10.1109/icfpt47387.2019.00093
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Shota Fukui
中科院分区：
文献类型：
--
作者：
Taito Manabe;Naofumi Yoshinaga;Yuta Imamura;Taichi Saikai;Koki Fujita;Masatomo Matsuda;Kotoko Miyata;Tatsuma Mori;Yuichiro Shibata;H. Egawa;Yuichi Kawamata;Tomohiro Kida;Ryouhei Tsugami;Ryohei Kakizaki;Taichi Katayama;Koki Tomonaga;Shota Fukui
为了实现完全无人驾驶的5级自动驾驶，接近人类水平的图像识别能力是必不可少的，因为目前安全驾驶所需的大多数信息都是以视觉信息的形式提供的，例如车道和标志。尽管图像识别包含多种技术，但本文重点关注直线检测，该技术尤其可用于车道保持。为了以更低的延迟和功耗实现实时线路检测，我们更喜欢使用 FPGA 进行基于流的硬件实现。线段检测器（LSD）是一种基于强度梯度的直线检测算法，在处理速度和精度方面优于众所周知的霍夫变换。然而，由于其迭代方法，以流方式在 FPGA 上实现 LSD 是很困难的。因此，我们提出了一种基于LSD的简单且流友好的线条检测算法。评估结果表明，所实现的系统结构紧凑，同时保持 VGA 运动图像 60 fps 的吞吐量。在本文中，我们还介绍了用于构建自动驾驶系统的其他组件。
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.