Suhr Jae Kyu, Jung Ho Gi
School of Intelligent Mechatronics Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea.
Department of Electronic Engineering, Korea National University of Transportation, 50 Daehak-ro, Chungju-si, Chungbuk 27469, Korea.
Sensors (Basel). 2018 Apr 16;18(4):1213. doi: 10.3390/s18041213.
An automatic parking system is an essential part of autonomous driving, and it starts by recognizing vacant parking spaces. This paper proposes a method that can recognize various types of parking slot markings in a variety of lighting conditions including daytime, nighttime, and underground. The proposed method can readily be commercialized since it uses only those sensors already mounted on off-the-shelf vehicles: an around-view monitor (AVM) system, ultrasonic sensors, and in-vehicle motion sensors. This method first detects separating lines by extracting parallel line pairs from AVM images. Parking slot candidates are generated by pairing separating lines based on the geometric constraints of the parking slot. These candidates are confirmed by recognizing their entrance positions using line and corner features and classifying their occupancies using ultrasonic sensors. For more reliable recognition, this method uses the separating lines and parking slots not only found in the current image but also found in previous images by tracking their positions using the in-vehicle motion-sensor-based vehicle odometry. The proposed method was quantitatively evaluated using a dataset obtained during the day, night, and underground, and it outperformed previous methods by showing a 95.24% recall and a 97.64% precision.
自动泊车系统是自动驾驶的重要组成部分，其首先要识别出空的停车位。本文提出了一种方法，该方法能够在包括白天、夜间和地下等各种光照条件下识别各类停车位标线。所提方法仅使用已安装在量产车辆上的传感器，即环视监视器（AVM）系统、超声波传感器和车内运动传感器，因此很容易实现商业化。该方法首先通过从AVM图像中提取平行线对来检测分隔线。基于停车位的几何约束，通过将分隔线配对来生成停车位候选区域。利用线条和角点特征识别这些候选区域的入口位置，并使用超声波传感器对其占用情况进行分类，从而确认这些候选区域。为了实现更可靠的识别，该方法不仅利用当前图像中找到的分隔线和停车位，还通过基于车内运动传感器的车辆里程计跟踪其位置，利用先前图像中找到的分隔线和停车位。使用在白天、夜间和地下获取的数据集对所提方法进行了定量评估，结果表明该方法的召回率为95.24%，精确率为97.64%，优于先前的方法。
