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Black Ice Detecting Using CNN for the Prevention of Accidents In 2020 International Conference on Computational Science and Computational Intelligence (CSCI) Black ice detection using CNN for the Prevention of Accidents in Automated Vehicle HoJun Lee MinHee Kang JaeIn Song dept. Urban Design & Planning dept. Smartcity Research Institute of Science and Hongik University Hongik University Technology Seoul, Korea Seoul, Korea Hongik University [email protected] [email protected] Seoul, Korea [email protected] Keeyeon Hwang dept.Urban Design & Planning Hongik University Seoul, Korea [email protected] Abstract— Black ice is recognized as the main cause of major of AVs, it is expected that technologies that can detect it in accidents in winter because it has characteristics that are advance and prevent accidents will be required. In this study, difficult to identify with the naked eye. This is expected to be a to prevent black ice accidents in AVs in the future, we potential cause of accidents in the era of automated vehicles as suggests method to detect black ice on the road using well. Accordingly, this study presents a CNN-based black ice Convolutional Neural Networks. detection plan to prevent traffic accidents caused by black ice. Due to the characteristic of black ice that is formed only in a This study is conducted in the following order: Chapter 2 certain environment, the data was augmented and the image of discusses the research on the use of CNN in the field of road environment in various environments was learned. Test transportation and derives the differentiation of this research, results show that the proposed CNN model detected black ice while Chapter 3 sets up the CNN model learning environment with 96% accuracy and reproducibility(recall). for the detection of black ice. Chapter 4 identifies and analyzes learning results through models, and Chapter 5 presents Keywords—Automated Vehicle, Blackice, CNN, Traffic implications and future studies with a brief summary. Accident, Prevention II. LIITERATURE REVIEW I. INTRODUCTION In this chapter, we will consider existing detection As discussions on the 4th industrial revolution become methods for black ice and studies using CNN in the more active, there is a movement to utilize big data, artificial transportation sector to derive the differentiation of this study. intelligence, and 5G. Among them, Automated Vehicles(AVs), a collection of various technologies, are A. Blackice Detection Methods attracting attention in the transportation field. AVs are Habib Tabatabai et al. (2017)[9] conducted a study to expected to bring effects such as improving mobility for the detect black ice, ice and water in roads and bridges using vulnerable and reducing traffic congestion costs, and are sensors embedded in concrete. In this study, a sensor that expected to minimize human and material losses in terms of detects the road surface condition was proposed through the preventing traffic accidents caused by driver negligence [1-2]. change of electrical resistance between stainless steel columns Currently, various companies such as Google, NVIDIA, and inside concrete. As a result of conducting experiments under Tesla are developing and experimenting with AV systems, various surface conditions, it was suggested that the proposed and each country is reorganizing its institutional foundation to sensor can effectively detect the road condition, thereby prepare for the commercialization of AVs. Despite these preventing various accidents. efforts, however, traffic accidents continue to occur in Youngis E. Abdalla et al (2017) (2017) [10] proposed a autonomous driving situations, and the social acceptability of system for detecting black ice using Kinect. The types of ice AVs has emerged due to Uber's pedestrian deaths in 2018 [3- (Soft Ice, Wet Snow, Hard Ice, Black Ice) were classified and 5]. In order to solve these problems fundamentally, Germany the thickness and volume of the ice were measured using and the United States have issued an ethics guideline for AVs Kinect. Experiments have shown that the types of ice formed [6-7]. The guidelines specify the need to develop principles to in the range of 0.82m to 1.52m from the camera can be cope with dilemma situations, along with information on the distinguished, and the error rate of measured thickness and preventive design of the AVs to avoid accidents. Preventive volume is very low, suggesting that black ice can be detected design of AVs is an issue about risk management that can by utilizing Kinect. occur in a realistic driving environment, changing from passive safety systems to active safety systems research [8]. Xinxu Ma et al. (2020)[11] studied a black ice detection While various preventive design studies are being carried out, method using a 3-wavelength non-contact optical technology. there is a lack of research on preventing black ice accidents, The study conducted an experiment to distinguish dry, wet, which are the main causes of large-scale traffic accidents in black ice, ice and snowy conditions using 3 wavelengths winter. Black ice is a thin ice film formed on the road by (1310nm, 1430nm, 1550nm). As a result of the experiment, it combining rain and snow with pollutants such as dust, which was confirmed that black ice was detected through the is likely to lead to fatal accidents because it is difficult to reflectance of each wavelength, and it was suggested that it identify the naked eye. As Black Ice is considered to be a can be used as basic data for the development of equipment to potential accident factor even in the era of commercialization detect road conditions. 978-1-7281-7624-6/20/$31.00 ©2020 IEEE 1189 DOI 10.1109/CSCI51800.2020.00222 B. The Utilization of CNN in Transportation C. Summary Lele Xie et al (2018) (2018) [12] proposed an ALMD- In summary, it was confirmed that various studies using YOLO structure to conduct various angles of vehicle license black ice detection research and CNN in the transportation plate detection studies utilizing CNN-based Multi-Directive field are in progress. The black ice detection study proposed a YOLO (MD-YOLO). Comparing performance with various method to detect black ice using a sensor, a detection system, models (ALMD-YOLO, Faster R-CNN, SSD, MD-YOLO, and optical technology. In addition, in the CNN-related study etc.) was found to have the best performance of the proposed in the transportation field, a study was conducted to detect the ALMD-YOLO. This suggested that the simple structure of the most important objects constituting the road environment such model would shorten the computational time, thereby creating as pedestrians, vehicles, traffic signs, and road cracks. a high-performance, multi-way license plate detection model. In spite of such as studies, it is expected that there is a limit Hongbo Gao et al (2018) [13] conducted a study using to preventing black ice accidents in advance due to problems LIDAR and CNN to detect objects in autonomous driving such as the installation of a black ice detection system. situations. The above study collected data with LIDARs Accordingly, this study proposes a method to detect black ice mounted on self-driving cars and used AlexNet to detect five by identifying road conditions based on CNN technique to classes of objects (Pedestrian, Cyclist, Car, Truck and Other). prevent black ice accidents in autonomous vehicles. Experiments have shown near 100% accuracy in most classes except Truck (88.6%). The small number of classes was III. LERANING ENVIRONMENT SETTING pointed out as a limitation, but it was confirmed that CNN- A. Data Collecting & Pre-Process based object detection was possible using LIDAR only. Data used for learning were classified into a total of four Yung Yao Chen et al (2019) [14] conducted a pedestrian (blackice, road, wet road, snow road) and obtained through detection study using the heat detection system and the Faster Google image search. After that, it was processed into a size R-CNN. For data collection, thermal imaging cameras were of 128×128 px so that the characteristics of the road could be used instead of ordinary optical cameras, and analysis was clearly identified. Next, we converted to GRAYSCALE (1 made using the ResNet structure. Experiments have shown channel) for image data learning and proceeded with data better performance than conventional CNN and confirmed padding. Results of processing and learning by data padding that night pedestrian detection is also possible using thermal are shown in Table 1, and image distortion of original data is imaging cameras and Faster R-CNN. confirmed during processing. In addition, experimental Alexander Shustanov, & Yakimov, P. (2017)[15] verification has shown that the loss value (loss) of padding conducted a CNN model design study for real-time traffic sign data is lower than the original data, and a high accuracy has recognition. The above study used modified Generalized been calculated and all data has been padded. Hough Transform (GHT) and CNN, with 99.94% accuracy. It was also confirmed that the proposed algorithm could process TABLE I. RESULT OF AUGMENTATION AND LEARNING high-definition images in real time and accurately recognize Original Data Padding Data traffic signs farther away than similar traffic sign recognition systems. Result of Augmentation Young-Jin Cha and two others (2017) [16] conducted a concrete crack detection study based on CNN. Data was acquired in various situations, and the CNN model was Learning Loss 1.39 0.26 constructed by placing Convolution layer and Max-pooling Result Accuracy 0.253 0.891 layer alternately. 98.22% of the accuracy was obtained as a Through this process, 25,400 images of 150×150 px-sized result of learning, and based on this, cracks in test data were image data were obtained in the form of GRAYSCALE. detected using sliding window technique. The above study has implications for designing models that show high TABLE II.
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