The Detection of Black Ice Accidents Using CNNs
As automated vehicles become more popular it is critical for these cars to be tested on every realistic driving scenario. Since AVs aim to improve safety on the road they must be able to handle all kinds of road conditions. One way an AV can prevent an accident is going from a passive safety system to an active safety system once a risk is identified.
Every country has their own challenges and in Canada for example, AVs need to understand how to drive in the winter. However, not enough testing and training has been done to mitigate winter risks. Black ice is one of the leading causes of accidents in the winter and is very challenging to see since it is a thin, transparent layer of ice. Because of this, focus needs to be placed on AVs identifying black ice.
In the past other methods of detecting black ice included using:
- Electric current sensors imbedded in concrete
- Change of electrical current resistance between stainless steel columns inside the concrete based on how what is on top of the road
- Sound Waves:
- Used 3 different soundwaves
- Road conditions detected through reflectance of the waves
- To be used for basic data in the development of road condition detectors
- Light Sources
- Different road conditions have unique light reflection
- Specular and diffuse reflections
- Types of ice were classified based on thickness and volume
- Other road conditions could be determined through reflection as well
Transportation in general has been using artificial intelligence for many different purposes.
Vehicle and pedestrian detection has been using various forms of convolutional neural networks like AlexNet, YOLO, R-CNN, Faster R-CNN, etc. Some models had better performance whereas others had a faster processing time but overall great success has been achieved.
In addition, the identification of traffic signs has had studies using similar CNN structures. These algorithms are able to process high-definition images quickly and recognize the boundary of the traffic sign allowing for quick processing.
Lastly, the detection of cracks in the road used CNN algorithms to identify the existence of a crack and classifying the it’s length with a maximum misclassification of 1cm.
Significant progress has been made for transportation but there is a lack of training on winter roads and black ice specifically. Since CNN has great success with quickly identifying objects of interest in images, using CNN for black ice detection and accident prevention is a natural extension.
CNN is a popular class of Artificial Neural Networks (ANN) that is commonly used in image analysis due to its excellent performance in object detection using images.It differs from ANN in that it maintains and delivers spatial information on images by adding synthetic and pooling layers to a normal ANN. As mentioned earlier, various studies regarding the transportation sector had used CNN, but the study of black ice detection on the road has only thus far been conducted using other methodologies (sensors and optics). This study aims to detect black ice by utilizing CNN on images of various road conditions.. In this chapter, the details of data collection, 1st preprocessing, and 2nd preprocessing, how the model was designed, and the training undertaken (see Figure 1) are discussed.
1. Data Collection
Image data was collected using Google Image Search for four categories of road condition: road, wet road, snow road and black ice. Images were of different regions and road environments and make up a total of 2230 images.
2. Data Split
To assist in feature extraction, objects such as road structures, lanes, and shoulders within each image were removed so that the road characteristics of interest can be clearly identified. Consideration was given in the decision of the image size by weighing the pros and cons. In general, making images smaller will cause a loss of information. However, smaller image sizes allow for a larger number of images and deep neural network implementations. On the other hand, when the image size is large, feature extraction can be more accurate as the finer features are not lost, and the network can learn more robust features, but the disadvantage is that the number of images is reduced, and a deep neural network is difficult to implement. In this study, a 128 x 128 px size is selected to proceed with training. The results of the data split are shown in Figure 2.