The Detection of Black Ice Accidents Using CNNs

Introduction

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.

Previous Work

In the past other methods of detecting black ice included using:

• Sensors 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 <\ul> 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.

  Motivation

  Model Architecture

  Conclusion

  Critiques

  References