Weakly-supervised Road Condition Classification Using Automatically Generated Labels

Predicting the condition of the road is an important task for autonomous vehicles to make driving decisions. Vehicles are expected to slow down or stop for potential road risks such as road cracks, bumps and potholes. Vision systems are widely used to provide such information given the rich colours and textures carried by images. This paper presents a weakly-supervised deep learning method to classify road images into two category sets. The first category identifies the existence of bumps or ramps in the image. The second category determines the road roughness given an input image. These two outputs are combined into a single convolutional neural network (CNN) to classify the camera image simultaneously. As a supervised learning method, deep learning algorithms normally require a large amount of training data with manually annotated labels. The annotation process is, however, very time-consuming and labour-intensive. This paper presents a method to avoid this costly process using a pipeline to automatically generate ground-truth labels by incorporating IMU and wheel encoder data. This automated pipeline does not require human effort to label images and will not be impeded by adverse environmental or illumination conditions. The experimental results presented show that after training the model using the automatically generated labels, the two-output CNN is capable to achieve good accuracy for classifying road conditions.