Resource Efficient Classification of Road Conditions through CNN Pruning
Daniel Fink, Alexander Busch, Mark Wielitzka, Tobias Ortmaier
Abstract
Towards autonomous driving, advanced driver assistance systems increasingly undertake basic driving tasks by replacing human assessment and interactions, when controlling the vehicle. The performance of these systems is directly related to knowledge of the vehicle’s state and influential parameters. In this respect, the road condition has a major influence on the tires’ traction and thus significantly affects the behavior of the vehicle. Therefore, a prediction of the upcoming road condition can improve the performance of the assistance systems which leads to an increased driving safety and comfort. The presented work aims to classify the road surface as well as its weather-related condition, based on images of the front camera view, using deep convolutional neural networks. In order to take computational limitations of vehicle control units into account, a pruning approach is investigated to reduce the network complexity.