In order to achieve the goal of autonomous driving, a precise perception of the vehicle’s environment is required. In particular, the weather-related road condition has a major influence on vehicle dynamics and thus on driving safety.
In this paper, we compare Deep Convolutional Neural Networks of different computational effort, namely Inception-v3, GoogLeNet and the much smaller SqueezeNet, for classification of road surface and its weather-related condition. Previously, different regions of interest were compared in order to provide the networks with optimal input data.
Classification of Road Surface and Weather-Related Condition Using Deep Convolutional Neural Networks
Lect.Notes Mechanical Engineering
The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks ; 2019 ; Gothenburg, Sweden August 12, 2019 - August 16, 2019
2020-02-13
10 pages
Article/Chapter (Book)
Electronic Resource
English
Automated Truck Taxonomy Classification Using Deep Convolutional Neural Networks
Springer Verlag | 2022
|