Mapping road safety features from streetview imagery: A deep learning approach
This work addresses the need for accurate and efficient mapping of road safety features for transportation agencies, though it is incremental as it builds on existing CNNs by adding LSTMs.
The paper tackles the problem of manually mapping road safety features from streetview imagery by proposing a deep learning approach that combines CNNs with RNNs (LSTMs) to capture geographic context, resulting in a model that outperforms baseline methods.
Each year, around 6 million car accidents occur in the U.S. on average. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety features is an important component of safety management systems for federal or state transportation agencies, helping traffic engineers identify locations to invest on safety infrastructure. In current practice, mapping road safety features is largely done manually (e.g., observations on the road or visual interpretation of streetview imagery), which is both expensive and time consuming. In this paper, we propose a deep learning approach to automatically map road safety features from streetview imagery. Unlike existing Convolutional Neural Networks (CNNs) that classify each image individually, we propose to further add Recurrent Neural Network (Long Short Term Memory) to capture geographic context of images (spatial autocorrelation effect along linear road network paths). Evaluations on real world streetview imagery show that our proposed model outperforms several baseline methods.