Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation

Collision-free space detection is a critical component of autonomous vehicle perception. The state-of-the-art algorithms are typically based on supervised learning. The performance of such approaches is always dependent on the quality and amount of labeled training data. Additionally, it remains an open challenge to train deep convolutional neural networks (DCNNs) using only a small quantity of training samples. Therefore, this paper mainly explores an effective training data augmentation approach that can be employed to improve the overall DCNN performance, when additional images captured from different views are available. Due to the fact that the pixels of the collision-free space (generally regarded as a planar surface) between two images captured from different views can be associated by a homography matrix, the scenario of the target image can be transformed into the reference view. This provides a simple but effective way of generating training data from additional multi-view images. Extensive experimental results, conducted with six state-of-the-art semantic segmentation DCNNs on three datasets, demonstrate the effectiveness of our proposed training data augmentation algorithm for enhancing collision-free space detection performance. When validated on the KITTI road benchmark, our approach provides the best results for stereo vision-based collision-free space detection.Comment: accepted to IEEE/ASME Transactions on Mechatronic

Mitochondrial dysfunction in pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is characterized by the increased pulmonary vascular resistance due to pulmonary vasoconstriction and vascular remodeling. PAH has high disability, high mortality and poor prognosis, which is becoming a more common global health issue. There is currently no drug that can permanently cure PAH patients. The pathogenesis of PAH is still not fully elucidated. However, the role of metabolic theory in the pathogenesis of PAH is becoming clearer, especially mitochondrial metabolism. With the deepening of mitochondrial researches in recent years, more and more studies have shown that the occurrence and development of PAH are closely related to mitochondrial dysfunction, including the tricarboxylic acid cycle, redox homeostasis, enhanced glycolysis, and increased reactive oxygen species production, calcium dysregulation, mitophagy, etc. This review will further elucidate the relationship between mitochondrial metabolism and pulmonary vasoconstriction and pulmonary vascular remodeling. It might be possible to explore more comprehensive and specific treatment strategies for PAH by understanding these mitochondrial metabolic mechanisms