Lane detection or road detection is one of the key features of autonomous driving. In computer vision area, it is still a very challenging target since there are various types of road scenarios which require a very high robustness of the algorithm. And considering the rather high speed of the vehicles, high efficiency is also a very important requirement for practicable application of autonomous driving. In this paper, we propose a deep convolution neural network based lane detection method, which consider the lane detection task as a pixel level segmentation of the lane markings. We also propose an automatic training data generating method, which can significantly reduce the effort of the training phase. Experiment proves that our method can achieve high accuracy for various road scenes in real-time.

Automatic labeling of prosodic features is an important topic when constructing large speech databases for speech synthesis or analysis purposes. Perceptually-related F0 parameters are proposed with the aim of automatically classifying phrase final tones. Analyses are conducted to verify how consistently subjects are able to categorize phrase final tones, and how perceptual features are related with the categories. Three types of acoustic parameters are proposed and analyzed for representing the perceptual features related to the tone categories: one related to pitch movement within the phrase final, one related to pitch reset prior to the phrase final, and one related to the length of the phrase final. A classification tree is constructed to evaluate automatic classification of phrase final tones, resulting in 79.2% accuracy for the consistently categorized samples, using the best combination among the proposed acoustic parameters.