Scene-Adaptive Driving Area Prediction Based on Automatic Label Acquisition from Driving Information

Takuya Migishima, Haruya Kyutoku, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Abstract: Technology for autonomous vehicles has attracted much attention for reducing traffic accidents, and the demand for its realization is increasing year-by-year. For safety driving on urban roads by an autonomous vehicle, it is indispensable to predict an appropriate driving path even if various objects exist in the environment. For predicting the appropriate driving path, it is necessary to recognize the surrounding environment. Semantic segmentation is widely studied as one of the surrounding environment recognition methods and has been utilized for drivable area prediction. However, the driver’s operation, that is important for predicting the preferred drivable area (scene-adaptive driving area), is not considered in these methods. In addition, it is important to consider the movement of surrounding dynamic objects for predicting the scene-adaptive driving area. In this paper, we propose an automatic label assignment method from actual driving information, and scene-adaptive driving area prediction method using semantic segmentation and Convolutional LSTM (Long Short-Term Memory). Experiments on actual driving information demonstrate that the proposed methods could both acquire the labels automatically and predict the scene-adaptive driving area successfully.