Speed and steering angle prediction for intelligent vehicles based on deep belief network

Learning and predicting human driving behavior plays an important role in the development of advanced driving assistance systems (ADAS). Speed and steering angle which reflect the longitudinal and lateral behavior of drivers are two important parameters for behavior prediction. However, traditional behavior learning methods, especially the methods based on artificial neural networks rely on the human-selected features, and thus have poor adaptability to the highly changeable traffic environment. This paper aims to overcome this drawback by using deep learning which can learn features automatically from the driving data without human interventions. Specifically, the deep belief network (DBN) is used to build the learning model, and the training data are collected from drivers driving on the real-world road. Based on the model, the steering angle of the front wheel and the speed of vehicle are predicted. The prediction results show that, compared with the traditional learning method, DBN has a higher accuracy and can adapt to different driving scenarios with much less modifications.