Experimental validation of lane-change intention prediction methodologies based on CNN and LSTM

• Published in: 2019 IEEE Intelligent Transportation Systems Conference (ITSC) pp. 3657 - 3662
• Main Authors: Izquierdo, R., Quintanar, A., Parra, I., Fernandez-Llorca, D., Sotelo, M. A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2019
• Subjects: Cameras; Data mining; History; Labeling; Trajectory; Visualization
• DOI: 10.1109/ITSC.2019.8917331

This paper describes preliminary results of two different methodologies used to predict lane changes of surrounding vehicles. These methodologies are deep learning based and the training procedure can be easily deployed by making use of the labeling and data provided by The PREVENTION dataset. In this case, only visual information (data collected from the cameras) is used for both methodologies. On the one hand, visual information is processed using a new multi-channel representation of the temporal information which is provided to a CNN model. On the other hand, a CNN-LSTM ensemble is also used to integrate temporal features. In both cases, the idea is to encode local and global context features as well as temporal information as the input of a CNN-based approach to perform lane change intention prediction. Preliminary results showed that the dataset proved to be highly versatile to deal with different vehicle intention prediction approaches.