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A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling

  • Journal Article
  • 09-07-02-0009
  • ISSN: 2327-5626, e-ISSN: 2327-5634
Published November 14, 2019 by SAE International in United States
A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling
Sector:
Citation: Gao, J., Yi, J., Zhu, H., and Murphey, Y., "A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling," SAE Int. J. Trans. Safety 7(2):163-174, 2019.
Language: English

Abstract:

Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components.
  • Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data.
  • Secondly, the LCM has an ability of learning the global spatial-temporal information via Temporal Modeling Blocks (TMBs).
  • Finally, a two-layer Long Short-Term Memory (LSTM) network is used to learn video contextual features combined with lane boundary based distance features in lane change events.
The experimental results on a -world driving dataset show that the LCM is capable of learning the latent features of lane-changing behaviors and achieving significantly better performance than other prevalent models.