Citation

Gao J, Yi J, Zhu H, Murphey YL. SAE Int. J. Transp. Safety 2019; 7(2): 09-07-02-0009.

Copyright

(Copyright © 2019, SAE International)

DOI

10.4271/09-07-02-0009

PMID

unavailable

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.

Language: en