@article{ref1,
title="A personalized lane-changing model for advanced driver assistance system based on deep learning and spatial-temporal modeling",
journal="SAE International journal of transportation safety",
year="2019",
author="Gao, Jun and Yi, Jiangang and Zhu, Honghui and Murphey, Yi Lu",
volume="7",
number="2",
pages="09-07",
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.<p /> <p>Language: en</p>",
language="en",
issn="2327-5626",
doi="10.4271/09-07-02-0009",
url="http://dx.doi.org/10.4271/09-07-02-0009"
}