An Integrated Model for Autonomous Speed and Lane Change Decision-Making Based on Deep Reinforcement Learning
Author:
Affiliation:
1. School of Transportation, Southeast University, Nanjing, China
2. Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI, USA
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
‘Zhishan’ Scholars Programs of Southeast University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Science Applications,Mechanical Engineering,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/6979/9942712/09843863.pdf?arnumber=9843863
Reference46 articles.
1. Prediction of Lane Change Trajectories through Neural Network
2. Enhanced intelligent driver model for two-dimensional motion planning in mixed traffic
3. The Effect of Lane-Change Maneuvers on a Simplified Car-Following Theory
4. Categorization of the lane change decision process on freeways
5. Lane-changing in traffic streams
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