Journal
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
Volume 15, Issue 6, Pages -Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/1729881418817162
Keywords
Car-following; inverse reinforcement learning (IRL); autonomous vehicle; decision-making; automatic driving
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Funding
- Junior Fellowships for Advanced Innovation Think-tank Program of China Association for Science and Technology [DXB-ZKQN-2017-035]
- China Postdoctoral Science Foundation [2017M620765]
- China Postdoctoral Science Foundation Special Foundation [2018T110095]
- National Key Research and Development Program of China [2017YFB0102603]
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There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment.
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