Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making
2018 ◽
Vol 15
(6)
◽
pp. 172988141881716
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Keyword(s):
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.
2019 ◽
Vol 16
(3)
◽
pp. 172988141985318
2019 ◽
Vol 33
◽
pp. 7749-7758
2021 ◽
Vol 20
(02)
◽
pp. 2150011
2021 ◽
pp. 095440702110630
Keyword(s):
2019 ◽
Vol 46
(3)
◽
pp. 444-452
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