Hierarchical reinforcement learning for self‐driving decision‐making without reliance on labelled driving data

Hierarchical reinforcement learning for self‐driving decision‐making without reliance on labelled driving data

Decision making for self‐driving cars is usually tackled by manually encoding rules from drivers’ behaviours or imitating drivers’ manipulation using supervised learning techniques. Both of them rely on mass driving data to cover all possible driving scenarios. This study presents a hierarchical rei...

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Bibliographic Details
Main Authors: Jingliang Duan, Shengbo Eben Li, Yang Guan, Qi Sun, Bo Cheng
Format: Article
Language:English
Published: Wiley 2020-05-01
Series:IET Intelligent Transport Systems
Subjects:
self‐driving cars
labelled driving data
high‐level manoeuvre selection
low‐level motion control
asynchronous parallel reinforcement learners
driving decisions
Online Access:https://doi.org/10.1049/iet-its.2019.0317

Internet

https://doi.org/10.1049/iet-its.2019.0317

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