Multi-scenario Learning MPC for Automated Driving in Unknown and Changing Environments-Reference-Cited by-同舟云学术

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Multi-scenario Learning MPC for Automated Driving in Unknown and Changing Environments

Published:2023-07-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 21st International Conference on Industrial Informatics (INDIN)
language:
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Author:

Yue Yu¹,Wang Zhenpo²,Liu Jianhong³,Li Guoqaing²

Affiliation:

1. Beijing BitNei Corp., Ltd School of Mechanical Engineering Beijing Institute of Technology,Beijing,China

2. Beijing Institute of Technology,School of Mechanical Engineering,Beijing,China

3. Intelligent Hardware R&D Beijing Bitnei Corp., Ltd,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10217218/10217836/10218123.pdf?arnumber=10218123

Reference21 articles.

1. Conditioning sparse variational Gaussian processes for online decision-making;maddox;Neural Information Processing Systems,2021

2. Tracking Multiple Vehicles Using a Variational Radar Model

3. Meta Learning With Paired Forward and Inverse Models for Efficient Receding Horizon Control

4. Variational Measurement Update for Extended Object Tracking Using Gaussian Processes

5. Leveraging experience for robust, adaptive nonlinear MPC on computationally constrained systems with time-varying state uncertainty

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