Safe, learning-based MPC for highway driving under Lane-change uncertainty: a distributionally robust approach
From MaRDI portal
Publication:6103663
DOI10.1016/j.artint.2023.103920arXiv2206.13319OpenAlexW4366169357MaRDI QIDQ6103663
Unnamed Author, Christopher Meissen, Panagiotis Patrinos, Alexander Katriniok, H. Eric Tseng
Publication date: 27 June 2023
Published in: Artificial Intelligence (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2206.13319
path planningmodel predictive controlrisk measuresdistributionally robust optimizationautomated driving
Related Items (1)
Cites Work
- Unnamed Item
- Automated driving: the role of forecasts and uncertainty -- a control perspective
- Risk-averse model predictive control
- CasADi: a software framework for nonlinear optimization and optimal control
- Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
- On the Lambert \(w\) function
- On safe tractable approximations of chance constraints
- On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
- Multistage Stochastic Optimization
- Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems
- Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics
- Lectures on Stochastic Programming
- The minimax approach to stochastic programming and an illustrative application
- Wasserstein Distributionally Robust Stochastic Control: A Data-Driven Approach
- A Nonlinear Model Predictive Control Framework Using Reference Generic Terminal Ingredients
- Vehicle Dynamics and Control
- A General Framework for Learning-Based Distributionally Robust MPC of Markov Jump Systems
- Safe, learning-based MPC for highway driving under Lane-change uncertainty: a distributionally robust approach
This page was built for publication: Safe, learning-based MPC for highway driving under Lane-change uncertainty: a distributionally robust approach