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On Integrating POMDP and Scenario MPC for Planning under Uncertainty - with Applications to Highway Driving
- Publication Year :
- 2022
-
Abstract
- Motion planning and decision-making while considering uncertainty is critical for an autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new combined two-step approach for this problem, where a partially observable Markov decision process (POMDP) is tightly coupled with a scenario model predictive control (SCMPC) step. To generate the scenarios in the SCMPC step, the solution to the POMDP is used together with a novel scenario-reduction procedure, which selects a small representative subset of all scenarios considered in the POMDP. The resulting planner is evaluated in a simulation study where the impact of the two-step approach and the scenario-reduction method is shown.<br />Funding Agencies|Swedens Innovation Agency
Details
- Database :
- OAIster
- Notes :
- Hynén, Carl, Axehill, Daniel
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1387013625
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1109.IV51971.2022.9827005