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# [MPDM2: Multipolicy Decision-Making for Autonomous Driving via Changepoint-based Behavior Prediction](https://www.roboticsproceedings.org/rss11/p43.pdf) | ||
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_June 2024_ | ||
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tl;dr: Improvement of MPDM in predicting the intention of other vehicles. | ||
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#### Overall impression | ||
The majority is the same as the previous work [MPDM](mpdm.md). | ||
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For the policy tree (or policy-conditioned scenario tree) building, we can see how the tree got built with more and more careful pruning process with improvements from different works. | ||
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* [MPDM](mpdm.md) iterates over all ego policies, and uses the most likely one policy given road structure and pose of vehicle. | ||
* [MPDM2](mpdm2.md) iterates over all ego policies, and iterate over (a set of) possible policies of other agents predicted by a motion prediction model. | ||
* [EUDM](eudm.md) itrates all ego policies, and then iterate over all possible policies of other agents to identify **critical scenarios** (CFB, conditioned filtered branching). [EPSILON](epsilon.md) used the same method. | ||
* [MARC](marc.md) iterates all ego policies, iterates over a set of predicted policies of other agents, identifies **key agents** (and ignores other agents even in critical scenarios). | ||
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![](https://pic3.zhimg.com/80/v2-a7778368cbf39f083ef5ad5a2f931a4e_1440w.webp) | ||
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#### Key ideas | ||
- Motion prediction of other agents with a classical ML methods (Maximum likelihood estimation). | ||
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#### Technical details | ||
- Summary of technical details, such as important training details, or bugs of previous benchmarks. | ||
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#### Notes | ||
- Questions and notes on how to improve/revise the current work | ||
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