TMPF: A Two‐Stage Merging Planning Framework for Dense Traffic
Planning for autonomous vehicles to merge into high‐density traffic flows within limited mileage is quite challenging. Specifically, the driving trajectory will inevitably have intersections with other vehicles whose driving intentions can't be directly observed. Herein, a two‐stage algorithm framework that is decomposed into the longitudinal and lateral planning processes for online merging planning is proposed. An improved particle filter is used to estimate the driving models of surrounding vehicles for predicting their future driving intentions. Based on Monte Carlo tree search (MCTS), different action spaces are evaluated for longitudinal merging gap selection and lateral interactive merging operation, while heuristic pruning is used to reduce the computation cost. Moreover, the coefficients related to the driving styles are introduced, and their influences on merging performance are analyzed. Finally, the proposed algorithm is implemented in a two‐lane simulation environment. The results show that the proposal has outperformed other baseline methods.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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TMPF: A Two‐Stage Merging Planning Framework for Dense Traffic ; day:12 ; month:05 ; year:2023 ; extent:13
Advanced intelligent systems ; (12.05.2023) (gesamt 13)
- Urheber
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Chen, Ci
Yong, Chenghao
Guo, Xuexun
Pei, Xiaofei
- DOI
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10.1002/aisy.202300081
- URN
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urn:nbn:de:101:1-2023051315201362482488
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:48 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Chen, Ci
- Yong, Chenghao
- Guo, Xuexun
- Pei, Xiaofei
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