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Uncertainty-Aware Decision-Making and Planning for Autonomous Forced Merging

Zhou, Jian ; Gao, Yulong ; Olofsson, Björn LU and Frisk, Erik (2024) 63rd IEEE Conference on Decision and Control, CDC 2024 In Proceedings of the IEEE Conference on Decision and Control p.1095-1102
Abstract

In this paper, we develop an uncertainty-aware decision-making and motion-planning method for an autonomous ego vehicle in forced merging scenarios, considering the motion uncertainty of surrounding vehicles. The method dynamically captures the uncertainty of surrounding vehicles by online estimation of their acceleration bounds, enabling a reactive but rapid understanding of the uncertainty characteristics of the surrounding vehicles. By leveraging these estimated bounds, a non-conservative forward occupancy of surrounding vehicles is predicted over a horizon, which is incorporated in both the decision-making process and the motion-planning strategy, to enhance the resilience and safety of the planned reference trajectory. The method... (More)

In this paper, we develop an uncertainty-aware decision-making and motion-planning method for an autonomous ego vehicle in forced merging scenarios, considering the motion uncertainty of surrounding vehicles. The method dynamically captures the uncertainty of surrounding vehicles by online estimation of their acceleration bounds, enabling a reactive but rapid understanding of the uncertainty characteristics of the surrounding vehicles. By leveraging these estimated bounds, a non-conservative forward occupancy of surrounding vehicles is predicted over a horizon, which is incorporated in both the decision-making process and the motion-planning strategy, to enhance the resilience and safety of the planned reference trajectory. The method successfully fulfills the tasks in challenging forced merging scenarios, and the properties are illustrated by comparison with several alternative approaches.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2024 IEEE 63rd Conference on Decision and Control, CDC 2024
series title
Proceedings of the IEEE Conference on Decision and Control
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
63rd IEEE Conference on Decision and Control, CDC 2024
conference location
Milan, Italy
conference dates
2024-12-16 - 2024-12-19
external identifiers
  • scopus:86000668151
ISSN
2576-2370
0743-1546
ISBN
9798350316339
DOI
10.1109/CDC56724.2024.10886137
project
ELLIIT B14: Autonomous Force-Aware Swift Motion Control
RobotLab LTH
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 IEEE.
id
3549dcd5-4770-4320-916f-8a51ff069f77
alternative location
https://arxiv.org/abs/2410.20514
date added to LUP
2025-04-08 16:59:54
date last changed
2025-07-16 00:40:03
@inproceedings{3549dcd5-4770-4320-916f-8a51ff069f77,
  abstract     = {{<p>In this paper, we develop an uncertainty-aware decision-making and motion-planning method for an autonomous ego vehicle in forced merging scenarios, considering the motion uncertainty of surrounding vehicles. The method dynamically captures the uncertainty of surrounding vehicles by online estimation of their acceleration bounds, enabling a reactive but rapid understanding of the uncertainty characteristics of the surrounding vehicles. By leveraging these estimated bounds, a non-conservative forward occupancy of surrounding vehicles is predicted over a horizon, which is incorporated in both the decision-making process and the motion-planning strategy, to enhance the resilience and safety of the planned reference trajectory. The method successfully fulfills the tasks in challenging forced merging scenarios, and the properties are illustrated by comparison with several alternative approaches.</p>}},
  author       = {{Zhou, Jian and Gao, Yulong and Olofsson, Björn and Frisk, Erik}},
  booktitle    = {{2024 IEEE 63rd Conference on Decision and Control, CDC 2024}},
  isbn         = {{9798350316339}},
  issn         = {{2576-2370}},
  language     = {{eng}},
  pages        = {{1095--1102}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Proceedings of the IEEE Conference on Decision and Control}},
  title        = {{Uncertainty-Aware Decision-Making and Planning for Autonomous Forced Merging}},
  url          = {{http://dx.doi.org/10.1109/CDC56724.2024.10886137}},
  doi          = {{10.1109/CDC56724.2024.10886137}},
  year         = {{2024}},
}