Intention-aware reachability prediction for dynamic collision avoidance in uncertain traffic
Zhou, Jian ; Zhou, Qidong ; Yu, Pian ; Carrizosa-Rendon, Alvaro ; Gao, Yulong ; Olofsson, Björn LU
and Frisk, Erik
(2026)
In Transportation Research Part C: Emerging Technologies
192.
p.1-20
- Abstract
- Safe motion planning for autonomous driving systems in uncertain environments remains a significant challenge. The inherent traffic uncertainties caused by both the uncertain control and behavioral intentions of dynamic surrounding vehicles (SVs) are difficult to anticipate, hindering accurate motion prediction and complicating safe decision-making for the autonomous ego vehicle (EV). To mitigate these challenges, this paper proposes an efficient and safe motion-planning strategy that integrates intention awareness into the uncertainty prediction of SVs. The uncertainty prediction is performed analytically online through forward reachability analysis and is further improved by capturing both the control intentions and behavioral intentions... (More)
- Safe motion planning for autonomous driving systems in uncertain environments remains a significant challenge. The inherent traffic uncertainties caused by both the uncertain control and behavioral intentions of dynamic surrounding vehicles (SVs) are difficult to anticipate, hindering accurate motion prediction and complicating safe decision-making for the autonomous ego vehicle (EV). To mitigate these challenges, this paper proposes an efficient and safe motion-planning strategy that integrates intention awareness into the uncertainty prediction of SVs. The uncertainty prediction is performed analytically online through forward reachability analysis and is further improved by capturing both the control intentions and behavioral intentions of SVs to support the motion-planning process. The effectiveness of the method is demonstrated in various challenging scenarios, including planning tasks in encounter scenarios, multi-vehicle distributed planning problems, and a multi-vehicle case study based on a recorded real-world traffic dataset. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2509f1ec-6fb0-44b6-807f-1ce02aa1951b
- author
- Zhou, Jian
; Zhou, Qidong
; Yu, Pian
; Carrizosa-Rendon, Alvaro
; Gao, Yulong
; Olofsson, Björn
LU
and Frisk, Erik
- organization
- publishing date
- 2026-11
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Transportation Research Part C: Emerging Technologies
- volume
- 192
- article number
- 105883
- pages
- 20 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:105044996778
- ISSN
- 0968-090X
- DOI
- 10.1016/j.trc.2026.105883
- project
- ELLIIT B14: Autonomous Force-Aware Swift Motion Control
- RobotLab LTH
- language
- English
- LU publication?
- yes
- id
- 2509f1ec-6fb0-44b6-807f-1ce02aa1951b
- date added to LUP
- 2026-08-04 11:23:30
- date last changed
- 2026-09-15 05:36:56
@article{2509f1ec-6fb0-44b6-807f-1ce02aa1951b,
abstract = {{Safe motion planning for autonomous driving systems in uncertain environments remains a significant challenge. The inherent traffic uncertainties caused by both the uncertain control and behavioral intentions of dynamic surrounding vehicles (SVs) are difficult to anticipate, hindering accurate motion prediction and complicating safe decision-making for the autonomous ego vehicle (EV). To mitigate these challenges, this paper proposes an efficient and safe motion-planning strategy that integrates intention awareness into the uncertainty prediction of SVs. The uncertainty prediction is performed analytically online through forward reachability analysis and is further improved by capturing both the control intentions and behavioral intentions of SVs to support the motion-planning process. The effectiveness of the method is demonstrated in various challenging scenarios, including planning tasks in encounter scenarios, multi-vehicle distributed planning problems, and a multi-vehicle case study based on a recorded real-world traffic dataset.}},
author = {{Zhou, Jian and Zhou, Qidong and Yu, Pian and Carrizosa-Rendon, Alvaro and Gao, Yulong and Olofsson, Björn and Frisk, Erik}},
issn = {{0968-090X}},
language = {{eng}},
pages = {{1--20}},
publisher = {{Elsevier}},
series = {{Transportation Research Part C: Emerging Technologies}},
title = {{Intention-aware reachability prediction for dynamic collision avoidance in uncertain traffic}},
url = {{http://dx.doi.org/10.1016/j.trc.2026.105883}},
doi = {{10.1016/j.trc.2026.105883}},
volume = {{192}},
year = {{2026}},
}