Open Access
Adaptive Maneuver Planning for Autonomous Vehicles Using Behavior Tree on Apollo Platform
Mais Jamal
1
,
Aleksandr Panov
1, 2
Publication type: Book Chapter
Publication date: 2021-12-06
scimago Q2
SJR: 0.352
CiteScore: 2.4
Impact factor: —
ISSN: 03029743, 16113349, 18612075, 18612083
Abstract
In safety-critical systems such as autonomous driving systems, behavior planning is a significant challenge. The presence of numerous dynamic obstacles makes the driving environment unpredictable. The planning algorithm should be safe, reactive, and adaptable to environmental changes. The paper presents an adaptive maneuver planning algorithm based on an evolving behavior tree created with genetic programming. In addition, we make a technical contribution to the Baidu Apollo autonomous driving platform, allowing the platform to test and develop overtaking maneuver planning algorithms.
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7
Total citations:
7
Citations from 2024:
4
(57%)
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Jamal M., Panov A. Adaptive Maneuver Planning for Autonomous Vehicles Using Behavior Tree on Apollo Platform // Lecture Notes in Computer Science. 2021. Vol. 13101 LNAI. pp. 327-340.
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Jamal M., Panov A. Adaptive Maneuver Planning for Autonomous Vehicles Using Behavior Tree on Apollo Platform // Lecture Notes in Computer Science. 2021. Vol. 13101 LNAI. pp. 327-340.
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TY - GENERIC
DO - 10.1007/978-3-030-91100-3_26
UR - https://doi.org/10.1007/978-3-030-91100-3_26
TI - Adaptive Maneuver Planning for Autonomous Vehicles Using Behavior Tree on Apollo Platform
T2 - Lecture Notes in Computer Science
AU - Jamal, Mais
AU - Panov, Aleksandr
PY - 2021
DA - 2021/12/06
PB - Springer Nature
SP - 327-340
VL - 13101 LNAI
SN - 0302-9743
SN - 1611-3349
SN - 1861-2075
SN - 1861-2083
ER -
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BibTex (up to 50 authors)
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@incollection{2021_Jamal,
author = {Mais Jamal and Aleksandr Panov},
title = {Adaptive Maneuver Planning for Autonomous Vehicles Using Behavior Tree on Apollo Platform},
publisher = {Springer Nature},
year = {2021},
volume = {13101 LNAI},
pages = {327--340},
month = {dec}
}
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