Motion planning for intersection turning of heavy-duty autonomous vehicles using vehicle body trajectory prediction and risk potential evaluation
The shortage of drivers for trucks and buses has become a critical problem in recent years, leading to active research and development on unmanned and manpower-saving operation of heavy-duty vehicles. However, due to their larger overhangs compared to passenger cars, heavy-duty vehicles are more dif...
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Format: | Article |
Language: | Japanese |
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The Japan Society of Mechanical Engineers
2023-10-01
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Series: | Nihon Kikai Gakkai ronbunshu |
Subjects: | |
Online Access: | https://www.jstage.jst.go.jp/article/transjsme/89/926/89_23-00139/_pdf/-char/en |
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author | Yutaka HAMAGUCHI Yo SANOGAWA Pongsathorn RAKSINCHAROENSAK |
author_facet | Yutaka HAMAGUCHI Yo SANOGAWA Pongsathorn RAKSINCHAROENSAK |
author_sort | Yutaka HAMAGUCHI |
collection | DOAJ |
description | The shortage of drivers for trucks and buses has become a critical problem in recent years, leading to active research and development on unmanned and manpower-saving operation of heavy-duty vehicles. However, due to their larger overhangs compared to passenger cars, heavy-duty vehicles are more difficult to maneuver on narrow roads and sharp curves, especially those commonly found in urban areas or parking lots. Consequently, path tracking control systems commonly applied to passenger cars cannot be directly used for heavy-duty vehicles. Thus, the objective of this study is to develop a path planning and motion control system for autonomous driving of heavy-duty vehicles on roads with large curvatures. To design the control system, a risk potential is utilized to express the driver's sense of danger towards obstacles and road boundaries. Simulations are conducted on a course that replicates an actual intersection, and a parameter study is carried out. Finally, the simulation results of this approach are compared with those of a conventional path-following method. The results demonstrate that the proposed method is effective for obstacle avoidance in intersection situations. |
first_indexed | 2024-03-11T15:49:40Z |
format | Article |
id | doaj.art-1ea28efbe6514e788d30ae0eedeb8db3 |
institution | Directory Open Access Journal |
issn | 2187-9761 |
language | Japanese |
last_indexed | 2024-03-11T15:49:40Z |
publishDate | 2023-10-01 |
publisher | The Japan Society of Mechanical Engineers |
record_format | Article |
series | Nihon Kikai Gakkai ronbunshu |
spelling | doaj.art-1ea28efbe6514e788d30ae0eedeb8db32023-10-26T04:51:05ZjpnThe Japan Society of Mechanical EngineersNihon Kikai Gakkai ronbunshu2187-97612023-10-018992623-0013923-0013910.1299/transjsme.23-00139transjsmeMotion planning for intersection turning of heavy-duty autonomous vehicles using vehicle body trajectory prediction and risk potential evaluationYutaka HAMAGUCHI0Yo SANOGAWA1Pongsathorn RAKSINCHAROENSAK2Hino Motors, Ltd.Department of Mechanical Systems Engineering, Tokyo University of Agriculture and TechnologyDepartment of Mechanical Systems Engineering, Tokyo University of Agriculture and TechnologyThe shortage of drivers for trucks and buses has become a critical problem in recent years, leading to active research and development on unmanned and manpower-saving operation of heavy-duty vehicles. However, due to their larger overhangs compared to passenger cars, heavy-duty vehicles are more difficult to maneuver on narrow roads and sharp curves, especially those commonly found in urban areas or parking lots. Consequently, path tracking control systems commonly applied to passenger cars cannot be directly used for heavy-duty vehicles. Thus, the objective of this study is to develop a path planning and motion control system for autonomous driving of heavy-duty vehicles on roads with large curvatures. To design the control system, a risk potential is utilized to express the driver's sense of danger towards obstacles and road boundaries. Simulations are conducted on a course that replicates an actual intersection, and a parameter study is carried out. Finally, the simulation results of this approach are compared with those of a conventional path-following method. The results demonstrate that the proposed method is effective for obstacle avoidance in intersection situations.https://www.jstage.jst.go.jp/article/transjsme/89/926/89_23-00139/_pdf/-char/enheavy-duty vehiclesautonomous drivingmotion planningintersectionrisk potential |
spellingShingle | Yutaka HAMAGUCHI Yo SANOGAWA Pongsathorn RAKSINCHAROENSAK Motion planning for intersection turning of heavy-duty autonomous vehicles using vehicle body trajectory prediction and risk potential evaluation Nihon Kikai Gakkai ronbunshu heavy-duty vehicles autonomous driving motion planning intersection risk potential |
title | Motion planning for intersection turning of heavy-duty autonomous vehicles using vehicle body trajectory prediction and risk potential evaluation |
title_full | Motion planning for intersection turning of heavy-duty autonomous vehicles using vehicle body trajectory prediction and risk potential evaluation |
title_fullStr | Motion planning for intersection turning of heavy-duty autonomous vehicles using vehicle body trajectory prediction and risk potential evaluation |
title_full_unstemmed | Motion planning for intersection turning of heavy-duty autonomous vehicles using vehicle body trajectory prediction and risk potential evaluation |
title_short | Motion planning for intersection turning of heavy-duty autonomous vehicles using vehicle body trajectory prediction and risk potential evaluation |
title_sort | motion planning for intersection turning of heavy duty autonomous vehicles using vehicle body trajectory prediction and risk potential evaluation |
topic | heavy-duty vehicles autonomous driving motion planning intersection risk potential |
url | https://www.jstage.jst.go.jp/article/transjsme/89/926/89_23-00139/_pdf/-char/en |
work_keys_str_mv | AT yutakahamaguchi motionplanningforintersectionturningofheavydutyautonomousvehiclesusingvehiclebodytrajectorypredictionandriskpotentialevaluation AT yosanogawa motionplanningforintersectionturningofheavydutyautonomousvehiclesusingvehiclebodytrajectorypredictionandriskpotentialevaluation AT pongsathornraksincharoensak motionplanningforintersectionturningofheavydutyautonomousvehiclesusingvehiclebodytrajectorypredictionandriskpotentialevaluation |