Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving

The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation a...
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Autor*in:	Bing Lu [verfasserIn] Hongwen He [verfasserIn] Huilong Yu [verfasserIn] Hong Wang [verfasserIn] Guofa Li [verfasserIn] Man Shi [verfasserIn] Dongpu Cao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	potential field sigmoid curve path planning autonomous vehicles

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 20(2020), 24, p 7197
Übergeordnetes Werk:	volume:20 ; year:2020 ; number:24, p 7197

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s20247197

Katalog-ID:	DOAJ031011489

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520			\|a The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving.
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allfields	10.3390/s20247197 doi (DE-627)DOAJ031011489 (DE-599)DOAJ58b60482968043d1aa39c4093a9941bd DE-627 ger DE-627 rakwb eng TP1-1185 Bing Lu verfasserin aut Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving. potential field sigmoid curve path planning autonomous vehicles Chemical technology Hongwen He verfasserin aut Huilong Yu verfasserin aut Hong Wang verfasserin aut Guofa Li verfasserin aut Man Shi verfasserin aut Dongpu Cao verfasserin aut In Sensors MDPI AG, 2003 20(2020), 24, p 7197 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:24, p 7197 https://doi.org/10.3390/s20247197 kostenfrei https://doaj.org/article/58b60482968043d1aa39c4093a9941bd kostenfrei https://www.mdpi.com/1424-8220/20/24/7197 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 24, p 7197
spelling	10.3390/s20247197 doi (DE-627)DOAJ031011489 (DE-599)DOAJ58b60482968043d1aa39c4093a9941bd DE-627 ger DE-627 rakwb eng TP1-1185 Bing Lu verfasserin aut Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving. potential field sigmoid curve path planning autonomous vehicles Chemical technology Hongwen He verfasserin aut Huilong Yu verfasserin aut Hong Wang verfasserin aut Guofa Li verfasserin aut Man Shi verfasserin aut Dongpu Cao verfasserin aut In Sensors MDPI AG, 2003 20(2020), 24, p 7197 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:24, p 7197 https://doi.org/10.3390/s20247197 kostenfrei https://doaj.org/article/58b60482968043d1aa39c4093a9941bd kostenfrei https://www.mdpi.com/1424-8220/20/24/7197 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 24, p 7197
allfields_unstemmed	10.3390/s20247197 doi (DE-627)DOAJ031011489 (DE-599)DOAJ58b60482968043d1aa39c4093a9941bd DE-627 ger DE-627 rakwb eng TP1-1185 Bing Lu verfasserin aut Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving. potential field sigmoid curve path planning autonomous vehicles Chemical technology Hongwen He verfasserin aut Huilong Yu verfasserin aut Hong Wang verfasserin aut Guofa Li verfasserin aut Man Shi verfasserin aut Dongpu Cao verfasserin aut In Sensors MDPI AG, 2003 20(2020), 24, p 7197 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:24, p 7197 https://doi.org/10.3390/s20247197 kostenfrei https://doaj.org/article/58b60482968043d1aa39c4093a9941bd kostenfrei https://www.mdpi.com/1424-8220/20/24/7197 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 24, p 7197
allfieldsGer	10.3390/s20247197 doi (DE-627)DOAJ031011489 (DE-599)DOAJ58b60482968043d1aa39c4093a9941bd DE-627 ger DE-627 rakwb eng TP1-1185 Bing Lu verfasserin aut Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving. potential field sigmoid curve path planning autonomous vehicles Chemical technology Hongwen He verfasserin aut Huilong Yu verfasserin aut Hong Wang verfasserin aut Guofa Li verfasserin aut Man Shi verfasserin aut Dongpu Cao verfasserin aut In Sensors MDPI AG, 2003 20(2020), 24, p 7197 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:24, p 7197 https://doi.org/10.3390/s20247197 kostenfrei https://doaj.org/article/58b60482968043d1aa39c4093a9941bd kostenfrei https://www.mdpi.com/1424-8220/20/24/7197 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 24, p 7197
allfieldsSound	10.3390/s20247197 doi (DE-627)DOAJ031011489 (DE-599)DOAJ58b60482968043d1aa39c4093a9941bd DE-627 ger DE-627 rakwb eng TP1-1185 Bing Lu verfasserin aut Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving. potential field sigmoid curve path planning autonomous vehicles Chemical technology Hongwen He verfasserin aut Huilong Yu verfasserin aut Hong Wang verfasserin aut Guofa Li verfasserin aut Man Shi verfasserin aut Dongpu Cao verfasserin aut In Sensors MDPI AG, 2003 20(2020), 24, p 7197 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:24, p 7197 https://doi.org/10.3390/s20247197 kostenfrei https://doaj.org/article/58b60482968043d1aa39c4093a9941bd kostenfrei https://www.mdpi.com/1424-8220/20/24/7197 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 24, p 7197
language	English
source	In Sensors 20(2020), 24, p 7197 volume:20 year:2020 number:24, p 7197
sourceStr	In Sensors 20(2020), 24, p 7197 volume:20 year:2020 number:24, p 7197
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	potential field sigmoid curve path planning autonomous vehicles Chemical technology
isfreeaccess_bool	true
container_title	Sensors
authorswithroles_txt_mv	Bing Lu @@aut@@ Hongwen He @@aut@@ Huilong Yu @@aut@@ Hong Wang @@aut@@ Guofa Li @@aut@@ Man Shi @@aut@@ Dongpu Cao @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Bing Lu
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topic_title	TP1-1185 Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving potential field sigmoid curve path planning autonomous vehicles
topic	misc TP1-1185 misc potential field misc sigmoid curve misc path planning misc autonomous vehicles misc Chemical technology
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title	Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving
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title_full	Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving
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title_sort	hybrid path planning combining potential field with sigmoid curve for autonomous driving
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title_auth	Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving
abstract	The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving.
abstractGer	The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving.
abstract_unstemmed	The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving.
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title_short	Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving
url	https://doi.org/10.3390/s20247197 https://doaj.org/article/58b60482968043d1aa39c4093a9941bd https://www.mdpi.com/1424-8220/20/24/7197 https://doaj.org/toc/1424-8220
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