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A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field

Establishing an accurate and computationally efficient model for driving risk assessment, considering the influence of vehicle motion state and kinematic characteristics on path planning, is crucial for generating safe, comfortable, and easily trackable obstacle avoidance paths. To address this topi...

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Autores principales: Liu, Ke, Wang, Honglin, Fu, Yao, Wen, Guanzheng, Wang, Binyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675226/
https://www.ncbi.nlm.nih.gov/pubmed/38005565
http://dx.doi.org/10.3390/s23229180
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author Liu, Ke
Wang, Honglin
Fu, Yao
Wen, Guanzheng
Wang, Binyu
author_facet Liu, Ke
Wang, Honglin
Fu, Yao
Wen, Guanzheng
Wang, Binyu
author_sort Liu, Ke
collection PubMed
description Establishing an accurate and computationally efficient model for driving risk assessment, considering the influence of vehicle motion state and kinematic characteristics on path planning, is crucial for generating safe, comfortable, and easily trackable obstacle avoidance paths. To address this topic, this paper proposes a novel dual-layered dynamic path-planning method for obstacle avoidance based on the driving safety field (DSF). The contributions of the proposed approach lie in its ability to address the challenges of accurately modeling driving risk, efficient path smoothing and adaptability to vehicle kinematic characteristics, and providing collision-free, curvature-continuous, and adaptable obstacle avoidance paths. In the upper layer, a comprehensive driving safety field is constructed, composed of a potential field generated by static obstacles, a kinetic field generated by dynamic obstacles, a potential field generated by lane boundaries, and a driving field generated by the target position. By analyzing the virtual field forces exerted on the ego vehicle within the comprehensive driving safety field, the resultant force direction is utilized as guidance for the vehicle’s forward motion. This generates an initial obstacle avoidance path that satisfies the vehicle’s kinematic and dynamic constraints. In the lower layer, the problem of path smoothing is transformed into a standard quadratic programming (QP) form. By optimizing discrete waypoints and fitting polynomial curves, a curvature-continuous and smooth path is obtained. Simulation results demonstrate that our proposed path-planning algorithm outperforms the method based on the improved artificial potential field (APF). It not only generates collision-free and curvature-continuous paths but also significantly reduces parameters such as path curvature (reduced by 62.29% to 87.32%), curvature variation rate, and heading angle (reduced by 34.11% to 72.06%). Furthermore, our algorithm dynamically adjusts the starting position of the obstacle avoidance maneuver based on the vehicle’s motion state. As the relative velocity between the ego vehicle and the obstacle vehicle increases, the starting position of the obstacle avoidance path is adjusted accordingly, enabling the proactive avoidance of stationary or moving single and multiple obstacles. The proposed method satisfies the requirements of obstacle avoidance safety, comfort, and stability for intelligent vehicles in complex environments.
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spelling pubmed-106752262023-11-14 A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field Liu, Ke Wang, Honglin Fu, Yao Wen, Guanzheng Wang, Binyu Sensors (Basel) Article Establishing an accurate and computationally efficient model for driving risk assessment, considering the influence of vehicle motion state and kinematic characteristics on path planning, is crucial for generating safe, comfortable, and easily trackable obstacle avoidance paths. To address this topic, this paper proposes a novel dual-layered dynamic path-planning method for obstacle avoidance based on the driving safety field (DSF). The contributions of the proposed approach lie in its ability to address the challenges of accurately modeling driving risk, efficient path smoothing and adaptability to vehicle kinematic characteristics, and providing collision-free, curvature-continuous, and adaptable obstacle avoidance paths. In the upper layer, a comprehensive driving safety field is constructed, composed of a potential field generated by static obstacles, a kinetic field generated by dynamic obstacles, a potential field generated by lane boundaries, and a driving field generated by the target position. By analyzing the virtual field forces exerted on the ego vehicle within the comprehensive driving safety field, the resultant force direction is utilized as guidance for the vehicle’s forward motion. This generates an initial obstacle avoidance path that satisfies the vehicle’s kinematic and dynamic constraints. In the lower layer, the problem of path smoothing is transformed into a standard quadratic programming (QP) form. By optimizing discrete waypoints and fitting polynomial curves, a curvature-continuous and smooth path is obtained. Simulation results demonstrate that our proposed path-planning algorithm outperforms the method based on the improved artificial potential field (APF). It not only generates collision-free and curvature-continuous paths but also significantly reduces parameters such as path curvature (reduced by 62.29% to 87.32%), curvature variation rate, and heading angle (reduced by 34.11% to 72.06%). Furthermore, our algorithm dynamically adjusts the starting position of the obstacle avoidance maneuver based on the vehicle’s motion state. As the relative velocity between the ego vehicle and the obstacle vehicle increases, the starting position of the obstacle avoidance path is adjusted accordingly, enabling the proactive avoidance of stationary or moving single and multiple obstacles. The proposed method satisfies the requirements of obstacle avoidance safety, comfort, and stability for intelligent vehicles in complex environments. MDPI 2023-11-14 /pmc/articles/PMC10675226/ /pubmed/38005565 http://dx.doi.org/10.3390/s23229180 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Ke
Wang, Honglin
Fu, Yao
Wen, Guanzheng
Wang, Binyu
A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field
title A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field
title_full A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field
title_fullStr A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field
title_full_unstemmed A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field
title_short A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field
title_sort dynamic path-planning method for obstacle avoidance based on the driving safety field
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675226/
https://www.ncbi.nlm.nih.gov/pubmed/38005565
http://dx.doi.org/10.3390/s23229180
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