{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:52:44Z","timestamp":1784645564827,"version":"3.55.0"},"reference-count":32,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52102463"],"award-info":[{"award-number":["52102463"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20210231"],"award-info":[{"award-number":["20210231"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation of State Key Laboratory of Automotive Simulation and Control","award":["52102463"],"award-info":[{"award-number":["52102463"]}]},{"name":"Foundation of State Key Laboratory of Automotive Simulation and Control","award":["20210231"],"award-info":[{"award-number":["20210231"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s forward motion. This generates an initial obstacle avoidance path that satisfies the vehicle\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s23229180","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T09:46:13Z","timestamp":1699955173000},"page":"9180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3016-5853","authenticated-orcid":false,"given":"Ke","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honglin","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Fu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanzheng","family":"Wen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binyu","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,14]]},"reference":[{"key":"ref_1","first-page":"1135","article-title":"A review of motion planning techniques for automated vehicles","volume":"17","author":"Nashashibi","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.trc.2015.09.011","article-title":"Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions","volume":"60","author":"Katrakazas","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15729","DOI":"10.1109\/TITS.2022.3145389","article-title":"Autonomous driving on curvy roads without reliance on frenet frame: A cartesian-based trajectory planning method","volume":"23","author":"Li","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19761","DOI":"10.1109\/ACCESS.2021.3053169","article-title":"A new algorithm based on Dijkstra for vehicle path planning considering intersection attribute","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1177\/01423312211046410","article-title":"A new path planning method based on sparse A* algorithm with map segmentation","volume":"44","author":"Zhaoying","year":"2022","journal-title":"Trans. Inst. Meas. Control"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1109\/LRA.2020.2969191","article-title":"An efficient sampling-based method for online informative path planning in unknown environments","volume":"5","author":"Schmid","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_7","first-page":"97","article-title":"Optimal path planning using RRT* based approaches: A survey and future directions","volume":"7","author":"Noreen","year":"2016","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.robot.2018.11.022","article-title":"Anticipatory kinodynamic motion planner for computing the best path and velocity trajectory in autonomous driving","volume":"114","author":"Talamino","year":"2019","journal-title":"Robot. Auton. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Benko Loknar, M., Klan\u010dar, G., and Bla\u017ei\u010d, S. (2023). Minimum-Time Trajectory Generation for Wheeled Mobile Systems Using B\u00e9zier Curves with Constraints on Velocity, Acceleration and Jerk. Sensors, 23.","DOI":"10.3390\/s23041982"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1049\/iet-its.2020.0048","article-title":"Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi-speed scheduler","volume":"14","author":"Wahid","year":"2020","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1109\/TIE.2019.2898599","article-title":"A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach","volume":"67","author":"Huang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","first-page":"1","article-title":"Overtaking Path Planning for CAV based on Improved Artificial Potential Field","volume":"9","author":"Ma","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TIV.2020.3045837","article-title":"MPC-based cooperative control strategy of path planning and trajectory tracking for intelligent vehicles","volume":"6","author":"Zuo","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pan, R., Jie, L., Zhao, X., Wang, H., Yang, J., and Song, J. (2023). Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario. Sensors, 23.","DOI":"10.3390\/s23063248"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106960","DOI":"10.1016\/j.asoc.2020.106960","article-title":"An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve","volume":"100","author":"Song","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/TIV.2020.2991951","article-title":"Improved path planning by tightly combining lattice-based path planning and optimal control","volume":"6","author":"Bergman","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wei, K., and Ren, B. (2018). A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm. Sensors, 18.","DOI":"10.3390\/s18020571"},{"key":"ref_18","unstructured":"Yang, G., Cai, M., Ahmad, A., Belta, C., and Tron, R. (2023). Efficient LQR-CBF-RRT*: Safe and Optimal Motion Planning. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1109\/TRO.2006.870668","article-title":"Modified Newton\u2019s method applied to potential field-based navigation for mobile robots","volume":"22","author":"Ren","year":"2006","journal-title":"IEEE Trans. Robot."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1017\/S0263574707003694","article-title":"Modified Newton\u2019s method applied to potential field-based navigation for nonholonomic robots in dynamic environments","volume":"26","author":"Ren","year":"2008","journal-title":"Robotica"},{"key":"ref_21","first-page":"90","article-title":"Real-time obstacle avoidance for manipulators and mobile robots","volume":"5","author":"Khatib","year":"1986","journal-title":"Ind. Robot."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xia, X., Li, T., Sang, S., Cheng, Y., Ma, H., Zhang, Q., and Yang, K. (2023). Path Planning for Obstacle Avoidance of Robot Arm Based on Improved Potential Field Method. Sensors, 23.","DOI":"10.3390\/s23073754"},{"key":"ref_23","first-page":"808","article-title":"A research on local path planning for autonomous vehicles based on improved APF method","volume":"9","author":"Caijing","year":"2013","journal-title":"Qiche Gongcheng"},{"key":"ref_24","first-page":"1451","article-title":"Simulation on the path planning of intelligent vehicles based on artificial potential field algorithm","volume":"39","author":"An","year":"2017","journal-title":"Qiche Gongcheng"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1109\/TVT.2016.2555853","article-title":"Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints","volume":"66","author":"Ji","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_26","first-page":"877","article-title":"Human vehicle steering collision avoidance path planning based on pedestrian location prediction","volume":"43","author":"Li","year":"2021","journal-title":"Qiche Gongcheng"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1109\/TITS.2015.2401837","article-title":"The driving safety field based on driver\u2013vehicle\u2013road interactions","volume":"16","author":"Wang","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.trc.2016.10.003","article-title":"Driving safety field theory modeling and its application in pre-collision warning system","volume":"72","author":"Wang","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_29","first-page":"79","article-title":"Dynamic lane-changing trajectory planning model for intelligent vehicle based on quadratic programming","volume":"34","author":"Wang","year":"2021","journal-title":"China J. Highw. Transp."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TMECH.2015.2493980","article-title":"Real-time trajectory planning for autonomous urban driving: Framework, algorithms, and verifications","volume":"21","author":"Li","year":"2015","journal-title":"IEEE-ASME Trans. Mechatron."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/LRA.2020.3045925","article-title":"Autonomous driving trajectory optimization with dual-loop iterative anchoring path smoothing and piecewise-jerk speed optimization","volume":"6","author":"Zhou","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3416","DOI":"10.1177\/09544070211014319","article-title":"Collision avoidance method of autonomous vehicle based on improved artificial potential field algorithm","volume":"235","author":"Feng","year":"2021","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. 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