{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T17:53:33Z","timestamp":1787162013956,"version":"build-2736575974"},"reference-count":161,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Autonomous driving technology has garnered significant attention due to its potential to revolutionize transportation through advanced robotic systems. Despite optimistic projections for commercial deployment, the development of sophisticated autonomous driving systems remains largely experimental, with the effectiveness of neurorobotics-based decision-making and planning algorithms being crucial for success. This paper delivers a comprehensive review of decision-making and planning algorithms in autonomous driving, covering both knowledge-driven and data-driven approaches. For knowledge-driven methods, this paper explores independent decision-making systems, including rule based, state transition based, game-theory based methods and independent planing systems including search based, sampling based, and optimization based methods. For data-driven methods, it provides a detailed analysis of machine learning paradigms such as imitation learning, reinforcement learning, and inverse reinforcement learning. Furthermore, the paper discusses hybrid models that amalgamate the strengths of both data-driven and knowledge-driven approaches, offering insights into their implementation and challenges. By evaluating experimental platforms, this paper guides the selection of appropriate testing and validation strategies. Through comparative analysis, this paper elucidates the advantages and disadvantages of each method, facilitating the design of more robust autonomous driving systems. Finally, this paper addresses current challenges and offers a perspective on future developments in this rapidly evolving field.<\/jats:p>","DOI":"10.3389\/fnbot.2025.1451923","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T01:53:19Z","timestamp":1739843599000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["A survey of decision-making and planning methods for self-driving vehicles"],"prefix":"10.3389","volume":"19","author":[{"given":"Jun","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuefeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningjia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingjie","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuotao","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hualin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaolu","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,2,18]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1109\/ITSC.2019.8917192","article-title":"\u201cAutomated lane change decision making using deep reinforcement learning in dynamic and uncertain highway environment,\u201d","volume-title":"Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC)","author":"Alizadeh","year":"2019"},{"key":"B2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/VNC57357.2023.10136277","article-title":"\u201cSuperdriverai: towards design and implementation for end-to-end learning-based autonomous driving,\u201d","volume-title":"Proceedings of the 2023 IEEE Vehicular Networking Conference (VNC)","author":"Aoki","year":"2023"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2019.XV.031","article-title":"Chauffeurnet: learning to drive by imitating the best and synthesizing the worst","author":"Bansal","year":"2018","journal-title":"arXiv"},{"key":"B4","first-page":"36","article-title":"\u201cOnline pomdp planning with anytime deterministic guarantees,\u201d","volume-title":"Advances in Neural Information Processing Systems","author":"Barenboim","year":"2024"},{"key":"B5","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/ICRA.2017.7989025","article-title":"\u201cFind your own way: weakly-supervised segmentation of path proposals for urban autonomy,\u201d","volume-title":"Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA)","author":"Barnes","year":"2017"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1109\/TITS.2017.2725912","article-title":"Expert level control of ramp metering based on multi-task deep reinforcement learning","volume":"19","author":"Belletti","year":"2017","journal-title":"IEEE Trans. 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