{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T09:09:31Z","timestamp":1767949771725,"version":"3.49.0"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2024QN11004"],"award-info":[{"award-number":["2024QN11004"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The intelligent scheduling of autonomous trucks at intersections in open-pit coal mines poses a fundamental challenge due to nonlinear dynamics and complex multi-truck interactions under gradient\u2013load coupling. Unlike existing intersection control studies that are mainly designed for flat urban roads and neglect mining-specific physical constraints, this study proposes a Gradient-Load Aware Multi-Agent Attention-Enhanced DDPG (GLA-MA-MADDPG) approach that explicitly embeds mining-domain physical laws into multi-agent reinforcement learning for intersection scheduling. A gradient\u2013load dynamic coupling model is developed to inject physics fidelity into MARL-based intersection scheduling, capturing nonlinear and load-sensitive kinematics overlooked by flat-road assumptions in prior studies. To handle coordination under these constraints, we design a task-oriented multi-dimensional attention mechanism that jointly interprets physical heterogeneity, asymmetric dynamics, priorities, and collision risks. Additionally, a gradient-aware adaptive priority strategy redefines right-of-way as a physics-grounded, state-dependent process, ensuring safe and preferential passage for loaded and downhill trucks. In the simulated four-branch gradient intersection environment, the proposed GLA-MA-MADDPG method achieves substantial performance gains over traditional MADDPG across 20 independent runs. Specifically, it improves throughput by 23.5%, reduces average transit time by 8.3%, decreases waiting time by 31.2%, and lowers the collision rate from 3.47\u2030 to 1.32%, achieving efficiency gains of 39.2% in mixed uphill\u2013downhill scenarios. Overall, this study contributes a first-of-its-kind integration of physics-informed gradient\u2013load modeling with attention-driven MARL, providing a generalizable computational framework for intelligent intersection scheduling in open-pit mining and other large-scale, safety-critical engineering systems.<\/jats:p>","DOI":"10.1093\/jcde\/qwaf140","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T12:52:56Z","timestamp":1766494376000},"page":"352-370","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent scheduling control for coordinated passing of multiple autonomous trucks at intersections in open-pit coal mines via enhanced multi-agent reinforcement learning"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3924-2210","authenticated-orcid":false,"given":"Boyu","family":"Luan","sequence":"first","affiliation":[{"name":"School of Mines, China University of Mining and Technology , Xuzhou 221116 ,","place":["China"]},{"name":"State Key Laboratory for Fine Exploration and intelligent Development of Coal Resources, China University of Mining and Technology , Xuzhou 221116 ,","place":["China"]},{"name":"School of Information and Control Engineering, China University of Mining and Technology , Xuzhou 221116 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2994-918X","authenticated-orcid":false,"given":"Yufeng","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Mines, China University of Mining and Technology , Xuzhou 221116 ,","place":["China"]},{"name":"State Key Laboratory for Fine Exploration and intelligent Development of Coal Resources, China University of Mining and Technology , Xuzhou 221116 ,","place":["China"]},{"name":"School of Information and Control Engineering, China University of Mining and Technology , Xuzhou 221116 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8758-2511","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Fine Exploration and intelligent Development of Coal Resources, China University of Mining and Technology , Xuzhou 221116 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4649-6080","authenticated-orcid":false,"given":"Hairong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mines, China University of Mining and Technology , Xuzhou 221116 ,","place":["China"]},{"name":"State Key Laboratory for Fine Exploration and intelligent Development of Coal Resources, China University of Mining and Technology , Xuzhou 221116 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1633-4446","authenticated-orcid":false,"given":"Xiang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mines, China University of Mining and Technology , Xuzhou 221116 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5968-9102","authenticated-orcid":false,"given":"Zhihui","family":"Han","sequence":"additional","affiliation":[{"name":"Shandong Yankuang Design Consulting Co. 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