Intelligentization of Maglev Transportation Systems Driven by Critical Embodied Intelligence: Recent Advances, Applications, and Future Challenges

XU Junqi; LI Fengkai; CHEN Chen; SUN Yougang; RONG Lijun

doi:10.3969/j.issn.0258-2724.20260003

Article Contents

Journal of Southwest Jiaotong University > 2026 > Accepted Manuscript

XU Junqi, LI Fengkai, CHEN Chen, SUN Yougang, RONG Lijun. Intelligentization of Maglev Transportation Systems Driven by Critical Embodied Intelligence: Recent Advances, Applications, and Future Challenges[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20260003

Citation:

XU Junqi, LI Fengkai, CHEN Chen, SUN Yougang, RONG Lijun. Intelligentization of Maglev Transportation Systems Driven by Critical Embodied Intelligence: Recent Advances, Applications, and Future Challenges[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20260003

Citation:

XU Junqi, LI Fengkai, CHEN Chen, SUN Yougang, RONG Lijun. Intelligentization of Maglev Transportation Systems Driven by Critical Embodied Intelligence: Recent Advances, Applications, and Future Challenges[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20260003

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Intelligentization of Maglev Transportation Systems Driven by Critical Embodied Intelligence: Recent Advances, Applications, and Future Challenges

doi: 10.3969/j.issn.0258-2724.20260003

XU Junqi^{1, 2, 3
,},
LI Fengkai^{1, 3},
CHEN Chen^{1, 2},
SUN Yougang^{1, 2},
RONG Lijun^{1, 2
,
,}

1.
College of Transportation Engineering, Tongji University, Shanghai 201804, China
2.
National Maglev Transportation Engineering R & D Center, Tongji University, Shanghai 201804, China
3.
State Key Laboratory of High-speed Maglev Transportation Technology, Tongji University, Shanghai 201804, China

Received Date: 04 Jan 2026
Rev Recd Date: 20 Apr 2026

Available Online: 02 Jun 2026

Abstract

Abstract

Maglev transportation systems have become an important development direction for future rail transit by virtue of their advantages such as non-contact operation, high speed, and low noise; however, their complex operating environments, high safety requirements, and high operation and maintenance costs put forward higher requirements for the autonomous perception, decision-making, and control capabilities of the systems. In this context, critical embodied intelligence, as an important paradigm of next-generation artificial intelligence, emphasizes the real-time interaction and continuous learning between intelligent agents (such as onboard intelligent control agents, trackside sensing agents, and robots) and the physical environment, promotes the deep integration of perception, decision-making, and control, and provides a new technical pathway for the intelligent development of maglev transportation systems. In specific applications, critical embodied intelligence firstly empowers operational safety and the precise control of levitation and guidance. By fusing multimodal sensor data to dynamically construct the vehicle-track-environment situation, it achieves millimeter-level perception, state prediction, and the autonomous generation of levitation-guidance and traction-braking strategies, ensuring stability and comfort under extreme working conditions; secondly, in the field of autonomous infrastructure inspection and intelligent maintenance, robots or unmanned aerial vehicles driven by embodied intelligence can replace manual labor to execute high-risk tasks, identify defects through interactive detection, predict component lifespans based on experience, and optimize maintenance cycles; finally, in terms of global scheduling and collaborative optimization, multiple embodied intelligent agents constitute a distributed system, and by sharing local perception information, they dynamically negotiate and adjust timetables to achieve high-density, collaborative, and energy-efficiency optimized operations of train groups. In summary, critical embodied intelligence is driving the transformation of maglev transportation systems from “automated execution” to “autonomous evolution”. While enhancing safety, operational efficiency, and system resilience, it helps to reduce full life-cycle costs and is a key pathway to constructing the next-generation adaptive and evolvable intelligent rail transit. Future research should focus on the high-reliability validation of intelligent agents, multi-agent collaboration mechanisms, and robustness enhancement in complex environments to accelerate their engineering application process.

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References(177)

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Intelligentization of Maglev Transportation Systems Driven by Critical Embodied Intelligence: Recent Advances, Applications, and Future Challenges

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Intelligentization of Maglev Transportation Systems Driven by Critical Embodied Intelligence: Recent Advances, Applications, and Future Challenges

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