列车自动驾驶动态运行控制技术研究进展

doi:10.3901/JME.260730

机械工程学报 ›› 2026, Vol. 62 ›› Issue (16): 228-252.doi: 10.3901/JME.260730

• 特邀专辑：轨道运载装备动态设计理论与运维技术 • 上一篇

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列车自动驾驶动态运行控制技术研究进展

凌亮¹, 李涛涛¹, 高峰², 李宏伟³, 陈哲⁴, 刘建新¹, 侯冶⁵, 王希云⁶, 翟婉明¹

1. 西南交通大学轨道交通运载系统全国重点实验室成都 610031;
2. 中国铁道科学研究院集团有限公司动车组和机车牵引与控制国家重点实验室北京 100081;
3. 中车长春轨道客车股份有限公司长春 130011;
4. 中车株洲电力机车有限公司株洲 412001;
5. 国能朔黄铁路发展有限责任公司肃宁 062350;
6. 国能包神铁路集团有限责任公司包头 014000

收稿日期:2025-08-17 修回日期:2026-02-06 发布日期:2026-10-14
作者简介:凌亮(通信作者)，男，1986年出生，博士，教授，博士研究生导师。主要研究方向为列车与运行环境动态相互作用理论、铁路轮轨系统动力学与智能运维、轨道车辆服役安全与智能控制。E-mail：liangling@swjtu.edu.cn
基金资助:
国家重点研发计划青年科学家(2025YFF0524300)和国家自然科学基金(U2268210，52388102)和动车组和机车牵引与控制国家重点实验室开放课题基金(2024YJ075)资助项目。

Advances in Dynamic Control Technology for Train Autonomous Operation

LING Liang¹, LI Taotao¹, GAO Feng², LI Hongwei³, CHEN Zhe⁴, LIU Jianxin¹, HOU Ye⁵, WANG Xiyun⁶, ZHAI Wanming¹

1. State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031;
2. State Key Locomotive for Traction and Control System of EMU and Locomotive, China Academy of Railway Sciences Corporation Limited, Beijing 100081;
3. CRRC Changchun Railway Vehicle Corporation, Changchun 130011;
4. CRRC Zhuzhou Electric Locomotive Co., Ltd., Zhuzhou 412001;
5. CHN Energy Shuohuang Railway Development Co., Ltd., Suning 062350;
6. CHN Energy Baoshen Railway Co., Ltd., Baotou 014000

Received:2025-08-17 Revised:2026-02-06 Published:2026-10-14

摘要/Abstract

摘要： 针对在轨道交通领域得到广泛应用的列车自动驾驶(Automatic train operation，ATO)技术，系统概述ATO技术的基本原理和系统架构，分析不同自动化等级的特点及应用场景，综述国内外ATO技术的速度曲线规划和运行速度控制两项关键技术的研究现状；总结城市轨道交通列车和高速动车组自动驾驶的技术特点与工程应用实践，阐述重载列车自动驾驶面临的纵向冲动安全、系统非线性强及时滞大、长大下坡线路等独特挑战以及应对策略和技术手段；从全域环境感知、决策控制等方面展望列车自动驾驶技术发展方向。研究表明：当前列车自动驾驶动态运行技术虽已取得显著进展，但速度曲线规划、动态安全控制等环节与环境感知数据的深度融合程度不够，“感知-决策-控制”全链条协同机制尚不完善，导致复杂场景下的动态适应性不足；针对重载列车自动驾驶技术的研究，速度曲线规划算法与速度跟踪控制算法未紧密结合精细化动力学模型，控制策略难以适应复杂工况下的运行安全性要求。未来还应进一步发展涵盖列车系统动力学、牵引制动动态特性、列车状态感知等适用于列车运行安全控制的精细化模型，利用数据驱动技术进行模型降阶与在线校正，刻画列车在复杂工况下的非线性动态行为；构建全域精准环境认知体系，提升感知技术水平，增强系统在复杂运行图与多变环境下的安全运行能力；推动感知决策控制深度协同与自主进化，引入深度强化学习与多目标优化算法，实现列车运行状态的精准推演与最优驾驶策略的动态生成，助力轨道交通数智化发展。

关键词: 列车自动驾驶, 动态运行控制, 高速列车, 重载列车, 城市轨道交通, 速度曲线规划, 动态安全控制, 环境感知

Abstract: Automatic train operation(ATO), widely applied the rail transit domain, is systematically reviewed in this paper. The fundamental principles and system architecture of ATO are outlined, and the characteristics and application scenarios of different automation grades are analyzed. The status of two pivotal technologies in ATO, namely speed trajectory optimization and speed tracking control, is examined both domestically and internationally. The technical features and engineering practices of ATO in urban rail transit and high-speed trains are summarized. The unique challenges in the ATO of heavy-haul trains are elaborated, including safety issues related to longitudinal impacts, significant system nonlinearity coupled with large time delays, and operations on long downhill sections. Corresponding countermeasures and technical approaches are also presented. The development direction of ATO is prospected from aspects of global environmental perception and decision-making control. It is demonstrated that, although significant progress has been made in dynamic train operation for ATO, the integration of environmental perception data into critical modules such as speed trajectory optimization and dynamic safety control remains insufficient. Moreover, the collaborative mechanism across the full“perception-decision-control”chain is still inadequate, resulting in limited adaptability under complex operational scenarios. In the context of heavy-haul train ATO, speed trajectory optimization algorithms and speed tracking control algorithms are not yet tightly coupled with refined train dynamics models, making it difficult for control strategies to meet safety requirements under complex operating conditions. In the future, refined models for train operation safety control should be further developed, encompassing train system dynamics, dynamic characteristics of traction and braking systems, and train state perception. Data-driven technologies should be employed to conduct model order reduction and online calibration, thereby accurately describing the nonlinear dynamic behavior of trains under complex operating conditions. A global and accurate environmental cognition system should be constructed to improve the level of perception technology and enhance the safe operation capability of the system under complex operation diagrams and changing environments. The in-depth collaboration and autonomous evolution of perception, decision-making and control should be promoted, and deep reinforcement learning and multi-objective optimization algorithms should be introduced to realize the accurate deduction of train operation state and the dynamic generation of optimal driving strategies, to advance the digital and intelligent transformation of rail transit.

Key words: automatic train operation, dynamic control, high-speed train, heavy-haul train, urban rail transit, speed trajectory optimization, safety control, environmental perception

中图分类号:

TG156

凌亮, 李涛涛, 高峰, 李宏伟, 陈哲, 刘建新, 侯冶, 王希云, 翟婉明. 列车自动驾驶动态运行控制技术研究进展[J]. 机械工程学报, 2026, 62(16): 228-252.

LING Liang, LI Taotao, GAO Feng, LI Hongwei, CHEN Zhe, LIU Jianxin, HOU Ye, WANG Xiyun, ZHAI Wanming. Advances in Dynamic Control Technology for Train Autonomous Operation[J]. Journal of Mechanical Engineering, 2026, 62(16): 228-252.

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列车自动驾驶动态运行控制技术研究进展

Advances in Dynamic Control Technology for Train Autonomous Operation

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