人本协同自动驾驶系统的演进脉络、关键挑战与未来展望

刘晓波; 董霓; 闫学东

doi:10.3969/j.issn.0258-2724.20260022

人本协同自动驾驶系统的演进脉络、关键挑战与未来展望

doi: 10.3969/j.issn.0258-2724.20260022

刘晓波^{1, 2,},
董霓^{1, 2,},
闫学东^1, ,

1.
西南交通大学交通运输与物流学院，四川成都 611756
2.
西南交通大学综合运输智能化四川省重点实验室，四川成都 611756

基金项目: 国家重点研发计划（2025YFB2606500）；国家自然科学基金项目（725712229）；四川省科技计划“骈骥”项目（2025HJPJ0011）；四川省国际科技创新合作项目（2025YFHZ0235）

详细信息

作者简介:
刘晓波（1974—），男，教授，研究方向为智能交通与物流，E-mail：xiaobo.liu@swjtu.edu.cn

通讯作者:
闫学东（1975—），男，教授，研究方向为综合交通系统分析、交通安全，E-mail：xdyan@swjtu.edu.cn

中图分类号: U491
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- 文章访问数: 162
- HTML全文浏览量: 64
- PDF下载量: 50
- 被引次数: 0
出版历程
- 收稿日期: 2026-01-16
- 修回日期: 2026-04-13
- 刊出日期: 2026-04-16

Evolutionary Pathways, Key Challenges, and Future Prospects of Human-Centered Collaborative Autonomous Driving Systems

LIU Xiaobo^{1, 2
,},
DONG Ni^{1, 2
,},
YAN Xuedong^{1
, ,}

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
2.
Sichuan Provincial Key Laboratory of Intelligent Integrated Transportation, Southwest Jiaotong University, Chengdu 611756, China

摘要

摘要:
随着自动驾驶系统的发展，“长尾场景”的瓶颈日益凸显，证明了以技术主导的 “自动化替代”路线缺乏对人类角色、人机关系与系统伦理的完整定义. 本研究首先梳理自动驾驶系统从“工具化辅助”到“自动化主导”，最终迈向“协同化管控”的演进脉络，阐释人机关系从单向控制转向双向协作的本质变革；其次，针对动态信任机制的建立与维持、控制权限的实时优化分配，以及双向协同交互范式的构建等制约协同效能提升的关键挑战，阐述其在认知机理、控制理论与交互设计层面的内在逻辑、影响因素与现存瓶颈；最后，系统性提出发展基于多源融合的人类状态感知与建模技术、探索基于实时评估的自适应协同控制架构，以及实现从“单向通知”到“双向协同”的交互范式转变，以完善自动驾驶系统的未来发展路径. 本研究通过对既有理论、技术路线与应用案例的综述研究，为推进可理解、可信赖、可广泛接受的“人本协同”自动驾驶系统发展提供了系统性理论支撑与实践参考.
- 人本协同 /
- 自动驾驶系统 /
- 人类驾驶员 /
- 人机协作 /
- 交互范式
Abstract:
With the development of autonomous driving systems, the increasingly prominent bottleneck of “long-tail scenarios” demonstrates that the technology-driven “automation substitution” approach lacks a complete definition of human roles, human-machine relationships, and system ethics. First, the evolutionary pathways of autonomous driving systems from “tool-based assistance” to “automation-led” and ultimately to “collaborative control” were traced, and the fundamental transformation of the human-machine relationship from one-way control to two-way collaboration was elucidated. Secondly, to address key challenges that restrict the improvement of collaborative efficiency, such as the establishment and maintenance of dynamic trust mechanisms, the real-time optimal allocation of control permissions, and the construction of two-way collaborative interaction paradigms, their inherent logic, influencing factors, and existing bottlenecks at the levels of cognitive mechanisms, control theory, and interaction design were expounded on. Finally, developing human state perception and modeling technologies based on multi-source fusion, exploring adaptive collaborative control architectures based on real-time evaluation, and realizing the interaction paradigm shift from “one-way notification” to “two-way collaboration” were systematically proposed, which refined the future development pathways of autonomous driving systems. Through a review of existing theories, technical routes, and application cases, systematic theoretical support and practical references are provided for promoting the development of understandable, trustworthy, and widely acceptable “human-centered collaborative” autonomous driving systems.
- human-centered collaboration /
- autonomous driving system /
- human driver /
- human-machine collaboration /
- interaction paradigm

HTML全文

图 1 HCC-ADS中人机协作设计准则、挑战与协作方法论

Figure 1. Design principles, challenges, and collaborative methodologies for human-machine collaboration in HCC-ADS

下载: 全尺寸图片幻灯片

表 1 自动驾驶系统向“人本协同”范式演进路径

Table 1. Evolutionary pathways of autonomous driving systems towards a “human-centered collaboration” paradigm

对比维度	工具化辅助阶段	自动化主导阶段	协同化探索阶段	“人本协同” 范式核心特征
核心目标	在人类驾驶员全权负责下，提升特定任务的执行精度与舒适度^[42]	优化系统的驾驶平顺性、安全性及接管效率，减轻人类驾驶员负担^[27-28]	追求人机团队的整体安全性、任务成功率与用户体验^[35-36]	追求人机系统效能最优化及干预最小化
交互逻辑	人类决策并主导，系统被动响应指令或提供预警，无复杂交互	系统主导驾驶，人类负责监控与接管；交互多为系统发起的状态通知或接管请求	强调人机双向意图理解与透明通信，支持共享控制与即时反馈^[37]	基于驾驶员意图识别与系统可解释性的双向闭环信息流
角色分配	人类为唯一驾驶员，系统为无自主性的工具	系统为“驾驶员”，人类为监督者/接管者. 角色切换由系统状态触发^[27-28]	基于实时情境、系统能力与人类驾驶员状态，动态协商与分配控制权与责任	从静态分工转向动态适配，实现个性化与自适应的协作角色分配^[5]
信任基础	信任基于单一功能的可靠性与实用性	信任建立在系统整体表现的稳定与安全记录上，脆弱且易崩溃^[29-30]	信任源于系统的透明度、能力边界声明及在协作中表现出的可预测性与共情能力	从基于表现的信任转向基于理解与韧性的信任，通过信任校准机制建立长期稳健的合作关系
技术重心	实现精准的纵向/横向控制	提升系统独立驾驶的鲁棒性	整合人类驾驶员状态监控、意图识别、共享控制与自然人机交互（HMI）^[35-36]	整合人因特征建模、人机共享决策模型与自适应控制架构
主要风险/局限	完全依赖人类导致疲劳、分心及反应能力局限^[25-26]	警惕性下降、情境意识断裂、技能退化、自动化悖论，在长尾场景下系统能力边界突显^[10]	人机意图对齐的复杂性、认知脱节后的情境意识快速恢复，以及建立深度互信的挑战^[39-40]	将人的局限性和人因风险转化为设计输入与协作核心，通过协同机制主动缓解
协作范式	系统是工具，尚未形成协作关系	以系统效率与安全为中心的主从式协作，人作为后备	以人机整体效能与用户体验为中心的伙伴式协作探索	成熟的人本协作范式，实现安全、舒适、可信且广泛接受的自动驾驶

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