| 摘要: |
| 无人驾驶技术的快速发展正推动城
市规划从“以车为本”向“人—车—境”协
同范式转型。本研究基于理论演进与实践案
例的双重视角，系统分析其对城市街道空间
的重塑效应。理论层面梳理了街道空间的百
年演变：从工业时代“车本位”主导，到20
世纪80—90 年代人性化街道理念兴起，直至
当前数字孪生与智慧交通推动的技术空间融
合。通过美、日、加、中等国案例研究，揭
示了技术落地面临的基础设施适配与空间权
属重构等挑战。研究发现，无人驾驶将从三
方面改变街道设计：功能尺度要素调整、路
权重构与优先级转变和治理模式与基础设施
革新。研究创新性提出分级应对策略：针对
L3+单车智能提出道路断面优化方案，针对
车路/车场协同设计停车空间改造方案。特别
探讨了无人车在融合“端到端”和大语言模
型的开发模式下，设计师需关注的设计议
题。研究成果为智慧城市发展提供了兼顾技
术创新与人文关怀的规划思路。 |
| 关键词: 无人驾驶 规划设计 街道设计理
论 无人驾驶规划案例 综述 |
| DOI：10.13791/j.cnki.hsfwest.20250407007 |
| 分类号: |
| 基金项目:国家资助博士后研究人员计划（GZC20240254）；国家自然科学基金青年科学基金项目（52402504）；国家自然科学基金面上项目（42471498）；
中国博士后科学基金项目（2023M740601）；江苏省卓越博士后计划（2024ZB363） |
|
| How autonomous driving technology reshapes street planning and design: Dual perspectivesfrom theory and practice |
|
YANG Liu,LIANG Yue,ZHANG Songan,BAI Jie,FENG Yuxiang
|
| Abstract: |
| As cities increasingly pursue smart development and carbon neutrality, autonomous driving
technology has emerged as a critical force reshaping urban mobility and spatial planning. By
optimizing driving behaviors and enhancing energy efficiency, autonomous vehicles (AVs) are
projected to reduce transport-related emissions by up to 34%. Yet beyond emissions reduction, AVs
are driving a broader transformation—from car-centric planning paradigms to integrated systems that
emphasize coordination among people, vehicles, and the urban environment. This paper conducts a
systematic examination of autonomous driving technology’s developmental trajectory and its spatial
consequences, with particular emphasis on urban street environments. Tracing the development of
AVs from early conceptual prototypes in the 20th century to recent breakthroughs in artificial
intelligence, deep learning, and vehicle-to-everything (V2X) communication, it identifies four key
phases: emergence (pre-1970s), initiation (1980s–1990s), expansion (2000s), and breakthrough (post-
2010s). Each phase corresponds with evolving urban design theories, shifting the role of streets from
utilitarian transport corridors to multifunctional public spaces that support diverse social and mobility
functions.The global landscape of AV integration into urban planning is illustrated through a series of
diverse case studies. San Francisco’s Smart City initiative reallocated road space from cars to
pedestrians and cyclists while introducing autonomous ride-hailing services. Although these
interventions were designed to promote more human-centered mobility, empirical evidence indicates
that the deployment of AV-based ride-hailing may have inadvertently contributed to increased
congestion due to a rise in vehicle miles travelled. In contrast, Toyota’s Woven City in Shizuoka
represents a purpose-built prototype that establishes a differentiated street hierarchy comprising AVdominant
corridors, pedestrian promenades, and ecological linear parks. This modular and scalable
framework exemplifies how AVs can be seamlessly integrated with recreational and environmental
priorities. Meanwhile, Toronto’s “City of Tomorrow,” proposed by Sidewalk Labs, envisioned a
highly connected smart district driven by AVs and data-centric infrastructure. Despite its ambition, the
project was ultimately cancelled due to growing public concerns over data privacy, opaque
governance processes, and potential monopolistic control. Finally, Chongqing’s AI-integrated transit
system in China offers a more pragmatic approach, featuring real-time traffic signal coordination,
vehicle-to-infrastructure (V2I) technologies, and autonomous minibuses. While demonstrating the
operational feasibility of AVs in mixed-use urban settings, it also exposes current limitations in
deploying AVs within complex and dynamic open-road environments. These cases illustrate the
diverse impacts and trade-offs of AV-led planning. While some strategies enable ecological and
pedestrian-oriented design, others raise new challenges around congestion, social acceptance, anddigital governance.This paper explores how autonomous driving technology is transforming street space design and urban planning. 1) AVs can reclaim road
space by reducing lanes and eliminating parking, reshaping street functions and elements. 2) They prompt a shift in right-of-way priorities, requiring integration
of the “complete streets” concept with emerging technologies, as cities move toward more pedestrian- and cyclist-friendly environments. 3) Street governance
must evolve to include dynamic lane use, flexible loading zones, and smart infrastructure. Further, the integration of end-to-end autonomous driving systems
and large language models introduces new challenges and opportunities for planning. Physical street elements need to be translated into semantic data via
multimodal perception and structured representation, requiring planners to engage in the full semantic modeling process. Human behavioral responses to
complex environments should also be captured through semantic networks. At the infrastructure level, proactive development of digital twin systems and
integration of V2X technologies are essential for supporting multimodal traffic coordination within the urban fabric. |
| Key words: autonomous driving urban planning and design street design theory planning case studies for autonomous driving review |
