基于“度量-语义-决策”空间的自动驾驶决策

应申, 石群智, 李玉, 顾江岩, 李必军

应申, 石群智, 李玉, 顾江岩, 李必军. 基于“度量-语义-决策”空间的自动驾驶决策[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20250115
引用本文: 应申, 石群智, 李玉, 顾江岩, 李必军. 基于“度量-语义-决策”空间的自动驾驶决策[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20250115
YING Shen, SHI Qunzhi, LI Yu, GU Jiangyan, LI Bijun. Autonomous Driving Decision-making Based on the " Measurement-Semantics-Decision" Space[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20250115
Citation: YING Shen, SHI Qunzhi, LI Yu, GU Jiangyan, LI Bijun. Autonomous Driving Decision-making Based on the " Measurement-Semantics-Decision" Space[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20250115

基于“度量-语义-决策”空间的自动驾驶决策

基金项目: 

国家自然科学基金(42471479),湖北重大科技攻关项目(2023BAA017)。

详细信息
    作者简介:

    应申,博士,教授,研究方向为自动驾驶高精地图、三维地籍、地图学、可视化。shy@whu.edu.cn

  • 中图分类号: P208

Autonomous Driving Decision-making Based on the " Measurement-Semantics-Decision" Space

  • 摘要: 自动驾驶是一个跨学科的集成领域,研究者一般将自动驾驶系统分为感知、规划、控制三个模块,但三个模块内具体任务存在较大差异,模块之间的交互关系不够明确,系统在面对复杂且多样的交通场景会表现出不足。同时,端到端的自动驾驶系统具备可解释性不足的问题。提出了自动驾驶“度量-语义-决策”空间决策链条,旨在针对感知、规划、控制自动驾驶系统这种流水线关系进行补充以及在一定程度上增添端到端的自动驾驶系统的可解释性,分别构建位置与几何、语义类别、规则与推理三个空间,其中度量空间提供自动驾驶交通环境中的统一时空基准以及要素位置、几何信息,语义空间提供要素类别、移动性、危险性、规则的多维信息表达,决策空间基于度量空间与语义空间实施交通规则转换、全局路径规划、局部车道规划、引导线生成、运动控制等功能。该决策链条明确了各空间内自动驾驶任务的具体内容,分析了空间之间任务的关联关系以及高精地图对三个空间的支撑作用,并以路口场景决策为例,分析了该决策链条的实际应用流程。
    Abstract: Objectives: The rapid development of autonomous driving systems has brought forward the challenge of integrating various components from different disciplines. Traditional autonomous driving systems are generally divided into three modules: perception, planning, and control. However, these modules typically handle distinct tasks with limited interaction, resulting in suboptimal performance when encountering complex and diverse traffic scenarios. Additionally, end-to-end autonomous driving systems suffer from a lack of interpretability. This study aims to address these issues by proposing a " Measurement -Semantics-Decision" spatial decision-making chain for autonomous driving. The goal is to complement the pipeline relationship of perception, planning and control of autonomous driving system and to increase the interpretability of end-to-end autonomous driving system. Methods: The autonomous driving "Measurement-Semantics-Decision" space decision chain constructs three spaces: position and geometry, semantic categories, and rules and reasoning. The measurement space provides a unified spatiotemporal reference as well as positional and geometric information of elements in the autonomous driving environment. The semantics space offers a multi-dimensional information representation of element categories, mobility, risk, and rules. The decision space, based on the measurement and semantics spaces, implements functions such as traffic rule transformation, global path planning, local lane planning, guidance line generation, and motion control. Results: This decision chain clarifies the specific tasks within each space of autonomous driving, analyzes the relationships between tasks across spaces, and examines the supporting role of high-definition maps for the three spaces. And then, taking the decision-making of intersection scenarios as an example, the actual application process of this decision-making chain was clarified. Conclusions:The proposed Metric-Semantic-Decision spatial decision-making chain effectively organizes and clarifies the tasks within the perception, planning, and control modules. By structuring the decision-making process into clearly defined spaces, the system is better equipped to handle a broader range of traffic scenarios, including complex and dynamic environments such as intersections. Additionally, the integration of the Metric and Semantic Spaces allows for a more interpretable and explainable decision-making process, addressing the challenges of traditional end-to-end autonomous systems.
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