[1]LI Deyi,WANG Liuzhao,DU Yu,et al.Spatial intelligence: a living map in driving situation[J].CAAI Transactions on Intelligent Systems,2026,21(2):498-509.[doi:10.11992/tis.202601022]
Copy

Spatial intelligence: a living map in driving situation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 2 Page number: 498-509 Column: 学术论文—智能系统 Public date: 2026-03-05
Title:
Spatial intelligence: a living map in driving situation
Author(s):
LI Deyi1;  WANG Liuzhao2;  DU Yu3;  YIN Jialun4
1. Institute of Systems Engineering, Academy of Military Sciences, Beijing 100091, China;
2. Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100036, China;
3. College of Robotics, Beijing Union University, Beijing 100101, China;
4. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Keywords:
spatial intelligence; living map; right of way; driving situation map; moving with the vehicle; variable-resolution grid; logarithmic polar coordinates; Bayesian filtering; hierarchical memory; autonomous driving
CLC:
TP18
DOI:
10.11992/tis.202601022
Abstract:
This paper examines the real-time generation technology of the live map from the driver’s perspective, who follows the car during the process of autonomous driving, known as driving space intelligence. Online generation and continuous updating of the geospatial representation of the vehicle’s current road right is the basic data of driving decision-making in the machine driver’s mind, which is of fundamental significance to the realization of cognitive engineering of unmanned vehicles. Taking a variable granularity logarithmic polar grid as a unified formal language, the generation framework of the active map system is given. It can be upgraded from the traditional static map problem to the state estimation problem of the geographical space that moves with the car, and it can be continuously updated and estimated; It can carry the fusion of multi-source heterogeneous road sensor perception and prior knowledge, and maintain posterior distribution with Bayesian filtering as the core mechanism; Instantaneous memory-working memory-long-term memory is used to maintain the continuity of the map, and real-time planning is used to realize the cognitive closed loop of the driving map. If there are no other moving obstacles on the road, the mobile map can enable the car to drive from the starting point to the destination at varying speeds on different sections.
References:
[1] ASRAT K T, CHO H J. A comprehensive survey on high-definition map generation and maintenance[J]. ISPRS international journal of geo-information, 2024, 13(7): 232
[2] 李德毅, 张天雷, 韩威, 等. 认知机器的结构和激活[J]. 智能系统学报, 2024, 19(6): 1604-1613 LI Deyi, ZHANG Tianlei, HAN Wei, et al. Structure and activation of cognitive machines[J]. CAAI transactions on intelligent systems, 2024, 19(6): 1604-1613
[3] LI Qi, WANG Yue, WANG Yilun, et al. HDMapNet: an online HD map construction and evaluation framework[EB/OL]. (2021-07-13)[2026-01-18]. https://arxiv.org/abs/2107.06307.
[4] 李德毅. 认知物理学导引[J]. 智能系统学报, 2024, 19(3): 493 LI Deyi. Introduction to cognitive physics[J]. CAAI transactions on intelligent systems, 2024, 19(3): 493
[5] 李德毅, 殷嘉伦, 张天雷, 等. 机器认知四要素说[J]. 中国基础科学, 2023, 25(3): 1-10,22 LI Deyi, YIN Jialun, ZHANG Tianlei, et al. Four most basic elements in machine cognition[J]. China basic science, 2023, 25(3): 1-10,22
[6] LI Deyi, LIU Changyu, GAN Wenyan. A new cognitive model: Cloud model[J]. International journal of intelligent systems, 2009, 24(3): 357-375
[7] ATKINSON R C, SHIFFRIN R M. Human memory: a proposed system and its control processes[M]//SPENCE K W, SPENCE J T, eds. Psychology of Learning and Motivation. New York: Academic Press, 1968: 89-195.
[8] BADDELEY A D, HITCH G. Working memory[M]//BOWER G H, ed. Psychology of Learning and Motivation. New York: Academic Press, 1974: 47-89.
[9] SAND?Y S S, HEGDE J, SCHJ?LBERG I, et al. Polar map: a digital representation of closed structures for underwater robotic inspection[J]. Aquacultural engineering, 2020, 89: 102039
[10] GENG Junyi, LANGELAAN J W. A quasi polar local occupancy grid approach for vision-based obstacle avoidance[C]//AIAA Guidance, Navigation, and Control Conference. Grapevine: AIAA, 2017: 1-25.
[11] QIU Wenzhao, Pang Shanmin, Zhang Hao, et al. HeightMapNet: explicit height modeling for end-to-end HD map learning[EB/OL]. (2021-07-13)[2026-01-18]. https://arxiv.org/abs/2107.06307.
[12] FOX D, BURGARD W, THRUN S. Markov localization for mobile robots in dynamic environments[J]. Journal of artificial intelligence research, 1999, 11: 391-427
[13] KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of basic engineering, 1960, 82(1): 35-45
[14] ASGHARIVASKASI A, ATANASOV N. Active Bayesian multi-class mapping from range and semantic segmentation observations[C]//2021 IEEE International Conference on Robotics and Automation. Xi’an: IEEE, 2021: 1-7.
[15] MORAVEC H, ELFES A. High resolution maps from wide angle sonar[C]//Proceedings of 1985 IEEE International Conference on Robotics and Automation. St. Louis: IEEE, 2003: 116-121.
[16] ELFES A. Using occupancy grids for mobile robot perception and navigation[J]. Computer, 1989, 22(6): 46-57
[17] NARAYANA M, KOLLING A, NARDELLI L, et al. Lifelong update of semantic maps in dynamic environments[EB/OL]. (2020-10-17)[2026-01-18]. https://arxiv.org/abs/2010.08846.
[18] THRUN S, BURGARD W, FOX D. Probabilistic robotics[M]. Cambridge, Mass.: MIT Press, 2006.
[19] DOUCET A, FREITAS N, GORDON N. Sequential Monte Carlo methods in practice[M]. New York: Springer, 2001.
[20] 李德毅, 刘常昱. 论正态云模型的普适性[J]. 中国工程科学, 2004, 6(8): 28-34 LI Deyi, LIU Changyu. Study on the universality of the normal cloud model[J]. Strategic study of CAE, 2004, 6(8): 28-34
[21] SANFELIU A, FU K S. A distance measure between attributed relational graphs for pattern recognition[J]. IEEE transactions on systems, man, and cybernetics, 1983, 13(3): 353-362
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems