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基于平行测试的认知自动驾驶智能架构研究

王晓,  张翔宇,  周锐,  田永林,  王建功,  陈龙,  孙长银

文章导航 > 自动化学报  > 2024 >  50(2): 356-371
王晓, 张翔宇, 周锐, 田永林, 王建功, 陈龙, 孙长银. 基于平行测试的认知自动驾驶智能架构研究. 自动化学报, 2024, 50(2): 356−371 doi: 10.16383/j.aas.c220820
引用本文: 王晓, 张翔宇, 周锐, 田永林, 王建功, 陈龙, 孙长银. 基于平行测试的认知自动驾驶智能架构研究. 自动化学报, 2024, 50(2): 356−371 doi: 10.16383/j.aas.c220820
shu
Wang Xiao, Zhang Xiang-Yu, Zhou Rui, Tian Yong-Lin, Wang Jian-Gong, Chen Long, Sun Chang-Yin. An intelligent architecture for cognitive autonomous driving based on parallel testing. Acta Automatica Sinica, 2024, 50(2): 356−371 doi: 10.16383/j.aas.c220820
Citation: Wang Xiao, Zhang Xiang-Yu, Zhou Rui, Tian Yong-Lin, Wang Jian-Gong, Chen Long, Sun Chang-Yin. An intelligent architecture for cognitive autonomous driving based on parallel testing. Acta Automatica Sinica, 2024, 50(2): 356−371 doi: 10.16383/j.aas.c220820
shu

基于平行测试的认知自动驾驶智能架构研究

doi: 10.16383/j.aas.c220820
  • 1.

    安徽大学人工智能学院 合肥 230031

  • 2.

    自主无人系统技术教育部工程研究中心 合肥 230031

  • 3.

    安徽省无人系统与智能技术工程研究中心 合肥 230031

  • 4.

    中国科学院自动化研究所复杂系统管理与控制国家重点实验室 北京 100190

  • 5.

    青岛智能产业技术研究院 青岛 266000

  • 6.

    澳门科技大学创新工程学院系统工程研究所 澳门 999078

  • 7.

    青岛慧拓智能机器有限公司 青岛 266109

基金项目: 广东省重点领域研发计划 (2020B0909050003), 国家自然科学基金 (62173329) 资助
详细信息
    作者简介:

    王晓：安徽大学人工智能学院教授. 2016年获得中国科学院大学社会计算专业博士学位. 主要研究方向为社会计算, 平行车联网以及认知自动驾驶. E-mail: x.wang@ia.ac.cn

    张翔宇：中国科学院自动化研究所博士研究生. 2021年获得电子科技大学机械工程专业硕士学位. 主要研究方向为认知自动驾驶, 知识图谱, 机器人自动化控制.E-mail: xiangyu.zhang@ia.ac.cn

    周锐：澳门科技大学创新工程学院博士研究生, 中国科学院自动化研究所高级工程师. 2014年获得德国布伦瑞克工业大学车辆工程硕士学位. 主要研究方向为自动驾驶, 智能网联测试场及功能安全.E-mail: rui.zhou@waytous.com

    田永林：中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士后. 2022年获得中国科学技术大学控制科学与工程专业博士学位. 主要研究方向为计算机视觉, 自动驾驶.E-mail: yonglin.tian@ia.ac.cn

    王建功：中国科学院自动化研究所博士研究生. 2018年获得同济大学学士学位. 主要研究方向为计算机视觉, 交通场景理解, 医学图像处理.E-mail: wangjiangong2018@ia.ac.cn

    陈龙：中国科学院自动化研究所研究员. 2013年获得武汉大学博士学位. 主要研究方向为自动驾驶车辆, 机器人. E-mail: long.chen@ia.ac.cn

    孙长银：安徽大学人工智能学院教授. 主要研究方向为智能控制与优化, 强化学习, 神经网络, 数据驱动控制. 本文通信作者.E-mail: cysun@seu.edu.cn

An Intelligent Architecture for Cognitive Autonomous Driving Based on Parallel Testing

  • 1.

    School of Artificial Intelligence, Anhui University, Hefei 230031

  • 2.

    Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei 230031

  • 3.

    Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, Hefei 230031

  • 4.

    The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190

  • 5.

    Qingdao Academy of Intelligent Industries, Qingdao 266000

  • 6.

    Institute of System Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078

  • 7.

    Vehicle Intelligence Pioneers, Inc., Qingdao 266109

Funds: Supported by Key-area Research and Development Program of Guangdong Province (2020B0909050003) and National Natural Science Foundation of China (62173329)
More Information
    Author Bio:

    WANG Xiao　Professor at the School of Artificial Intelligence, Anhui University. She received her Ph.D. degree in social computing from University of Chinese Academy of Sciences in 2016. Her research interest covers social computing, parallel internet of vehicles, and cognitive autonomous vehicle

    ZHANG Xiang-Yu　Ph.D. candidate at the Institute of Automation, Chinese Academy of Sciences. He received his master degree in mechanic engineering from University of Electronic Science and Technology of China in 2021. His research interest covers cognitive autonomous vehicle, knowledge graph, and robot automatic control

    ZHOU Rui　Ph.D. candidate in the Faculty of Innovation Engineering, Macau University of Science and Technology, senior engineer at the Institute of Automation, Chinese Academy of Sciences. He received his master degree in automobile engineering from Technical University of Braunschweig, Germany in 2014. His research interest covers autonomous vehicle, test area for intelligent-connected vehicle, and functional safety

    TIAN Yong-Lin　Postdoctor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree in control science and engineering from University of Science and Technology of China in 2022. His research interest covers computer vision and autonomous driving

    WANG Jian-Gong　Ph.D. candidate at the Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree from Tongji University in 2018. His research interest covers computer vision, traffic scene understanding, and medical image processing

    CHEN Long　Professor at the Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from Wuhan University in 2013. His research interest covers autonomous cars and robotics

    SUN Chang-Yin　Professor at the School of Artificial Intelligence, Anhui University. His research interest covers intelligent control and optimization, reinforcement learning, neural networks, and data-driven control. Corresponding author of this paper

    摘要
  • 摘要: 在大数据、云计算和机器学习等新一代人工智能技术的推动下, 自动驾驶的感知智能在近年来得到显著的提升与发展. 然而, 与人类驾驶过程中隐含的以自我目的实现为引导的自探索性和自主性相比, 现阶段自动驾驶技术主要以辅助驾驶功能为主, 还停留在以被动感知、规划与控制为主的初级智能自动驾驶阶段. 为实现车辆智能从数据驱动的环境感知、辅助决策、被动规划到知识驱动的场景认知、推理决策、主动规划的提升, 亟需增强车辆自身对复杂外界信息归纳提炼、推理决策、评价估计等类人能力. 首先回顾自动驾驶关键技术演化及其应用发展历程; 随后分析测试对车辆智能评估的效用; 然后基于平行测试理论, 提出自动驾驶车辆认知智能训练、测试与评估空间的构建方法, 并设计基于平行测试的认知自动驾驶智能训练框架. 该项研究工作预期能为推动自动驾驶从感知智能向认知智能的升级提供可行的技术支撑与实现路径.
    Abstract: Driven by the new generation of artificial intelligence technologies such as big data, cloud computing and machine learning, the perceptive intelligence of autonomous driving has been significantly improved and progressed in recent years. However, compared with the self-purpose driven human driving process, the current autonomous driving technologies are mainly focusing on the auxiliary driving functions, and still stay in a primary-intelligence stage which is dominated by passive perception, planning and control. In order to cross the “cognition gap” of vehicle intelligence, from data-driven environment perception, assisted decision making and passive planning to knowledge-driven scenario cognition, reasonable decision making and active planning, it is important to enhance the humanoid abilities of the vehicles, including but not limited to summarize and extract complex external information from the environment, reasoning, evaluation and estimation. This paper reviews the evolution and application of key technologies in autonomous driving, analyzes the effectiveness of testing on vehicle intelligence and performance evaluation. After then, based on the parallel test theory, it puts forward the space construction method for training, testing and evaluating the cognitive intelligence of autonomous vehicle, and proposes an intelligent training framework for cognitive autonomous driving. The work is expected to provide a feasible and possible path for autonomous vehicle cognitive intelligence.
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