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汽车安全与节能学报 ›› 2025, Vol. 16 ›› Issue (2): 334-343.DOI: 10.3969/j.issn.1674-8484.2025.02.017

• 智能驾驶与智慧交通 • 上一篇    

基于OnSite平台的自动泊车测评工具的研究与设计

杨俊儒1(), 郑四发1,2,*(), 许述财1,2, 田野3, 孙剑3, 孙川1, 李浩然1   

  1. 1.清华大学 苏州汽车研究院,苏州 215134,中国
    2.清华大学 车辆与运载学院,北京 100084,中国
    3.同济大学 交通运输工程学院,上海 201804,中国
  • 收稿日期:2025-01-21 修回日期:2025-02-27 出版日期:2025-04-30 发布日期:2025-04-22
  • 通讯作者: * 郑四发,教授。E-mail:zsf@tsinghua.edu.cn
  • 作者简介:杨俊儒(1994—),男(汉),湖北,博士。E-mail:yangjunru@tsari.tsinghua.edu.cn
  • 基金资助:
    国家重点研发计划项目(2023YFB4302600);国家自然科学基金项目(52002215);江苏省自然科学基金项目(BK20231197)

Design and research of an automated parking evaluation tool based on the OnSite platform

YANG Junru1(), ZHENG Sifa1,2,*(), XU Shucai1,2, TIAN Ye3, SUN Jian3, SUN Chuan1, LI Haoran1   

  1. 1. Suzhou Automotive Research Institute, Tsinghua University, Suzhou 215134, China
    2. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    3. College of Transportation Engineering, Tongji University, Shanghai 201804, China
  • Received:2025-01-21 Revised:2025-02-27 Online:2025-04-30 Published:2025-04-22

摘要:

为了完善“公开自然驾驶智能汽车仿真测试环境(OnSite)”平台功能,提出了一种自动泊车的测评工具。该工具采用实车数据采集与建模还原的场景构建方法,依据行业标准与停车位数据,建立了更为全面的测试场景库。提出了一种以完成率为核心,兼顾安全性、效率与精准度的多维评价体系。该测评工具经硬件在环仿真,并与CARLA仿真平台及实车测试结果对比。通过分析第2届OnSite自动驾驶算法挑战赛泊车测试前10名队伍的得分,讨论了未来测评工具及OnSite平台发展。结果表明:该工具不仅为自动泊车功能优化提供科学依据,也可为自动驾驶测评工具研发提供参考。

关键词: 自动驾驶, 仿真测试, 自动泊车, 测试评价, 测评工具, 场景生成

Abstract:

An automated parking evaluation tool was developed to enhance the functionality of the platform OnSite (Open Naturalistic Simulation and Testing Environment) for autonomous driving. This tool used a scenario construction method based on real vehicle data collection and modeling reconstruction. A more comprehensive test scenario library was established according to industry standards and parking space data. A multidimensional evaluation system was proposed, focusing on completion rate while considering safety, efficiency, and accuracy. The evaluation tool underwent hardware-in-the-loop simulation and was compared with results from the CARLA simulation platform and real vehicle tests. Scores of the top 10 teams in the parking test of the 2nd OnSite Autonomous Driving Algorithm Challenge were analyzed to discuss the future development of the evaluation tool and the OnSite platform. The results show that the tool provides a scientific basis for optimizing automated parking functions and serves as a reference for developing autonomous driving evaluation tools.

Key words: autonomous driving, simulation testing, automated parking, testing and evaluation, evaluation tool, scenario generation

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