吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (9): 2511-2519.doi: 10.13229/j.cnki.jdxbgxb.20221425

• 交通运输工程·土木工程 • 上一篇    

面向智能汽车-行人交互的虚拟测试场景构建

郭洪艳1,2(),张家铭1,2,刘俊1,3(),胡云峰1,2   

  1. 1.吉林大学 汽车底盘集成与仿生全国重点实验室，长春 130022
    2.吉林大学 通信工程学院，长春 130022
    3.吉林大学 汽车工程学院，长春 130022
  • 收稿日期:2022-11-10 出版日期:2024-09-01 发布日期:2024-10-28
  • 通讯作者: 刘俊 E-mail:guohy11@jlu.edu.cn;liujun20@jlu.edu.cn
  • 作者简介:郭洪艳（1980-），女，教授，博士.研究方向：智能车辆路径规划与稳定性控制.E-mail：guohy11@jlu.edu.cn
  • 基金资助:
    科学技术部科技创新2030-“新一代人工智能”重大项目(2020AAA0108105)

Construction of virtual test scenario for intelligent vehicle and pedestrian interaction

Hong-yan GUO1,2(),Jia-ming ZHANG1,2,Jun LIU1,3(),Yun-feng HU1,2   

  1. 1.National Key Laboratory of Automotive Chassis Integration and Bionics，Jilin University，Changchun 130022，China
    2.College of Communication Engineering，Jilin University，Changchun 130022，China
    3.College of Automotive Engineering，Jilin University，Changchun 130022，China
  • Received:2022-11-10 Online:2024-09-01 Published:2024-10-28
  • Contact: Jun LIU E-mail:guohy11@jlu.edu.cn;liujun20@jlu.edu.cn

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摘要：

针对城市工况下的智能汽车与行人交互测试需求，提出了一种综合考虑场景在真实世界中的出现频率及其对人车交互性能挑战程度的场景生成方法。首先，依据智能汽车与行人交互的关键特征从自然驾驶数据集中提取出行人横穿道路原始场景数据；然后，针对加速测试需求设计了基于重要性采样理论的关键场景提取方法，从原始场景中提取并构建针对智能汽车-行人交互测试的重要场景；最后，通过对重要场景与原始场景的数据分布比较，证明本文方法能够有效筛选出驾驶过程中可能对安全性能带来挑战的场景，从而实现加速测试，同时兼顾测试场景的统计特征。

关键词: 控制科学与工程, 场景生成, 智能汽车, 自然驾驶数据, 重要性采样, 行人

Abstract:

To satisfy the testing requirements for intelligent vehicles and pedestrians interaction under urban conditions， a scenario generation method that comprehensively considers the appearance frequency of scenarios in the real world and their challenges to intelligent vehicles performance is proposed. First， the original scenarios are extracted from the natural driving dataset. Then a critical scenario extraction method based on importance sampling theory is designed to extract essential scenarios from the original scenarios according to the accelerated test requirement， and pedestrian crossing road scenarios based on natural driving data are constructed. Finally， comparing the distribution of essential scenarios and original scenarios，the results show that this method can effectively screen out scenarios that may pose challenges to intelligent vehicles safety and it also realizes accelerated testing while retaining the statistical characteristics of test scenarios.

Key words: control science and engineering, scenario generation, intelligent vehicle, natural driving data, importance sampling, pedestrian

中图分类号: 

  • TP13

图1

智能汽车测试典型流程"

图2

汽车与行人交互场景生成框架"

图3

车-行人交互场景种类及其示意图"

图4

不同车速下距行人初始纵向距离的出现频率"

图5

车-行人交互场景的决定性变量示意图"

图6

行人横穿道路场景的暴露频率"

图7

智能汽车纵向速度模型遇前方行人的制动过程"

图8

车-行人碰撞模型"

图9

车与行人进入潜在危险区域的相对时间关系"

图10

场景生成总流程"

图11

车与行人最近距离示意图"

图12

重要场景与原始场景组成类型占比对比"

图13

不同方法提取的场景TTC频率分布对比"

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