面向多维度逻辑场景的自动驾驶安全性聚类评价方法*

doi:10.19562/j.chinasae.qcgc.2020.11.002

摘要/Abstract

摘要： 准确可靠的安全性测试评价是自动驾驶汽车推广应用的基础,但自动驾驶汽车行驶环境复杂多变,传统方法无法满足其安全性评价需求。本文中建立了一种面向多维度逻辑场景的自动驾驶安全性聚类评价方法。首先基于高斯模型对多维度逻辑场景下的遍历测试危险参数结果进行聚类;然后利用聚类结果,提出危险域离散度、危险域范围两个基本评价指标,并将其耦合形成自动驾驶安全性聚类评价参数——场景危险率;最后应用本文提出的方法对一种黑盒自动驾驶算法进行了测试评价。结果表明,本文提出的自动驾驶安全性聚类评价方法可综合考虑算法在多维度逻辑场景下的统计学规律,获得量化指标,实现更为全面的科学评价。

关键词: 自动驾驶, 安全性评价, 多维度逻辑场景, 聚类分析, 场景危险率

Abstract: Accurate and reliable safety evaluation is the basis for the promotion and application of autonomous vehicles. However, the driving environment is complex and changeable, and traditional methods can’t meet the needs of its safety evaluation. A multi-dimensional logical scenario-oriented clustering evaluation method is established in this paper for autonomous driving safety evaluation. Firstly, the results of ergodic test hazard parameters in a multi-dimensional logic scenario are clustered based on Gaussian model; then, two basic evaluation indicators of the danger field dispersion and danger field range are proposed by using the clustering result. An autonomous driving safety clustering evaluation parameter, namely scenario hazard rate is built by coupling the two indicators. Finally, a black box autopilot algorithm is evaluated using the method proposed in the paper. The results show that the method proposed in this paper can comprehensively consider the statistical law in a multi-dimensional logical scenario, obtain a quantitative index, and achieve a comprehensive scientific evaluation

Key words: autonomous driving, safety evaluation, multi-dimensional logical scenario, cluster analysis, scenario hazard rate

朱冰, 张培兴, 赵健. 面向多维度逻辑场景的自动驾驶安全性聚类评价方法^*[J]. 汽车工程, 2020, 42(11): 1458-1463.

Zhu Bing, Zhang Peixing, Zhao Jian. Clustering Evaluation Method of Autonomous Driving Safety for Multi-dimensional Logical Scenario[J]. Automotive Engineering, 2020, 42(11): 1458-1463.

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面向多维度逻辑场景的自动驾驶安全性聚类评价方法^*

Clustering Evaluation Method of Autonomous Driving Safety for Multi-dimensional Logical Scenario

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