基于自然驾驶数据的匝道行驶典型场景聚类分析

Clustering Analysis of Typical Ramp Scenarios Based on Naturalistic Driving Data

摘要:

匝道行驶由于存在潜在的车辆间交通冲突，对自动驾驶汽车来说是一项挑战，因此，有必要对匝道的典型场景开展研究，以便应用于自动驾驶汽车的开发和测试。基于自然驾驶数据（naturalistic driving data， NDD）研究了匝道行驶典型场景。首先，通过对车辆在匝道上交互时的3个主要元素进行定义，包括初始状态（initial state， S）、驾驶动作（driving action， A）和交互性能（interaction performance， P），并以此来描述车辆的交互行为；然后，选取用于表征A和P的变量作为聚类特征，通过基于Calinski-Harabasz（CH）指数的K-means聚类方法获得8种聚类结果，根据聚类结果对各变量进行分析，得到4种典型的交互方式；再后，通过分析表征初始状态的变量，运用置信椭圆提取典型的逻辑场景；最后，基于逻辑场景随机选择两个具体场景对自动驾驶系统（autonomous driving system， ADS）进行测试和评估。结果表明，运用研究获得的匝道行驶典型场景进行测试，可揭示自动驾驶汽车与其他交互车辆间的交互能力，说明基于NDD并运用聚类分析方法生成的匝道行驶典型场景是有效的。

关键词:自动驾驶汽车;自然驾驶数据;匝道行驶典型场景;聚类分析

Abstract:

Ramp driving poses a big challenge to autonomous vehicles， on which there are potential traffic conflicts between vehicles. Therefore， it is necessary to study ramp scenarios for development and testing. In this paper， typical ramp scenarios are studied based on naturalistic driving data （NDD）. First， three major elements are defined to describe the interaction between vehicles on the ramp， including the initial state （S）， the driving action （A） and the interaction performance （P）. Next， variables to characterize the A and the P are selected to be clustering features， and then 8 kinds of categories are obtained by the K-means clustering method based on the Calinski-Harabasz （CH） index. Then， according to the clustering results， 4 kinds of typical interaction modes are obtained by analyzing the variables above. Afterwards， by analyzing the variables that characterize the S， typical logical scenarios are extracted by the confidence ellipse. Finally， based on the logical scenarios， two concrete scenarios are selected to test and evaluate the autonomous driving system （ADS）. The results show that testing with typical ramp scenarios can reveal the social cooperation capabilities of autonomous vehicles. Therefore， it is effective to generate typical ramp scenarios by clustering analysis based on NDD.

Key words:autonomous vehicle;naturalistic driving data;typical ramp scenarios;clustering analysis