Clustering Analysis of Typical Ramp Scenarios Based on Naturalistic Driving Data

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.