The AD4CHE Dataset and Its Application in Typical Congestion Scenarios of Traffic Jam Pilot Systems
Yuxin Zhang, Cheng Wang, Ruilin Yu, Luyao Wang, Wei Quan, Yang Gao, Pengfei Li
Abstract
Autonomous driving has attracted considerable attention from research and industry communities. Although prototypes of automated vehicles (AVs) are developed, remaining safety issues and functional insufficiencies hinder their market introduction. To obtain reasonably foreseeable scenarios and study human driving policies, many naturalistic driving datasets are proposed. However, no open-source dataset filled with congestion scenarios is publicly available. The paper presents the Aerial Dataset for China's Congested Highways & Expressways (AD4CHE). It contains 5.12 hours of aerial survey data from four different cities in China, with a total driving distance of 6540.7 km. Moreover, overlap and non-overlap cut-in scenarios are distinguished to better describe driver behavior in congestion scenarios. Both types of cut-in scenarios are extracted and parameterized. The Kernel Density Estimator (KDE) is utilized to generate parameter distributions for the scenario-based testing method. Furthermore, the driving behavior in overlap cut-in scenarios is intensively analyzed. The results reveal that the drivers have an evasive maneuver during overlap cut-in of challenging vehicles, and the preferred following distance varies with the relative longitudinal velocity. Both scenario parameterization and driving behavior analysis can contribute to developing and verifying Traffic Jam Pilot (TJP) systems deployed in Chinese traffic situations.