Preliminary establishment and analysis of automated driving benchmark in China

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Tsinghua Park N0. 1, Beijing, China

Abstract

In order to promote the localization of Automated Driving (AD) in China, it is necessary to collect large-scale traffic scene data with Chinese characteristic for future analysis. In this paper, we propose the methodologies and rules of establishing AD benchmark involving how to configure sensors, how to design the collection schema to show Chinese traffic characteristics and the rules of elaborating distinctive scenes and routes, what to label, and it is also demonstrated that the benchmark can support the future application of extended AD research. Data collection lasted about one month covering diverse scene data such as campus, highway, park, etc. from three representative Chinese cities and driving data from 30 different drivers. Moreover, some statistical results and analyses are produced in accordance with the designed methodologies as instances of potential application. Up to now, the dataset contains about 7,000 labelled image frames and corresponding LiDAR, GPS and Controller Area Network (CAN) data. Labels cover scene type, road user, traffic sign, traffic light, and lane marker. This benchmark can help researchers better understand Chinese traffic situation in aspects of environmental perception, driving behavior analysis, risk assessment, automated vehicle decision and control.