Research and Deduction of Car-to-TW Vehicle AEB Test Scenarios Based on Improved Clustering Methods

Abstract

Two-wheeled (TW) vehicle accidents are one of the major types of urban traffic accidents. TW cyclists who lack safety protection usually suffer more serious injuries and deaths in collisions. Developments in automotive active safety technologies are expected to reduce cyclist injuries and deaths, such as automatic emergency braking (AEB). To facilitate the development and testing of AEB technology, typical TW vehicle scenarios need to be constructed. Based on 400 cases of car-to-TW vehicle accident data from the National Automobile Accident In-Depth Investigation System (NAIS) database, we investigated the scenario elements that influence AEB robustness, such as weather, accident time, and road wetness. We obtained seven static scenarios using an improved clustering method, and we obtained specific speed and distance combinations in each scenario using a deduction method. Further, we compared the present findings to those of other scholars and the China New Car Assessment Program (C-NCAP). The kinematic states of the two were similar to that of C-NCAP, but the speed distribution was significantly different. The TW vehicle speed in the C-NCAP is set to 15 km/h or 20 km/h concerning the European test scenarios, but the TW vehicle speed in the present study was 10–60 km/h. Thus, the present findings recommended that subsequent C-NCAP test scenarios increase the category of motorcycles and the speed range of cars covering 20–70 km/h and consider the test conditions of bad weather and wet roads, to test the robustness of AEB.