Mass‐based omni‐directional risk indicator (MORI) for multi‐participant traffic

Abstract

Abstract This study focuses on the potential of connected and automated vehicles (CAVs) to enhance road traffic safety through the provision of rich physical motion state information. Real‐time risk indicators are crucial for improving driving safety and must be tailored to the specific characteristics of the CAV environment. To this end, this paper introduces the concept of “risk pair” to decompose the joint actions of multiple traffic participants into pairwise interactions. An omnidirectional risk indicator (ORI) is developed to describe the effect of “risk pairs”, and the superposition of ORI based on quality is proposed as the mass‐based ORI (MORI). A comparison between MORI and existing risk indicators shows that MORI has good performance in quantifying one‐dimensional scenarios. In two‐dimensional scenarios involving multiple participating entities, MORI provides two quantitative results: vector summation and scalar summation. The trajectory data from the next generation simulation database is used to validate the MORI model. The results show that both vector summation and scalar summation of MORI demonstrate strong risk quantification capabilities with different directions. Although some limitations of MORI still exist, this approach will provide a scientifically effective means of risk quantification for two‐dimensional complex scenarios.

Keywords

• accident prevention
• automated driving & intelligent vehicles