Risk perception and the warning strategy based on microscopic driving state

Evaluation of Drivers’ Interaction Ability at Social Scenarios: A Process-Based Framework

“…Quantifying the risk of moving objects using indices based on time or distance is a common and effective approach. Time-based quantifying indices include Time to Collision (TTC), Time Headway (THW), Time Margin (TM), and Post Encroachment Time (PET) (Zhao et al 2018;Xiong et al 2019;Liu, He, and Zhu 2019;Qi et al 2020), while distance-based indices commonly used are stopping distance and safety margin (Doi and A 1994;Shashua 2017, 2018).…”

Driver models for the definition of safety requirements of automated vehicles in international regulations. Application to motorway driving conditions

“…One of the most widely used safety metrics of TCT is the Time-To-Collision (TTC) ( Laureshyn et al, 2016 , Mahmud et al, 2019 , Wang and Stamatiadis, 2014 ). Using TTC, different traffic states can be identified in the famous NGSIM database ( Kovvali et al, 2007 ), with the states shown to correspond to different levels of risk ( Zhao et al, 2018 ). The potential risk for human drivers in dangerous car following situations can be assessed ( Liu et al, 2019 ).…”

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

“…Due to the non-negative function's value, the risk, which is represented by the potential field value, produced by different elements can be superposed [29]. The microscopic motion state of the vehicle holds a pivotal impact on the vehicle risk [30], thus rendering the establishment process of the artificial potential field model inseparable from the driving state of the vehicle. Therefore, the establishment process of the artificial potential field model is inseparable from the driving state of the vehicle.…”