Zone-of-Interaction Prioritization for Personalized Automated Driving by Considering Visual Attention Preferences

The personalization or customization of automated driving algorithms has attracted great attention because of its potential in enhancing trust and acceptance. This is true for both Advanced Driving Assistance Systems (ADAS) and higher-level functions such as automated driving. Currently, the common practice of personalized algorithm mainly focuses on reproducing users' naturalistic driving operations while still treating them as drivers, even if they are not necessarily in the control loop of driving tasks. Taking a new perspective from the user role transition between manual and automated driving modes, this work focuses on users' preferences in selecting potential interacting agent(s), i.e. the process of Zone-of-Interaction (ZOI) prioritization, based on their visual attention characteristics. Firstly, driving simulator experiments are conducted, and subjects' attention allocation preferences in both automated and manual driving modes are analyzed. Considering personal preferences in time and spatial distributions of attention, Markov Decision Processes (MDPs) are then adopted to model the personalized ZOI prioritization in automated driving across different scenarios. Simulation results based on random settings and real user data show that the proposed model can decide a ranking of potential interacting agents that is consistent with users' personal expectations. Further application in personalized automated driving is conducted, which indicates that the personalized ZOI prioritization model can significantly reduce users' mental workload and enhance user acceptance. Our work can hopefully be applied in developing personalized planning algorithm of automated driving, especially for complex scenarios involving multiple traffic agents.