Multi-vehicle anticipation-based driver behavior models: a synthesis of existing research and future research directions

Abstract

Multi-vehicle anticipation (MVA) refers to drivers’ ability to consider stimuli from several vehicles ahead in their maneuvering decisions, such as longitudinal, lateral, and a combination of longitudinal and lateral movements. This paper provides a comprehensive review of MVA-based driver behavior models developed for both homogeneous and heterogeneous disordered (HD) traffic streams. Studies on MVA identify various advantages of incorporating MVA in driver behavior models, such as superior numerical and behavioral soundness, plausible parameter estimates, and model outputs, and improved model realism. In addition, our findings indicate that MVA-based driver behavior models follow a similar pattern of extending the established single-leader car-following models, considering vehicles that are directly ahead (in the same lane), and focussing on a fixed number of vehicles ahead. For HD traffic streams, drivers’ also consider stimuli from vehicles obliquely placed or on either side. Furthermore, this review discusses issues with the current modeling approaches and suggests future research directions