Modelling soft inputs for human-driven vehicles in an urban traffic context

Abstract

Studies have shown that the advantages of fully automated traffic namely increased safety and efficiency, can be negated in mixed traffic scenarios by even a small proportion of human drivers. In addition to human driving and perception errors, this effect is caused by the lack of- or ineffective communication between the automatic intersection control system and the human drivers. Typically, this is only done via classical traffic lights. This work proposes a system which extends the interaction possibilities between human-driven vehicles (HDVs) and an intelligent intersection with warnings and manoeuvre recommendations, such as lane change or speed recommendations (cf. Green Light Optimal Speed Advisory (GLOSA)) or safety warnings. These so-called soft inputs enable HDVs to be integrated more efficiently into intelligent intersection systems. The human response to such recommendations, based on expert knowledge and naturalistic data, is modelled and simulated to determine the potential of connecting human drivers to an intelligent intersection control system. A suitable formulation of manoeuvre recommendations for a conflicting unprotected left turn scenario is outlined and tested in simulation studies in a typical urban intersection scenario. The analysis of the relevant performance indicators shows the high achievable performance and its trade-off characteristics regarding HDV penetration rate and compliance behaviour.