Development of a Lane Change Assistance System Adapting to Driver's Uncertainty During Decision-Making

Abstract

This paper presents the development of a lane change assistance system adapting to both criticality and driver's uncertainty during decision-making in overtaking scenarios on simulated two-lane highways. Based on information about the traffic situation, the proposed system relies on a probabilistic model of driver's uncertainty to classify whether a driver is unsure or not and on a safety analysis based on driver's preferences to suggest appropriate overtaking actions, which together trigger corresponding advices on the Human Machine Interface. Different to existing lane change assistance systems using traffic light colors to encode criticality and warn accordingly, the proposed system uses colored abstract faces with emotional expressions encoding both criticality and driver's uncertainty to provide suggestions to either overtake or decelerate. The proposed system has been implemented in a driving simulator. The qualitative results of a not yet analyzed evaluation study with 20 participants show that the proposed system is more accepted and trusted than reference systems that do not consider driver's uncertainty.