Abstract

More and more Advanced Driver Assistance Systems (ADASs) are entering the market for improving both safety and comfort. Adaptive Cruise Control (ACC) is an ADAS application that has high interaction with the driver. ACC systems use limited sensor input and have only few configuration possibilities. This may result in the behaviour of the ACC not matching user's preferences in all cases, resulting in lower acceptance of the system. In this work, we examine the possibilities for a Personalised ACC (PACC), which adapts the ACC settings such that it matches the driver preference in order to increase the acceptance. The driver preferred ACC behaviour is predicted using machine learning techniques and manual driving data. On-road experiments showed that the method is promising as it is able to discriminate between two preference clusters with an accuracy of 85%.

Abstract

This paper presents a continuous traffic-flow model for the explorative analysis of advanced driver-assistance systems (ADASs). Such systems use technology (sensors and intervehicle communication) to support the task of the driver, who retains full control over the vehicle. Based on a review of different traffic-flow modeling approaches and their suitability for exploring traffic-flow patterns in the presence of ADASs, kinetic traffic-flow models are selected because of their good representation on both the aggregate level (congestion dynamics) and the level of the individual vehicle (vehicular interactions either directly or through intervehicle communication). The human-kinetic modeling approach is presented. It is a multiclass variant of kinetic traffic-flow models that is strongly based on individual driver behavior, i.e., on fully continuous acceleration/deceleration behavior and explicit modeling of the activation level of the driver. The strength of this modeling approach is illustrated by application to a driver-assistance system that uses intervehicle communication. It warns drivers when approaching sharp decelerations in a queue tail. The explorative analysis shows that the system results in safer and smoother transition from free-flowing to congested traffic. It also avoids compression of the queue tail, thus preventing the emergence of stop-and-go congestion patterns.