Evaluation of ADAS with a supported-Driver Model for desired Allocation of Tasks between Human and Technology Performance

Abstract

Partly automated driving is relevant for solving mobility problems, but also cause concerns with respect to driver's reliability in task performance. The presented supported driver model is therefore intended to answer in which circumstances, what type of support enhances the driver's ability to control the vehicle. It became apparent that prerequisites for performing tasks differ per driving task's type and require different support. The possible support for each driving task's type has been combined with support-types to reduce the error causations from each different performance level (i.e. knowledge-based, rule-based and skill-based performance). The allocation of support in relation to performance level and driving task's type resulted in a supported driver model and this model relates the requested circumstances to appropriate support types. Among three tested ADAS systems, semi-automated parking showed best allocation of support; converting the demanding parallel parking task into a rather routine-like operation.