Abstract

Simulating and predicting behaviour of human drivers with Digital Human Driver Models (DHDMs) has the potential to support designers of new (partially autonomous) driver assistance systems (PADAS) in early stages with regard to understanding how assistance systems affect human driving behaviour. This paper presents the current research on an integrated driver model under development at OFFIS within the EU project ISi-PADAS. We will briefly show how we integrate improvements into CASCaS, a cognitive architecture used as framework for the different partial models which form the integrated driver model. Current research on the driver model concentrates on two aspects of longitudinal control (behaviour a signalized intersections and allocation of visual attention during car following). Each aspect is covered by a dedicated experimental scenario. We show how experimental results guide the modelling process. Document type: Part of book or chapter of book

Abstract

This study presents a numerical model that describes the dynamic process of building situation awareness after an automation-initiated transition. The model predicts the level of situation awareness as a function of elapsed time since the transition, and is verified using data from an experiment in which participants watched animated video clips of automated driving scenarios. Additionally, the ‘number of fixations per second’ is suggested for real-time monitoring of situation awareness in automated driving.

Abstract

This study presents a numerical model that describes the dynamic process of building situation awareness after an automation-initiated transition. The model predicts the level of situation awareness as a function of elapsed time since the transition, and is verified using data from an experiment in which participants watched animated video clips of automated driving scenarios. Additionally, the ‘number of fixations per second’ is suggested for real-time monitoring of situation awareness in automated driving.

Abstract

International audience; Nowadays pedestrian detectors are fast, scale-robust and quite efficient. Embedded within a UAV such a detector would open new possibilities. In this paper the very well known HOG detector is adapted for UAV use and a new kind of training dataset is proposed in order to increase the detector's angular robustness. A more appropriate set of detection windows, together with a new detection pipeline, is proposed in order to reduce the search space and consequently reduce the computation time. Tests conducted using the improved detector show significantly better results on aerial images.