Authors:
Maximilian Schukraft
1
;
Susanne Rothermel
2
;
Juergen Luettin
2
and
Lavdim Halilaj
2
Affiliations:
1
Robert Bosch Cross-Domain Computing, Renningen, Germany
;
2
Robert Bosch Corporate Research, Renningen, Germany
Keyword(s):
Context-aware Difficulty Estimation, Difficulty in Driving Scenarios, Context-aware, Human-Machine-Interaction.
Abstract:
The task of safe driving poses a huge challenge for drivers in day to day driving situations. Many times, this task can be very difficult, e.g., due to dense traffic, bad weather conditions, or a risky driving maneuver, and thus demand high concentration of the driver. The difficulty level escalates by the ever-increasing infotainment offers inside vehicles or distractions caused by occupants thus making substantial contribution to the driver distraction. This often results in dangerous driving situations which could be avoided by Advanced Driver Assistance Systems or highly automated driving systems taking the situation difficulty into account. E.g., an incoming phone call is postponed during a difficult situation. However, current systems do not consider all factors that influence the difficulty of a given situation. In this paper, we present an approach for estimating the difficulty of a driving situation by combining a number of different factors, such as environmental, inside-ve
hicle, driver state and personal characteristics, respectively. Our approach follows a rule-based paradigm to make the difficulty estimation reproducible and adjustable to current traffic rules. It is based on a generic and modularized architecture to allow integration and abstraction from heterogeneous data sources. Further, a feedback is provided to the driver or system to explain the contribution of the various factors to the difficulty status. Finally, we demonstrate the capability of the proposed approach with concrete examples, where we estimate the difficulty in various driving scenarios and for different drivers.
(More)