Andreas Korthauer

HMI Strategy – Warnings and Interventions

One of the most important aims of driver assistance systems is the prevention of accidents. Onboard-sensors, algorithms, and other technologies allow developing strategies to achieve this, for example by warning drivers in critical situations. Depending on the remaining time, different driver reactions may have to be elicited. In some situations it may be sufficient to slow down well in advance or to change lanes. However, in other situations only an emergency braking or a fast evasive manoeuvre can prevent a collision. Since in most situations drivers are still in control of the car, the question arises of how to best support them in these kinds of situations. This encompasses two basic HMI aspects: (1) How can the required reactions be elicited in drivers, before the system has to intervene automatically due to the increased criticality and reduced time left? In such a very critical situation, (2) how can drivers be explained how and why the assistance system has taken over and intervened? Within the scope of the UR:BAN project, HMI concepts were developed and evaluated with regard to these aspects of intervention and warning strategies. The chapter gives an overview about the conducted studies and resulting HMI concepts.

Predicting Strategies of Driving in Presence of Additional Visually Demanding Tasks: Inverse Optimal Control Estimation of Steering and Glance Behaviour Models

Driver distraction strongly influences the accident risk on both motorways and urban streets. In this context, visual distraction - long glances off the road - is the most contributing factor. However, in natural driving engagement in visually distracting activities is very frequent compared to the small number of critical incidents. This indicates that drivers apply situational-adaptive gaze and driving strategies that can provide a certain amount of driving safety. Yet, most state-of-the-art mitigation systems assess driver distraction based on fixed thresholds on glance duration. This chapter presents an approach for prediction of situation specific human behaviour in distracted driving. Here, we apply a driver model based on sub-optimal control. Taking into account driver strategies and their potential insufficiencies in the current driving context, our method has the potential to greatly improve assistance systems, by reducing unneeded warnings and interventions. This holds true especially in urban scenarios that are characterized by a broad variety of driving situations.