"Safety in Mixed Traffic Environment"
Safety in Mixed Traffic Environment
Sprache des Titels:
Safety is an essential target of the automation of driving functions. Safety is usually
understood mainly as primary safety, avoiding being involved in accidents, but secondary
safety is important as well, i.e., not inducing risks to other vehicles. While
primary safety is always considered in Advanced Driver Assistance Systems (ADAS)
development, secondary safety is usually not. The key question of this thesis: should
it be considered? and if so, how?
To answer these questions, we first start by noting that automation is taking place
mainly in form of single ADAS. While it is widely expected that this will eventually
lead to a fully automated vehicle, in the meantime partly or even fully automated
vehicles will have to share the driving space with human-driven vehicles. To test our
ideas, we concentrate on an example, a highway merging assistant, which is potentially
one of the most dangerous situations on highways. We then compare two versions of
this ADAS, one which considers only the safety of the controlled vehicle and another
one that tries to minimize the disturbance and so the risk to the next participants.
The results show that it indeed makes sense to include secondary safety in the design
of the ADAS algorithm, but even more for another aspect, the fluidity of traffic.
Including secondary safety means asking our ADAS to avoid behavior that might be
unexpected and/or disturbing for human-driven vehicles and lead to a risky reaction.
Defining such acceptable behavior is not a trivial task, as decisions by a human driver
will depend on many factors, some of them time-varying but quantifiable ones, like
weather and traffic conditions, but also very much on less crisp ones like the local
habits. Against this background, this thesis proposes a double layer model of human
driving behavior, in which a first decision level can be tuned to the actual situation
while a second actuation level is independent of it. Data sets from different countries
and driving conditions confirm the plausibility of this hypothesis. Then acceptable
behavior can be defined in several ways but essentially as the inversion of the model.