Situation Awareness for Driver-Centric Driving Style Adaptation
Johann Haselberger Bonifaz Stuhr Bernhard Schick Steffen Müller
There is evidence that the driving style of an autonomous vehicle is
important to increase the acceptance and trust of the passengers. The driving
situation has been found to have a significant influence on human driving
behavior. However, current driving style models only partially incorporate
driving environment information, limiting the alignment between an agent and
the given situation. Therefore, we propose a situation-aware driving style
model based on different visual feature encoders pretrained on fleet data, as
well as driving behavior predictors, which are adapted to the driving style of
a specific driver. Our experiments show that the proposed method outperforms
static driving styles significantly and forms plausible situation clusters.
Furthermore, we found that feature encoders pretrained on our dataset lead to
more precise driving behavior modeling. In contrast, feature encoders
pretrained supervised and unsupervised on different data sources lead to more
specific situation clusters, which can be utilized to constrain and control the
driving style adaptation for specific situations. Moreover, in a real-world
setting, where driving style adaptation is happening iteratively, we found the
MLP-based behavior predictors achieve good performance initially but suffer
from catastrophic forgetting. In contrast, behavior predictors based on
situationdependent statistics can learn iteratively from continuous data
streams by design. Overall, our experiments show that important information for
driving behavior prediction is contained within the visual feature encoder. The
dataset is publicly available at
huggingface.co/datasets/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation.