THE CHALLENGE WE TACKLE.
We offer a software library for efficient development of driver models.
These driver models are able to predict driving decisions, actions and driving styles of human drivers.
They can be used to adapt automated assistance and automated functions in vehicles
to the individual driver and to the current traffic situation.
Driver models are an innovative technology for the monitoring, understanding,
assessing and anticipating human drivers.
The car industry strives to realize the dream of automated driving.
An important challenge is to not forget the driver. Will the driver accept to be driven automatically?
Will the driver be scared when the car drives very different from own personal style?
Will the driver trust the automated car? Will the driver understand when he has to take over?
Trust and understanding can only be developed if the automated car communicates and interacts with the driver in an intuitive and transparent way.
an overtaking assistant should warn the driver only if (s)he really wants to overtake - this will avoid confusion,
an automated car should drive in a way that fits the preferences of the driver - this will increase trust.
Despite its importance, support for driver modelling
has been limited so far. General purpose suites like R, MATLAB, Simulink and SCADE can be used for
some aspects of driver modelling but lack the overarching toolchain to support a rapid development
from raw data towards executable models and tangible results.
WHAT WE OFFER.
Humatects offers a software library for the development, utilization, and machine
learning of driver models based on Dynamic Bayesian Networks – Driver Modelling
With DMS, the user can efficiently develop driver models by reusing ready-made
parameters and model structures for a variety of use cases, including driver intention
recognition, traffic prediction and autonomous control. Developed using the feedback of
automotive companies concerning unique Automotive requirements and use cases, DMS
comes with the following advantages:
variety of predefined psychologically motivated parameters and distribution
ready-made machine-learning algorithms for parameter and structure learning,
tools and applications for data pre-processing and annotation,
tools and applications for runtime utilization, visualization, evaluation and
diagnostics of models and parameters,
step-by-step example workflows allow for an easy access for beginners,
powerful API that provides all freedom for experts,
smooth integration into third-party middleware (e.g. RTMaps) and driving
simulators (e.g. SiLAB, Scanner).
DMS even offers algorithms for incremental learning of driver models during runtime. This functionality supports adaptation of the model to the indivdiual driver.
Our customers are automotive suppliers and OEMs who want to apply driver models to develop individualized assistance and automated vehicle systems.