  Increasingly sophisticated function development is taking place with the aim
of developing efficient, safe and increasingly Automated Driving Functions.
This development is possible with the use of diverse data from sources such as
Navigation Systems, eHorizon, on-board sensor data, Vehicle-to-Infrastructure
(V2I) and Vehicle-to-Vehicle (V2V) communication. Increasing challenges arise
with the dependency on large amounts of real-time data coming from off-board
sources. At the core of addressing these challenges lies the concept of a
Digital Dependability Identity (DDI) of a component or system. DDIs are
modular, composable, and executable components in the field, facilitating:
  $\bullet$ efficient synthesis of component and system dependability
information,
  $\bullet$ effective evaluation of information for safe and secure composition
of highly distributed and autonomous Cyber Physical Systems.
  In AVL's Connected Powertrain (TM), Automated Driving Functions are tailored
to Powertrain Control Strategies that predictively increase energy efficiency
according to the powertrain type and its component efficiencies.
Simultaneously, the burden on the driver is reduced by optimizing the vehicle
velocity, whilst minimizing any journey time penalty.In this work, the
development of dependable Automated Driving Functions is exemplified by the
Traffic Light Assistant, an adaptive strategy that utilizes predictions of
preceding traffic, upcoming road curvature, inclination, speed limits, and
especially traffic light signal phase and timing information to increase the
energy efficiency in an urban traffic environment. A key aspect of this
development is the possibility for seamless and simultaneous development; from
office simulation to human-in-the-loop and to real-time tests that include
vehicle and powertrain hardware. Driver's acceptance and comfort is rated in an
advanced diver simulator mounted on a hexapod, capable of emulating
longitudinal and lateral acceleration of a real vehicle. Test results from
real-time function validation on a Powertrain Testbed are shown, including real
traffic light signal phasing information and traffic flow representation on
Graz city roads.
