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not shown in the figure are property monitors (or observer, see Sec. 3 for
details) and a recorder. The co-simulation is executed on a cluster of standard
PCs.
Each component model evolves in discrete time steps, with update fre-
quency resolutions in the order of
Hz. Synchronisation and data
exchange is managed by the RTI. The output behaviour of models of the
ADAS, the ego car and the environment depends deterministically on their
input, where the trac environment is parameterized by scripts defining the
street layout and number and actions of other cars. A complete run of the
scenario consists on average of about 2700 discrete time steps.
The IMoST cognitive driver model consist of two parts: 1) The cogni-
tive architecture CASCaS, which integrates task-independent human cogni-
tive processes, e.g. a model of visual perception, declarative and procedural
memory models and a processor for task knowledge. 2) A formal model of
driving-task specific knowledge (e.g. knowledge about different driving ma-
noeuvres or tra
c rules), which is “uploaded” onto the architecture for simu-
lation purposes. Thus, a cognitive architecture can be understood as a generic
interpreter that executes task-specific knowledge in a psychologically plausi-
ble way.
The driver model incorporates different types of behavioural variation
concerning acceleration style (sportive vs. relaxed), gaze strategies (varia-
tions in gaze duration and frequency) and safety margins (preferred distance
to lead car / rear car), which where assessed through a series of experi-
ments with subjects. During simulation the model probabilistically chooses
amongst those different behaviours. These probabilistic capabilities are of
specific interest when thinking about guided simulation, because the prob-
abilistic choice will be replaced by a systematic variation of the possible
behaviours.
20
to
35
3
Property Specification
The properties of interest are defined in a first-order version of linear temporal
logic. In their atoms, the formulas may thus refer to attributes of the system
constituents like car positions, their speed, visible actions of the driver and
so on, complementing discrete observations (e.g., turn indicator, assistance-
system signals). The usual temporal operators (always, eventually, unless,
until) permit to express temporal relations of their occurrence. Formulas are
evaluated over complete traces which usually originate from the simulation
environment but might also be recordings of driver tests. With the temporal
operators one can formulate specific requirements on different situations and
phases of driving.
Extending the usual interpretation of logics, we chose a nonstandard,
quantitative semantics [2], [7] which assigns a numerical value to a formula
for each trace: A positive number means that the formula is satisfied, and
the result value gives the minimal distance in variable values which would
