With autonomous driving, the system complexity
of vehicles will increase drastically. This requires new ap-
proaches to ensure system safety. Looking at standards like ISO
26262 or ISO/PAS 21448 and their suggested methodologies,
an increasing trend in the recent literature can be noticed to
incorporate uncertainty. Often this is done by using Bayesian
Networks as a framework to enable probabilistic reasoning.
These models can also be used to represent causal relationships.
Many publications claim to model cause-effect relations, yet
rarely give a formal introduction of the implications and
resulting possibilities such an approach may have. This paper
aims to link the domains of causal reasoning and automotive
system safety by investigating relations between causal models
and approaches like FMEA, FTA, or GSN. First, the famous
“Ladder of Causation” and its implications on causality are
reviewed. Next, we give an informal overview of common
hazard and reliability analysis techniques and associate them
with probabilistic models. Finally, we analyse a mixed-model
methodology called Hybrid Causal Logic, extend its idea, and
build the concept of a causal shell model of automotive system
safety
