  Autonomous driving systems (ADSs) must be sufficiently tested to ensure their
safety. Though various ADS testing methods have shown promising results, they
are limited to a fixed set of vehicle characteristics settings (VCSs). The
impact of variations in vehicle characteristics (e.g., mass, tire friction) on
the safety of ADSs has not been sufficiently and systematically studied.Such
variations are often due to wear and tear, production errors, etc., which may
lead to unexpected driving behaviours of ADSs. To this end, in this paper, we
propose a method, named SAFEVAR, to systematically find minimum variations to
the original vehicle characteristics setting, which affect the safety of the
ADS deployed on the vehicle. To evaluate the effectiveness of SAFEVAR, we
employed two ADSs and conducted experiments with two driving simulators.
Results show that SAFEVAR, equipped with NSGA-II, generates more critical VCSs
that put the vehicle into unsafe situations, as compared with two baseline
algorithms: Random Search and a mutation-based fuzzer. We also identified
critical vehicle characteristics and reported to which extent varying their
settings put the ADS vehicles in unsafe situations.
