  Cyclists and pedestrians account for a significant share of fatalities and
serious injuries in the road transport system. In order to protect them,
advanced driver assistance systems are being developed and introduced to the
market, including autonomous emergency braking and steering systems (AEBSS)
that autonomously perform braking or an evasive manoeuvre by steering in case
of a pending collision, in order to avoid the collision or mitigate its
severity. This study proposes a new prospective framework for quantifying
safety benefit of AEBSS for the protection of cyclists and pedestrians in terms
of saved lives and reduction in the number of people suffering serious
injuries. The core of the framework is a novel application of Bayesian
inference in such a way that prior information from counterfactual simulation
is updated with new observations from real-world testing of a prototype AEBSS.
As an illustration of the method, the framework is applied for safety benefit
assessment of the AEBSS developed in the European Union (EU) project PROSPECT.
In this application of the framework, counterfactual simulation results based
on the German In-Depth Accident Study Pre-Crash Matrix (GIDAS-PCM) data were
combined with results from real-world tests on proving grounds. The proposed
framework gives a systematic way for the combination of results from different
sources and can be considered for understanding the real-world benefit of new
AEBSS. Additionally, the Bayesian modelling approach used in this paper has a
great potential to be used in a wide range of other research studies.
