  With safety being one of the primary motivations for developing automated
vehicles (AVs), extensive field and simulation tests are being carried out to
ensure AVs can operate safely on roadways. Since 2014, the California DMV has
been collecting AV collision and disengagement reports, which are valuable data
sources for studying AV crash patterns. In this study, crash sequence data
extracted from California AV collision reports were used to investigate
patterns and how they may be used to develop AV test scenarios. Employing
sequence analysis, this study evaluated 168 AV crashes (with AV in automatic
driving mode before disengagement or collision) from 2015 to 2019. Analysis of
subsequences showed that the most representative pattern in AV crashes was
(collision following AV stop) type. Analysis of event transition showed that
disengagement, as an event in 24 percent of all studied AV crash sequences, had
a transition probability of 68 percent to an immediate collision. Cluster
analysis characterized AV crash sequences into seven groups with distinctive
crash dynamic features. Cross-tabulation analysis showed that sequence groups
were significantly associated with variables measuring crash outcomes and
describing environmental conditions. Crash sequences are useful for developing
AV test scenarios. Based on the findings, a scenario-based AV safety testing
framework was proposed with sequence of events embedded as a core component.
