  Since 2014, the California Department of Motor Vehicles (CDMV) has compiled
information from manufacturers of autonomous vehicles (AVs) regarding factors
that lead to the disengagement from autonomous driving mode in these vehicles.
These disengagement reports (DRs) contain information detailing whether the AV
disengaged from autonomous mode due to technology failure, manual override, or
other factors during driving tests. This paper presents a machine learning (ML)
based analysis of the information from the 2023 DRs. We use a natural language
processing (NLP) approach to extract important information from the description
of a disengagement, and use the k-Means clustering algorithm to group report
entries together. The cluster frequency is then analyzed, and each cluster is
manually categorized based on the factors leading to disengagement. We discuss
findings from previous years' DRs, and provide our own analysis to identify
areas of improvement for AVs.
