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The advancement in machine learning and artificial intelligence is promotingthe testing and deployment of autonomous vehicles (AVs) on public roads. TheCalifornia Department of Motor Vehicles (CA DMV) has launched the AutonomousVehicle Tester Program, which collects and releases reports related toAutonomous Vehicle Disengagement (AVD) from autonomous driving. Understandingthe causes of AVD is critical to improving the safety and stability of the AVsystem and provide guidance for AV testing and deployment. In this work, ascalable end-to-end pipeline is constructed to collect, process, model, andanalyze the disengagement reports released from 2014 to 2020 using naturallanguage processing deep transfer learning. The analysis of disengagement datausing taxonomy, visualization and statistical tests revealed the trends of AVtesting, categorized cause frequency, and significant relationships betweencauses and effects of AVD. We found that (1) manufacturers tested AVsintensively during the Spring and/or Winter, (2) test drivers initiated morethan 80% of the disengagement while more than 75% of the disengagement were ledby errors in perception, localization & mapping, planning and control of the AVsystem itself, and (3) there was a significant relationship between theinitiator of AVD and the cause category. This study serves as a successfulpractice of deep transfer learning using pre-trained models and generates aconsolidated disengagement database allowing further investigation for otherresearchers.

Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline