An examination of real-world disengagement patterns of automated driving systems in autonomous-mode: A deep-learning method

OBJECTIVE: Widespread adoption of automated driving systems (ADS) depends fundamentally on their operational reliability. Frequent or unexpected disengagements pose a significant barrier by undermining user trust. Although human-automation interaction has been studied, key gaps remain in understanding contemporary disengagement trends, their underlying causes, and the implications of disengagement type (ADS-initiated versus driver-initiated). This paper addresses these gaps to provide insights for future technical development and policy frameworks. METHODS: This analysis utilized 23,505 disengagement reports from the California DMV's Autonomous Vehicle Tester program, covering Automated Driving Systems (ADS) at SAE Levels 3-5 for the period 2019-2022. A deep learning-based natural language processing (NLP) pipeline was developed to extract causal factors from unstructured disengagement text descriptions, followed by manual validation to classify events into seven root-cause categories: Control, Planning, Perception, Software/Hardware, Localization/Mapping, Prediction, and Other Road Users. Temporal trends and associations between disengagement types and causal factors were evaluated using statistical analyses, including logistic regression. RESULTS: < 0.01). Second, the root causes of disengagements exhibited a strong dependence on the initiator. Driver-initiated disengagements were predominantly associated with deficiencies in Perception, Planning, and Localization/Mapping. Conversely, ADS-initiated disengagements were overwhelmingly linked to Control-related failures. Finally, the failure profile of ADS-initiated disengagements evolved significantly between 2019 and 2022. Control-related issues, initially the dominant cause, became rare, while Software/Hardware issues emerged as the new leading cause, occurring at a rate three times that of driver-initiated disengagements by 2022. DISCUSSION: While declining ADS-initiated disengagements signal improved system robustness, persistent failures in Control and emerging Software/Hardware issues indicate unresolved challenges. The divergent causes of driver- versus ADS-initiated events-suggesting preemptive human intervention versus forced system handovers-reveal distinct operational failure modes. The increasing prominence of Software/Hardware problems likely stems from growing system complexity. Consequently, targeted edge-case validation and adaptive trust calibration are critical for future ADS development.