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

arXiv: https://arxiv.org/abs/2111.03511 · HTML: https://mlsanity.com/paper/2111.03511/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals](https://mlsanity.com/paper/1911.10422/) (2019)
- [CrowdTruth 2.0: Quality Metrics for Crowdsourcing with Disagreement](https://mlsanity.com/paper/1808.06080/) (2018)
- [Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings](https://mlsanity.com/paper/1904.07629/) (2019)
- [Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability](https://mlsanity.com/paper/2012.13603/) (2020)
- [Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving](https://mlsanity.com/paper/2001.04509/) (2020)
- [Using Eye-tracking Data to Predict Situation Awareness in Real Time during Takeover Transitions in Conditionally Automated Driving](https://mlsanity.com/paper/2103.14792/) (2021)
- [Predicting Driver Takeover Time in Conditionally Automated Driving](https://mlsanity.com/paper/2107.09545/) (2021)

## Built on by (most cited first, 3 of 3)

- [Areas of Improvement for Autonomous Vehicles: A Machine Learning Analysis of Disengagement Reports](https://mlsanity.com/paper/2408.00051/) (2024)
- [CRASH: Cognitive Reasoning Agent for Safety Hazards in Autonomous Driving](https://mlsanity.com/paper/2603.15364/) (2026)
- [Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data](https://mlsanity.com/paper/2208.00249/) (2022)

## Abstract (arXiv)

The advancement in machine learning and artificial intelligence is promoting the testing and deployment of autonomous vehicles (AVs) on public roads. The California Department of Motor Vehicles (CA DMV) has launched the Autonomous Vehicle Tester Program, which collects and releases reports related to Autonomous Vehicle Disengagement (AVD) from autonomous driving. Understanding the causes of AVD is critical to improving the safety and stability of the AV system and provide guidance for AV testing and deployment. In this work, a scalable end-to-end pipeline is constructed to collect, process, model, and analyze the disengagement reports released from 2014 to 2020 using natural language processing deep transfer learning. The analysis of disengagement data using taxonomy, visualization and statistical tests revealed the trends of AV testing, categorized cause frequency, and significant relationships between causes and effects of AVD. We found that (1) manufacturers tested AVs intensively during the Spring and/or Winter, (2) test drivers initiated more than 80% of the disengagement while more than 75% of the disengagement were led by errors in perception, localization & mapping, planning and control of the AV system itself, and (3) there was a significant relationship between the initiator of AVD and the cause category. This study serves as a successful practice of deep transfer learning using pre-trained models and generates a consolidated disengagement database allowing further investigation for other researchers.