Predicting Driver Errors during Automated Vehicle Takeovers
DOI: 10.1177/03611981231159122
archive: archived pipeline: cataloged verified
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Summary
This study addresses the critical safety challenge of predicting driver errors during the transition of control from automated vehicles to human drivers. While existing literature has explored modeling driver behavior during takeovers, there is limited understanding of how to accurately predict takeover performance errors and the optimal data structures, such as window sizes and feature inclusion, for such predictions. The authors aimed to fill this gap by using supervised machine learning to classify driver errors based on granular driving performance, physiological, and glance data collected prior to a takeover event. The research utilized data from a driving simulator experiment involving 64 participants who engaged in automated driving scenarios with SAE Level 2 automation. The study focused on braking response failures, where the automated system failed to respond to a decelerating lead vehicle, necessitating a manual takeover. Data were collected at high frequencies, including driving performance metrics (e.g., speed, acceleration, steering angle), physiological indicators (heart rate, breathing rate, electrodermal activity), and eye-tracking data (glance behavior). After preprocessing and filtering, the dataset comprised 122 complete drives, with 22 labeled as errors and 100 as non-errors. Errors were defined by failures to execute necessary subtasks, such as checking mirrors before lane changes or braking. The researchers extracted 42 features for various time windows ranging from 3 to 300 seconds before the takeover time. Three machine learning algorithms—decision tree, random forest, and support vector machine with a radial basis kernel—were trained and evaluated using five-fold grouped cross-validation. The results demonstrated that the random forest algorithm achieved the best predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.72 when trained on a 3-second window preceding the takeover. This performance was statistically significantly better than random guessing. Longer window sizes, particularly those exceeding 30 seconds, yielded lower AUC values, with some performing no better than random guessing. The study also identified the 10 most important predictors for error classification, highlighting the significance of specific physiological and glance features in the immediate moments before a takeover. The findings suggest that short-term data windows, specifically 3 seconds prior to a takeover, are most effective for predicting driver errors using machine learning models. This insight is significant for the development of automated vehicle systems, as it indicates that real-time monitoring of driver state using physiological and visual attention metrics can enhance the safety of the takeover process. By integrating these predictive algorithms into highly automated systems, manufacturers could potentially provide timely assistive technologies or alerts to mitigate driver errors, thereby improving overall safety during the transition of control.
Key finding
A random forest algorithm trained on a 3-second window of pre-takeover data achieved the highest predictive performance for classifying driver errors, with an area under the receiver operating characteristic curve of 0.72.
Methodology
simulator
Sample size: 64
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- takeover transitions
- automation surprise
- situational awareness
- automation
- human error taxonomy
- mode awareness
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: behavioral performance data
- Theoretical Contribution: computational model, conceptual framework