Risk Assessment and Enhancement Suggestions for Automated Driving Systems through Examining Testing Collision and Disengagement Reports
Bibliographic record
Abstract
The California Department of Motor Vehicles (DMV) reports, including disengagement and collision reports, provide information on each accident or disengagement activity for on-road testing of autonomous driving systems (ADSs) and autonomous vehicles (AVs). Unfortunately, current DMV reports have been misleading in relation to many key details, making it challenging for readers of those reports to discern the events’ root causes and interrelationships. Therefore, appropriate systematic classification methods and principles need to be adopted. We follow an identification method similar to fault tree analysis (FTA) with the help of the driving reliability and error analysis method (DREAM 3.0) and the Haddon matrix to find the potential key accident factors from all disengagement data. We also conduct ADS risk assessments of potential disengagements and genuine accidents classified by traditional accident types. In addition, the automated driving system is composed of various software modules, and a classification method that is suitable from the standpoint of ADS software developers is developed in this paper. Next, we sort out the characteristics of the most frequent accidents based on the risk assessment results. Finally, we propose a workable risk reduction solution according to the characteristics of accidents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".