Verification and Validation Methods for Decision-Making and Planning of Automated Vehicles: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Verification and validation (V&V) hold a significant position in the research and development of automated vehicles (AVs). Current literature indicates that different V&V techniques have been implemented in the decision-making and planning (DMP) system to improve AVs' safety, comfort, and energy optimization. This paper aims to review a range of different V&V approaches for the DMP system of AVs and divides these approaches into three distinct categories: scenario-based testing, fault injection testing, and formal verification. Further, scenario-based testing is categorized into fundamental and advanced approaches based on the interaction between road users in generated scenarios. In this paper, six criteria are proposed to compare and evaluate the characteristics of V&V approaches, which could help researchers gain insight into the benefits and limitations of the reviewed approaches and assist with approach choices. Next, the DMP system is broken down into a hierarchy of modules, and the functional requirements of each module are deduced. The suitable approaches are matched to verify and validate each module aiming at their different functional requirements. Finally, the current challenges and future research directions are concluded.
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.
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) | 0.001 | 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 | 0.001 |
| 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 it