How to Guarantee Driving Safety for Autonomous Vehicles in a Real-World Environment: A Perspective on Self-Evolution Mechanisms
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
A succession of accidents shows that production vehicles with autonomous driving systems do not work safely in real-world environments, especially when facing unseen scenarios. Therefore, how to ensure that autonomous systems drive more safely becomes a challenge. Thanks to the self-learning ability of human beings, human drivers can gradually learn how to drive from a driving test with typical and finite scenarios to the real world with infinite ones. Analogically, it is believed that accidents can be largely reduced once the designed autonomous vehicles are endowed with a self-learning ability to adapt to the unseen and then to infinite scenarios in the real world. Accordingly, this work proposes a principle to design autonomous systems with a self-evolution feature not just for a single vehicle but for a group. In addition, it describes our development of a self-evolution autonomous system as an illustrative case study of implementing such principles in practice. The ultimate aim is to propose a feasible solution to speed up the design process of a fully safe autonomous system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it