Machine Intelligence as a Foundation of Self-Driving Automotive (SDA) Systems
Bibliographic record
Abstract
Machine intelligence is a backbone of self-driving automotive (SDA) systems. Presently, ResNet, DenseNet, and ShuffleNet V2 are excellent convolution choices, whereas object detection focuses on YOLO and F-RCNN design. This study discovers the uniqueness of methods and argues the suitability of using each design in SDA technology. Real-time object detection is imperative in SDA technology, for CNN, as well as to object detection algorithms, an architecture that is a balance between speed and accuracy is important. The most favorable architecture in the scope of this case study would be ShuffleNetV2 and YOLO since both are networks that prioritize speed. But the drawback of speed prioritization is that they suffer from slight inaccuracies. One way to overcome this is to replace the neural network with a more accurate (albeit slower) model. The other solution is to use reinforcement learning to find the best architecture, basically using neural networks to create neural networks. Both approaches are resource-intensive in the sense of capital, talent, and computational budget.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".