Autonomous Learning Intelligent Vehicles Engineering: ALIVE 1.0
Bibliographic record
Abstract
The increasing number of vehicles on roads brings more risks associated with vehicular travel. Nevertheless, with the massive attraction towards self-driving vehicles and the use of artificial intelligence, a trained physical Autonomous Vehicle (AV) is now a major part of transports future. This paper discusses the limitations of the related research based on autonomous vehicles; particularly those who are not taking into account the real-world physics. It also proposes an Autonomous Learning Intelligent Vehicles Engineering, called ALIVE to let each vehicle have additional information about its surroundings in order to get an extended perception of its environment. Moreover, ALIVE car sensors will gather in real-time the required data concerning the vehicles environment which are fused into a learning algorithm predicting the vehicle's response. We tested our algorithm through different mazes to evaluate its efficiency to avoid obstacles and its capacity to adapt to any type of terrain. This has been done to make ALIVE versatile, open source, low-cost and work in any environment. Preliminary results demonstrate the effectiveness of ALIVE in terms of obstacle avoidance and delay minimization. Besides, we hope that our project can be used by other researchers to test their artificial intelligence in the real world instead of keeping it in a simulation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".