Finding better learning algorithms for self-driving cars: An overview of the LAOP platform
Bibliographic record
Abstract
Cars are becoming more and more intelligent, embedded with a range of sensors to give them local perception of their environment (LIDARs, cameras, etc.). Trendy companies like Google and Tesla are actively testing cars on American roads that can drive without any human interaction [1]. Neural networks are the modern approach for autonomous cars. However, an inefficient neural network algorithm will make the learning process slower and will result in a less reliable autonomous vehicle. In this paper, we will introduce a platform built in JAVA named LAOP (Learning Algorithm Optimization Platform) [2] while explaining the solutions we found to make it easy for researchers to test and compare their own algorithms. Then, we will show how we have integrated a natural selection algorithm with a neural network in order to improve them. Moreover, we will demonstrate how the Fully Connected Neural Network and the NeuroEvolution of Augmenting Topologies (NEAT) [3] algorithms are implemented in the context of vehicular learning on LAOP. Finally, we will display the different results extracted from LAOP by tuning several various parameters such as the weight mutation chance and the car density in the 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".