An Overview of Deep Learning Techniques for Autonomous Driving Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
The data to train autonomous cars was not so abundant before a few years. After Waymo released their driving data, it is widely used in academic research. It consists of a huge amount of great quality images which is collected from various driving scenarios. Reliable and authenticated driving policies have crucial roles to develop efficient automated driving systems. This becomes one of the fundamental challenges for researchers to find the precise solution for the same. Academic researchers need to make several assumptions for the implementation of automated vehicle parts in their simulations or models. These assumptions may not be relevant to the real-time interactions as per the simulation-based research. Since the internal driving policy is under proprietary protection, researchers need to design robust and reliable policies to implement automated driving parts using deep learning models. This paper analyzes several deep learning systems to learn autonomous driving behavior using Waymo's dataset. In addition, this article provides an extensive overview of different aspects of designing automated driving simulators.
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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 | 0.001 | 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