Evaluating Architecture Impacts on Deep Imitation Learning Performance for Autonomous Driving
Bibliographic record
Abstract
Imitation learning has gained huge popularity due to its promises in different fields such as robotics and autonomous systems. A great deal of past research work in the field of imitation learning has been devoted to developing efficient and effective policies using deep convolutional neural networks (CNNs). The performance of CNN-based control policies intimately depends on the network architecture. Determination of the optimal architecture for CNNs is still a hot research topic for the deep learning community. This study comprehensively investigates and quantifies the impact of CNN architecture on the performance of learned policy for an autonomous vehicle. CNN models with different architectures (number of layers and filters) are fed by visual information from multiple cameras obtained from multiple driving simulations. These networks are trained to precisely find the mapping between visual information and the steering angle. Two ensemble approaches are also introduced to further improve the overall accuracy of steering angle estimations. Obtained results indicate that deeper networks show a better performance than less deep networks during autonomous driving. Also it is observed that best results are achieved by ensemble approaches.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".