VWP:An Efficient DRL-Based Autonomous Driving Model
Bibliographic record
Abstract
In this paper, a novel DRL-based model (VWP, VAE-WGAN-PPOE) is proposed to solve the problem of long training time and unsatisfactory training effect in the end-to-end autonomous driving. The model is optimized from feature extraction and algorithm decision. In feature extraction, we encode the input video by combining variational auto encoder (VAE) with wasserstein generative adversarial network (WGAN). The state dimension is reduced and the problem of mode collapse and gradient disappearance caused by generative adversarial network (GAN) training is solved. In decision algorithm, we formulate a new reward function by analyzing the factors affecting driving performance. Furthermore, we propose an enhanced algorithm PPOE based on the proximal policy optimization (PPO). In the CARLA simulator, compared with CNN and ResNet34, the convergence speed of the DRL model based on VAE-WGAN increases by 26.1% and 20.3%, the navigation task completion rate increases by 18.5% and 9.2%, and the collision rate decreases by 13.6% and 9.4%. Compared with deep deterministic policy gradient (DDPG) decision algorithm, the convergence speed of the DRL model based on PPOE increases by 23.3%, the navigation task completion rate increases by 5.0% in sunny days and 8.4% in severe weather, the collision rate decreases by 3.5% in sunny days and 6.6% in severe weather. Extensive experiments show that the proposed model enables the agent to drive safely along the navigational route in the complex environment with pedestrian and vehicle interaction, even in severe weather.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".