Decision-Making for Autonomous Vehicles in Random Task Scenarios at Unsignalized Intersection Using Deep Reinforcement Learning
Bibliographic record
Abstract
This study constructs a decision-making control framework for autonomous ego vehicles (AEV) based on Soft Actor-Critic (SAC) in a random driving task scenario at an unsignalized intersection. The environment vehicles include both AEV and surrounding vehicles, and the three driving tasks through unsignalized intersections are going straight, left turn, and right turn. Since the driving tasks of AEV and surrounding vehicles are random, the environment is characterized by high uncertainty and difficulty. There are three innovative points in this paper. First, this paper proposes a new Mix-Attention Network based on the attention mechanism. Second, this paper improves the state by introducing a new input quantity to represent the driving task of the vehicle itself. Third, this paper has been enhanced in replay buffer, using more collision and arrival experiences to train the neural network. In this paper, the performance of the original and improved models is evaluated in terms of safety and efficiency. The simulation results show that all three proposed improvement methods can improve performance and achieve better results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".