DRNet: A Decision-Making Method for Autonomous Lane Changing with Deep Reinforcement Learning
Bibliographic record
Abstract
Machine learning techniques have outperformed many rule-based methods for the decision-making of autonomous vehicles. Despite recent efforts, lane changing remains a big challenge due to the slow learning rates and possibility of executing unsafe actions. To help improve the state-of-the-art, we propose to leverage emerging Deep Reinforcement learning (DRL) for laNE changing in Tactical level and present ``DRNet", a novel and highly efficient DRL-based framework that both enables a DRL agent to learn to drive by executing reasonable lane changing on simulated highways with an arbitrary number of lanes and overcomes limitations of inefficient learning rates in DRL. Furthermore, to obtain a safe policy for tactical decision-making, DRNet integrates ideas from safety verification, the most important of autonomous driving, to ensure that only safe actions are chosen at any time. With the setting of our state representation and reward function, the trained agent is able to take appropriate actions in a real-world-like simulator. Our DRL agent has the ability to learn the desired task without causing collisions and outperforms DDQN and other baseline models.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".