Heuristics‐oriented overtaking decision making for autonomous vehicles using reinforcement learning
Bibliographic record
Abstract
This study presents a three‐lane highway overtaking strategy for an automated vehicle, which is based on a heuristic planning reinforcement learning algorithm. The proposed decision‐making controller focuses on keeping the autonomous vehicle operating safely and efficiently. First, the modelling of the overtaking driving scenario is introduced and the reference approaches named intelligent driver model and minimise overall braking induced by lane changes are formulated. Second, the Dyna‐H algorithm, which combines the modified Q‐learning algorithm with a heuristic planning policy, is utilised for highway overtaking decision‐making. Three different heuristic strategies are formulated to improve learning efficiency and compare performance. This algorithm is applied to determine the lane change and speed selection for an ego vehicle in the environment with uncertainties. Finally, the performance of Dyna‐H is estimated in the autonomous overtaking scenario by comparing it with the reference and traditional learning methods. Furthermore, the Dyna‐H‐enabled decision‐making strategies are validated and analysed in an open‐sourcing driving dataset. Results prove that the proposed decision‐making strategy could produce superior performance in convergence rate and control.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".