Hierarchical Deep Reinforcement Learning with Cross-attention and Planning for Autonomous Roundabout Navigation
Bibliographic record
Abstract
Autonomous vehicle control is an important subfield of autonomous vehicle research. Many challenges remain to improve the safety and performance of autonomous vehicle control systems in urban driving environments. One such urban driving environment is the roundabout junction, which presents its own unique challenges to potential solutions to autonomous vehicle control. This paper proposes and tests a vehicle control agent as a candidate solution for urban roundabout navigation. The vehicle control agent is based on a hierarchical deep reinforcement learning architecture with a superior network selecting short-term lane-change behaviour and a subordinate network selecting longitudinal acceleration values. The road sequence followed by the agent is selected by a route planner based on Dijkstra’s algorithm. The proposed agent learns to navigate the roundabout environment safely, reaching the goal state in 100% of validation scenarios after training. The agent also outperforms an agent based on the Krauß-following model in 2 out of 5 tested metrics and matches the performance of the Krauß-following model in the remaining 3 metrics.
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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 | 0.000 | 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".