Communication-Aware Hierarchical Driving Control for Collaborative Autonomous Driving
Bibliographic record
Abstract
Collaborative autonomous driving holds significant potential to improve the performances of Connected Autonomous Vehicles (CAVs). This paper presents a communication-aware hierarchical driving control mechanism designed to operate under non-ideal Vehicle-to-Vehicle (V2V) communication conditions. To address the impact of delayed and partial observations of CAV, a state augmentation method is first introduced to convert the resulting random-delay partially observable Markov decision process (RD-POMDP) into a standard Markov decision process (MDP), enabling the application of deep reinforcement learning (DRL) algorithms with theoretical convergence guarantees. Based on this formulation, a hierarchical DRL framework is developed, comprising an event-triggered upper-level for driving behavior adaptation at a coarse time scale and a periodic lower-level for motion control at a fine time scale. A modified Twin Delayed Deep Deterministic Policy Gradient with Prioritized Experience Replay (TD3-PER) algorithm is used to train the lower-level motion control policy, while an option-critic framework is employed to train the upper-level behavior policy, leveraging the pretrained low-level policies. Simulation results demonstrate the effectiveness of the proposed mechanism in collaborative driving scenarios with imperfect V2V communications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| 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".