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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".