Jointly Learning V2X Communication and Platoon Control with Deep Reinforcement Learning
Bibliographic record
Abstract
In autonomous vehicle platooning, Vehicle-to-Everything (V2X) communications are leveraged in cooperative adaptive cruise control (CACC) to improve control performance. Since exchanging information at all times incurs significant communication overhead in vehicular networks, it is important to determine when V2X communication is necessary. To solve this problem, we propose a Deep Reinforcement Learning (DRL)-based algorithm named Attention-DDPG, which learns platoon control with Deep Deterministic Policy Gradient (DDPG), and learns when to communicate with an attention network. Specifically, each preceding vehicle is equipped with a deep neural network (DNN), which takes as input its local state and platoon control action and determines whether to transmit its acceleration or not to the following vehicle at each time step. The attention network of a preceding vehicle is trained using the feedback from the following vehicle on the value of V2X information in the form of an advantage function. In order to evaluate Attention-DDPG, simulations are performed using real driving data, and performance is compared with those of two baselines that communicate and do not communicate at all times, respectively. The results demonstrate that Attention-DDPG strikes a competitive tradeoff between control performance and communication overhead while ensuring platoon string stability.
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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 | 0.001 |
| 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".