cs.AIMar 19, 2026

Bridging Network Fragmentation: A Semantic-Augmented DRL Framework for UAV-aided VANETs

Authors: Gaoxiang Cao, Wenke Yuan, Huasen He, Yunpeng Hou, Xiaofeng Jiang, Shuangwu Chen, Jian Yang

Organizations: Department of Automation, University of Science & Technology of China

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Abstract

Urban Vehicular Ad-Hoc Networks (VANETs) can become fragmented because buildings obstruct wireless links and vehicle mobility continuously changes the network topology. Unmanned Aerial Vehicles (UAVs) can serve as mobile relays, but Deep Reinforcement Learning (DRL)-based deployment often suffers from inefficient exploration because it lacks road-topology guidance. To address this problem, we propose Semantic-Augmented DRL (SA-DRL), which models network fragmentation over the road topology and aligns a pretrained Large Language Model (LLM) to generate a topology-dependent action prior from dynamic traffic states. The resulting Semantic-Augmented PPO (SA-PPO) algorithm combines this prior with the PPO policy through Logit Fusion, guiding exploration toward promising intersections while retaining adaptation through environmental returns. Simulations driven by real-world urban trajectories show that SA-PPO reaches the final converged reward of Vanilla PPO using only 28.6% of its training episodes. It improves the average number of vehicles in connected components and the average connected-component size by 7.9% and 8.7%, respectively, while reducing UAV energy consumption by 21.3%.

Figures & tables

Fig. 1: UAV Aided VANET Scenario.
Fig. 2: The four-stage pipeline of the SA-DRL framework.
Fig. 3: Schematic diagram of the LoRA fine-tuning mechanism.
Fig. 4: Schematic diagram of the SA-PPO training and execution mechanism based on Logit Fusion.
SymbolParameterValue
τ\tauTime-slot duration100 s
TτT_{\tau}Total task duration5000 s
vuv_{u}UAV flight speed20 m/s
ε1\varepsilon_{1}UAV hovering power100 W
ε2\varepsilon_{2}UAV communication power5 W
ε3\varepsilon_{3}UAV propulsion power200 W
TABLE I: Simulation Environment Parameters
Fig. 5: The RTG corresponding to the road network used in simulation.
LLMQwen2.5Qwen2.5Qwen3Llama3.2Gemma3
Parameters3B3B4B3B4B
AlignmentPrompt-onlyLoRALoRALoRALoRA
BLEU-40.7975.1070.3244.6455.50
ROUGE-L18.5581.1978.4269.8174.84
Kendall’s τ\tau–86.1478.8285.6088.89
JSON PSR0.00100.00100.00100.00100.00
TABLE II: Performance Comparison of Prompt-Only and LoRA-Based Knowledge Alignment. Kendall’s τ\tau , JSON PSR, and HR10HR_{10} are reported as percentages.
Fig. 6: Inference efficiency comparison for different LLM architectures as batch size varies.
Fig. 7: Heatmap comparison of action scores predicted by different fine-tuned LLMs against ground truth.
Fig. 8: Comparison of action score predictions by Qwen2.5-3B against ground truth.
Fig. 9: Training reward curves for different algorithms.
Fig. 10: Comparison of VANET connectivity and UAV energy consumption metrics.
Fig. 11: UAV trajectory visualization under different algorithms.
Fig. 12: Ablation study results comparing SA-PPO with its variants.
Fig. 13: Sensitivity of SA-PPO to the semantic fusion coefficient λ\lambda . The reward and connectivity metrics are normalized by their values under the default setting λ=0.5\lambda=0.5 , while the UAV energy consumption is reported in absolute values. “Decay” denotes a schedule that progressively reduces λ\lambda during training.
Method08:0010:0012:0014:0016:0018:00
Average number of vehicles in connected components ↑\uparrow
Soft Actor-Critic36.159.344.976.382.160.7
Vanilla PPO38.458.244.074.786.262.2
GAT-PPO37.158.644.575.178.861.4
SA-PPO40.459.951.877.484.563.9
Average connected-component size ↑\uparrow
TABLE III: Temporal robustness under different task start times. The best result at each start time is shown in bold.
Fig. 14: Training performance on the Jinan road network.
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