Adaptive Vehicle Speed Classification via BMCNN with Reinforcement Learning-Enhanced Acoustic Processing
Abstract
Traffic congestion remains a pressing urban challenge, requiring intelligent transportation systems for real-time management. We present a hybrid framework that combines deep learning and reinforcement learning for acoustic vehicle speed classification. A dual-branch BMCNN processes MFCC and wavelet features to capture complementary frequency patterns. An attention-enhanced DQN adaptively selects the minimal number of audio frames and triggers early decisions once confidence thresholds are reached. Evaluations on IDMT-Traffic and our SZUR-Acoustic (Suzhou) datasets show 95.99% and 92.3% accuracy, with up to 1.63x faster average processing via early termination. Compared with A3C, DDDQN, SA2C, PPO, and TD3, the method provides a superior accuracy-efficiency trade-off and is suitable for real-time ITS deployment in heterogeneous urban environments.
Cite
@article{arxiv.2509.00839,
title = {Adaptive Vehicle Speed Classification via BMCNN with Reinforcement Learning-Enhanced Acoustic Processing},
author = {Yuli Zhang and Pengfei Fan and Ruiyuan Jiang and Hankang Gu and Dongyao Jia and Xinheng Wang},
journal= {arXiv preprint arXiv:2509.00839},
year = {2025}
}