标题： 基于BMCNN的自适应车辆速度分类方法，结合增强型声学处理的强化学习 显示英文标题

标题： Adaptive Vehicle Speed Classification via BMCNN with Reinforcement Learning-Enhanced Acoustic Processing

摘要： 交通拥堵仍然是一个紧迫的城市挑战，需要智能交通系统进行实时管理。 我们提出了一种混合框架，结合深度学习和强化学习用于声学车辆速度分类。 双分支BMCNN处理MFCC和小波特征以捕捉互补的频率模式。 一种注意力增强的DQN自适应地选择最少的音频帧，并在置信度阈值达到时触发早期决策。 在IDMT-Traffic和我们的SZUR-Acoustic（苏州）数据集上的评估显示准确率分别为95.99%和92.3%，通过早期终止平均处理速度提高了1.63倍。 与A3C、DDDQN、SA2C、PPO和TD3相比，该方法提供了更优的准确率-效率权衡，适用于异构城市环境中的实时ITS部署。 显示英文摘要

摘要： 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.