用强化学习优化音频处理，实现快速准确的车辆速度分类。
Adaptive Vehicle Speed Classification via BMCNN with Reinforcement Learning-Enhanced Acoustic Processing
- 双分支BMCNN融合梅尔频谱与小波特征，捕捉互补频率模式。
- 注意力DQN动态选择最少音频帧，达95.99%准确率并提速1.63倍。
- 适合城市复杂环境下的实时交通系统部署，兼顾精度与效率。
交通拥堵仍是城市亟待解决的问题，需依赖智能交通系统进行实时管理。本文提出一种融合深度学习与强化学习的混合框架，用于声学车辆速度分类。双分支BMCNN同时处理MFCC和小波特征，以捕获互补的频率模式。注意力增强型DQN自适应选择最少音频帧，并在置信度达标时触发提前决策。在IDMT-Traffic和我们自建的SZUR-Acoustic（苏州）数据集上的评估显示，准确率分别达到95.99%和92.3%，平均处理速度提升最高达1.63倍。相比A3C、DDDQN、SA2C、PPO和TD3，该方法在准确率与效率之间取得更优平衡，适用于异构城市环境中实时ITS部署。
原文摘要 · Abstract (English)
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.
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