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High-emitter identification for heavy-duty vehicles by temporal optimization LSTMand an adaptive dynamic threshold

Author(s): Zhenyi XU, Renjun WANG, Yang CAO, Yu KANG
Affiliation(s): Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; more
Corresponding email(s): xuzhenyi@mail.ustc.edu.cn, kangduyu@ustc.edu.cn
Key Words: High-emitter identification; Temporal optimization; On-board diagnostic device (OBD); Dynamic threshold

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Abstract: Heavy-duty diesel vehicles are important sources of urban nitrogen oxides (NO_x) in actual applications for environmental compliance, emitting more than 80% of NO_x and more than 90% of particulate matter (PM) in total vehicle emissions. The detection and control of heavy-duty diesel emissions are critical for protecting public health. Currently, vehicles on the road must be regularly tested, every six months or once a year, to filter out high-emission mobile sources at vehicle inspection stations. However, it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections, and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions. An on-board diagnostic device (OBD) is installed inside the vehicle and can record the vehicle’s emission data in real time. In this paper, we propose a temporal optimization long short-term memory (LSTM) and adaptive dynamic threshold approach to identify heavy-duty high-emitters by using OBD data, which can continuously track and record the emission status in real time. First, a temporal optimization LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice. Then, the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria, calculated by an adaptive dynamic threshold with changing driving conditions. Finally, a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results. Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.

基于时序优化长短期记忆和自适应阈值的高排放重型柴油车识别

许镇义¹，王仁军^1,2，曹洋^1,3,4，康宇^1,3,4
¹合肥综合性国家科学中心人工智能研究院，中国合肥市，230088
²安徽大学与合肥综合性国家科学中心人工智能研究院联合实验室，
中国合肥市，230601
³中国科学技术大学自动化系，中国合肥市，230027
⁴中国科学技术大学先进技术研究院，中国合肥市，230088
摘要：在实际场景中，重型柴油车是城市氮氧化物的重要来源，其排放的氮氧化物（NO_x）占车辆总排放量的80％以上，颗粒物（PM）占90％以上。检测和控制重型柴油车的排放对保护公众健康至关重要。目前，道路上的车辆必须每6个月或每年定期检测一次，在车辆检查站过滤出高排放的移动源。然而，由于年检间隔时间较长，很难及时有效地筛选出高排放车辆，而且固定的阈值不能适应车辆驾驶工况的动态变化。车载诊断设备（OBD）安装在车辆内部，可以连续跟踪和实时记录排放数据。本文提出一种时间优化长短期记忆（LSTM）和自适应动态阈值方法，使用OBD数据识别重型高排放车辆。首先，建立一个时间优化LSTM排放预测模型，以解决实际中大量OBD数据流造成的时间步注意力偏重问题。然后，利用灵活的阈值标准检测浓度预测误差序列，以区分异常排放情况，该阈值随驾驶条件变化自适应计算得到。最后，引入时间序列的相似性度量策略，以纠正一些假的异常结果。在3个真实OBD时间序列排放数据集上的实验表明，该方法得到优异的高排放源识别结果。

关键词组：高排放识别；时序优化；车载诊断设备（OBD）；动态阈值

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基于时序优化长短期记忆和自适应阈值的高排放重型柴油车识别

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