Automated Vehicle Identification Based on Car-Following Data With Machine Learning
Automated Vehicle Identification Based on Car-Following Data With Machine Learning
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DOI：
10.1109/tits.2023.3304607
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发表时间：
2023-12
影响因子：
8.5
通讯作者：
Qianwen Li;Xiaopeng Li;Handong Yao;Zhaohui Liang;Weijun Xie
中科院分区：
文献类型：
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作者：
Qianwen Li;Xiaopeng Li;Handong Yao;Zhaohui Liang;Weijun Xie
具有自适应巡航控制的车辆，即SAE Level 1和2 Automatic Vehicle(AVs)，一直在渗透率显著和快速增长的道路上运行。识别这些AVs对于了解近期的混合交通特征以及管理高速公路的机动性和安全性至关重要。该研究通过在短时间窗口内使用车辆跟踪轨迹构建一组基于学习的模型，从而识别配备自适应巡航控制的车辆与人类驾驶的车辆(HV)。当数据可用时，它可扩展到3级和+AV标识。为了比较模型性能和得出物理见解，提出了两个基于物理的模型，其前提是，通常情况下，AV的跟车行为比HV的波动性更小。将包括不同厂家的汽车跟踪器在内的四个跟驰数据集混合在一起，构建了一个全面的识别模型。结果表明，基于物理的方法可以识别80%以上的动静压和70%以上的动静压。基于学习的模型辨识精度更高。例如，集群感知的长期短期记忆网络可以识别98.79%的AVs和95.45%的Hv。本研究开发的基于学习的识别模型可以与现有的基础设施(如监控摄像头)相结合，用于提取车辆跟踪轨迹，以检测混合交通流中的AVs。这为分析和控制混合流量提供了无与伦比的数据驱动机会，以增强安全性(例如，通知周围流量存在AVs)和移动性(例如，在百分比足够大时开放AV专用车道)。
Vehicles with adaptive cruise control, i.e., SAE Levels 1 and 2 automated vehicles (AVs), have been operating on roads with a significant and rapidly growing penetration rate. Identifying these AVs is critical to understanding near-future mixed traffic characteristics and managing highway mobility and safety. This study identifies adaptive cruise control-equipped vehicles from human-driven vehicles (HVs) by constructing a set of learning-based models using car-following trajectories in a short time window. It is extendible to Level 3 and + AV identification when data is available. To compare model performance and draw physical insights, two physics-based models are proposed based on the premise that, in general, the car-following behavior of an AV is less volatile than an HV. Four car-following datasets, including AV makes from different manufacturers, are mixed to build a comprehensive identification model. Results show that physics-based approaches identify more than 80% AVs and 70% HVs. The identification accuracy of learning-based models is even higher. For example, the cluster-aware long short-term memory network identifies 98.79% of AVs and 95.45% of HVs. Learning-based identification models developed by this study can be integrated with the existing infrastructure (e.g., surveillance cameras), which have been used to extract car-following trajectories, to detect AVs in mixed traffic streams. This opens unparalleled data-driven opportunities to analyze and control mixed traffic to enhance safety (e.g., notifying surrounding traffic of the presence of AVs) and mobility (e.g., opening AV dedicated lanes when the percentage is great enough).