利用异构特征表征上的异常感知联合学习识别驾驶员操纵交互

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Pub Date : 2024-01-12 DOI:10.1145/3631421
Mahan Tabatabaie, Suining He
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引用次数: 0

摘要

驾驶员操作交互学习(DMIL)是指以识别不同驾驶员与车辆操作交互(如左转/右转)为目标的分类任务。现有的传统研究主要侧重于集中收集驾驶员智能手机中的传感器数据(例如惯性测量单元或 IMU,如加速度计和陀螺仪)。这种集中式机制可能会受到数据法规的限制。此外,由于(i)异构驾驶员操纵模式的复杂性,以及(ii)由于激进驾驶风格和行为等原因造成的异常驾驶员操纵的影响,如何启用自适应和准确的 DMIL 框架仍具有挑战性。为了克服上述挑战,我们提出了 AF-DMIL,一种异常感知的联合驾驶员操纵交互学习系统。我们以真实世界的 IMU 传感器数据集(如智能手机收集的数据集)为试点案例研究的重点。特别是,我们为 AF-DMIL 设计了三种异构表示法,分别涉及从 IMU 传感器读数中获得的频谱、时间序列和统计特征。我们设计了一种基于频谱通道关注、时间序列关注和统计特征学习机制的新型异构表征关注网络(HetRANet),可共同捕捉和识别驾驶员操纵行为中的复杂模式。此外,我们还在 HetRANet 中设计了一个密集连接的卷积神经网络,以实现复杂特征提取并提高 HetRANet 的计算效率。此外,我们还在 AF-DMIL 中设计了一种新颖的异常感知联合学习方法,用于分散式 DMIL,以应对异常操纵数据。为了便于提取操纵模式和评估它们之间的相互差异,我们设计了一个嵌入式投影网络,将高维驾驶员操纵特征投影到低维空间,并进一步得出代表驾驶员操纵模式的范例,以便进行相互比较。然后,AF-DMIL 进一步利用范例的相互差异来识别那些表现出异常模式和偏离其他模式的范例,并减轻它们对联合 DMIL 的影响。我们在三个真实数据集(其中一个数据集是我们自己采集的)上进行了广泛的驱动数据分析和实验研究,以评估 AF-DMIL 的原型,结果表明与最先进的 DMIL 基线相比,AF-DMIL 的准确性和有效性更高(在 DMIL 准确性方面平均提高了 13% 以上),而且通信轮数更少(与现有的分布式学习机制相比,平均减少了 29.20%)。
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Driver Maneuver Interaction Identification with Anomaly-Aware Federated Learning on Heterogeneous Feature Representations
Driver maneuver interaction learning (DMIL) refers to the classification task with the goal of identifying different driver-vehicle maneuver interactions (e.g., left/right turns). Existing conventional studies largely focused on the centralized collection of sensor data from the drivers' smartphones (say, inertial measurement units or IMUs, like accelerometer and gyroscope). Such a centralized mechanism might be precluded by data regulatory constraints. Furthermore, how to enable an adaptive and accurate DMIL framework remains challenging due to (i) complexity in heterogeneous driver maneuver patterns, and (ii) impacts of anomalous driver maneuvers due to, for instance, aggressive driving styles and behaviors. To overcome the above challenges, we propose AF-DMIL, an Anomaly-aware Federated Driver Maneuver Interaction Learning system. We focus on the real-world IMU sensor datasets (e.g., collected by smartphones) for our pilot case study. In particular, we have designed three heterogeneous representations for AF-DMIL regarding spectral, time series, and statistical features that are derived from the IMU sensor readings. We have designed a novel heterogeneous representation attention network (HetRANet) based on spectral channel attention, temporal sequence attention, and statistical feature learning mechanisms, jointly capturing and identifying the complex patterns within driver maneuver behaviors. Furthermore, we have designed a densely-connected convolutional neural network in HetRANet to enable the complex feature extraction and enhance the computational efficiency of HetRANet. In addition, we have designed within AF-DMIL a novel anomaly-aware federated learning approach for decentralized DMIL in response to anomalous maneuver data. To ease extraction of the maneuver patterns and evaluation of their mutual differences, we have designed an embedding projection network that projects the high-dimensional driver maneuver features into low-dimensional space, and further derives the exemplars that represent the driver maneuver patterns for mutual comparison. Then, AF-DMIL further leverages the mutual differences of the exemplars to identify those that exhibit anomalous patterns and deviate from others, and mitigates their impacts upon the federated DMIL. We have conducted extensive driver data analytics and experimental studies on three real-world datasets (one is harvested on our own) to evaluate the prototype of AF-DMIL, demonstrating AF-DMIL's accuracy and effectiveness compared to the state-of-the-art DMIL baselines (on average by more than 13% improvement in terms of DMIL accuracy), as well as fewer communication rounds (on average 29.20% fewer than existing distributed learning mechanisms).
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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
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