Driver Maneuver Interaction Identification with Anomaly-Aware Federated Learning on Heterogeneous Feature Representations
Driver Maneuver Interaction Identification with Anomaly-Aware Federated Learning on Heterogeneous Feature Representations
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DOI：
10.1145/3631421
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发表时间：
2023-12
影响因子：
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通讯作者：
Mahan Tabatabaie;Suining He
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文献类型：
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作者：
Mahan Tabatabaie;Suining He
驾驶员操纵交互学习（DMIL）是指以识别不同的驾驶员-车辆操纵交互（例如，左/右转弯）。现有的传统研究主要集中在从驾驶员的智能手机（例如，惯性测量单元或伊穆斯，如加速度计和陀螺仪）集中收集传感器数据。这种集中的机制可能会因数据监管限制而被排除在外。此外，如何实现自适应和准确的DMIL框架仍然具有挑战性，这是由于（i）异构驾驶员操纵模式的复杂性，以及（ii）由于例如激进的驾驶风格和行为而导致的异常驾驶员操纵的影响。为了克服上述挑战，我们提出了AF-DMIL，一个异常感知的联邦驾驶员机动交互学习系统。我们专注于真实世界的IMU传感器数据集（例如，通过智能手机收集）用于我们的试点案例研究。特别是，我们已经设计了三个异构表示AF-DMIL关于光谱，时间序列和统计特征，来自IMU传感器读数。我们设计了一种新的异构表示注意力网络（HetRANet）的基础上，频谱通道的注意力，时间序列的注意力，和统计特征学习机制，共同捕捉和识别驾驶员操纵行为中的复杂模式。此外，我们在HetRANet中设计了一个密集连接的卷积神经网络，以实现复杂的特征提取并提高HetRANet的计算效率。此外，我们在AF-DMIL中设计了一种新的异常感知联邦学习方法，用于分散式DMIL，以应对异常机动数据。为了便于提取的机动模式和评估他们的相互差异，我们设计了一个嵌入投影网络，投影到低维空间的高维驾驶员操纵功能，并进一步推导出的样本，代表驾驶员操纵模式进行相互比较。然后，AF-DMIL进一步利用样本的相互差异来识别那些表现出异常模式并偏离其他模式的样本，并减轻它们对联合DMIL的影响。我们对三个真实数据集进行了广泛的驾驶员数据分析和实验研究（一个是我们自己收获的）来评估AF-DMIL的原型，与最先进的DMIL基线相比，证明AF-DMIL的准确性和有效性（DMIL准确度平均提高13%以上），以及更少的通信回合（比现有的分布式学习机制平均少29.20%）。
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).