论文标题
转向信号预测：联合学习案例研究
Turn Signal Prediction: A Federated Learning Case Study
论文作者
论文摘要
驾驶礼节为每个地方带来了不同的风味，因为驾驶员不仅遵守规则/法律，而且还遵守当地不言而喻的惯例。何时打开转向信号（指示器）是一个这样的礼节，没有确定的权利或错误答案。从车辆中集成的各种传感器模式产生的数据中学习这种行为是进行深度学习的合适候选者。但是，使其成为联合学习的主要候选人是任何数据聚合的隐私问题和带宽限制。本文使用车辆控制区域网络（CAN）信号数据介绍了较长的短期内存（LSTM）转向信号预测（打开或关闭）模型。该模型是使用两种方法训练的，一种是通过将数据集中汇总的，另一个是以联合方式进行的。在类似的高参数设置下比较了经过集中训练的模型和联合模型。这项研究证明了联合学习的功效，为驾驶礼节的车辆学习铺平了道路。
Driving etiquette takes a different flavor for each locality as drivers not only comply with rules/laws but also abide by local unspoken convention. When to have the turn signal (indicator) on/off is one such etiquette which does not have a definitive right or wrong answer. Learning this behavior from the abundance of data generated from various sensor modalities integrated in the vehicle is a suitable candidate for deep learning. But what makes it a prime candidate for Federated Learning are privacy concerns and bandwidth limitations for any data aggregation. This paper presents a long short-term memory (LSTM) based Turn Signal Prediction (on or off) model using vehicle control area network (CAN) signal data. The model is trained using two approaches, one by centrally aggregating the data and the other in a federated manner. Centrally trained models and federated models are compared under similar hyperparameter settings. This research demonstrates the efficacy of federated learning, paving the way for in-vehicle learning of driving etiquette.
