Multivariate Time Series Analysis for Driving Style Classification using Neural Networks and Hyperdimensional Computing
Multivariate Time Series Analysis for Driving Style Classification using Neural Networks and Hyperdimensional Computing
复制标题
使用神经网络和超维计算进行驾驶风格分类的多元时间序列分析
DOI：
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发表时间：
2021
期刊：
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通讯作者：
P. Protzel
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作者：
Kenny Schlegel;Florian Mirus;Peer Neubert;P. Protzel
本文提出了一种基于时间序列数据的驾驶风格分类新方法。我们提出了一种用于高维向量数据表示的超维计算（HDC）和更简单的前馈神经网络的组合，而不是使用递归神经网络自动学习用于输入数据的时间表示的嵌入向量。这种方法提供了三个关键优势：首先，我们的方法允许使用HDC的代数运算以人类可理解的方式在高维向量中编码这种时间结构，而不是让循环神经网络的“黑盒子”学习数据的时间表示，同时仅依赖于前馈神经网络进行分类任务。其次，我们证明了这种组合与最先进的基于长短期记忆（LSTM）的网络相比，能够实现至少相似甚至略上级的分类准确性，同时显著减少训练时间和成功学习所需的数据量。第三，我们基于HDC的数据表示以及前馈神经网络，允许在尖峰神经网络（SNN）的基底中实现。SNN显示出比其基于速率的对应物更节能的数量级，同时在部署在专用神经形态计算硬件上时保持相当的预测准确性，这可能是未来智能车辆中的节能添加，对车载计算和能源资源有严格的限制。我们提出了一个公开的数据集，包括与国家的最先进的参考模型的比较，我们的方法进行了彻底的分析。
In this paper, we present a novel approach for driving style classification based on time series data. Instead of automatically learning the embedding vector for temporal representation of the input data with Recurrent Neural Networks, we propose a combination of Hyperdimensional Computing (HDC) for data representation in high-dimensional vectors and much simpler feed-forward neural networks. This approach provides three key advantages: first, instead of having a “black box” of Recurrent Neural Networks learning the temporal representation of the data, our approach allows to encode this temporal structure in high-dimensional vectors in a human-comprehensible way using the algebraic operations of HDC while only relying on feed-forward neural networks for the classification task. Second, we show that this combination is able to achieve at least similar and even slightly superior classification accuracy compared to state-of-the-art Long Short-Term Memory (LSTM)-based networks while significantly reducing training time and the necessary amount of data for successful learning. Third, our HDC-based data representation as well as the feed-forward neural network, allow implementation in the substrate of Spiking Neural Networks (SNNs). SNNs show promise to be orders of magnitude more energy-efficient than their rate-based counterparts while maintaining comparable prediction accuracy when being deployed on dedicated neuromorphic computing hardware, which could be an energy-efficient addition in future intelligent vehicles with tight restrictions regarding on-board computing and energy resources. We present a thorough analysis of our approach on a publicly available data set including a comparison with state-of-the-art reference models.