Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification.
Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification.
复制标题
使用时间序列分割和分类进行驾驶员操纵检测和分析。
DOI：
10.1061/jtepbs.teeng-7312
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Sharma,Anuj
中科院分区：
文献类型：
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作者：
Aboah,Armstrong;Adu-Gyamfi,Yaw;Gursoy,SenemVelipasalar;Merickel,Jennifer;Rizzo,Matt;Sharma,Anuj
本文实现了一种在自然驾驶环境下从车辆遥测数据中自动检测车辆动作的方法。以前的方法将车辆机动检测视为一个分类问题，尽管由于输入的遥测数据是连续的，因此需要对时间序列进行分割和分类。我们的目标是开发一个端到端的管道，用于将自然驾驶研究视频逐帧注释为各种驾驶事件，包括停车和车道保持事件、车道改变、左右转弯运动和水平曲线机动。为了解决时间序列分割问题，研究了一种能量最大化算法(EMA)，该算法能够从连续信号数据中提取不同持续时间和频率的驱动事件。为了减少超配和虚警率，使用启发式算法对具有高度可变模式的事件进行分类，如停车和车道保持。为了对分段的驾驶事件进行分类，实现了四个机器学习模型，并在多个数据源上评估了它们的准确性和可转移性。EMA提取的事件持续时间与实际事件相当，准确率从59.30%(左变道)到85.60%(车道保持)。此外，一维卷积神经网络模型的总体准确率为98.99%，其次是长-短期记忆模型(97.75%)，然后是随机森林模型(97.71%)，支持向量机模型(97.65%)。这些模型的精确度在不同的数据源上是一致的。研究结论是，实施分割-分类流水线显著提高了驾驶员机动检测的准确性，并提高了浅层和深层ML模型在不同数据集上的可转移性。
The current paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data are continuous. Our objective is to develop an end-to-end pipeline for the frame-by-frame annotation of naturalistic driving studies videos into various driving events including stop and lane-keeping events, lane changes, left-right turning movements, and horizontal curve maneuvers. To address the time series segmentation problem, the study developed an energy-maximization algorithm (EMA) capable of extracting driving events of varying durations and frequencies from continuous signal data. To reduce overfitting and false alarm rates, heuristic algorithms were used to classify events with highly variable patterns such as stops and lane-keeping. To classify segmented driving events, four machine-learning models were implemented, and their accuracy and transferability were assessed over multiple data sources. The duration of events extracted by EMA was comparable to actual events, with accuracies ranging from 59.30% (left lane change) to 85.60% (lane-keeping). Additionally, the overall accuracy of the 1D-convolutional neural network model was 98.99%, followed by the long-short-term-memory model at 97.75%, then the random forest model at 97.71%, and the support vector machine model at 97.65%. These model accuracies were consistent across different data sources. The study concludes that implementing a segmentation-classification pipeline significantly improves both the accuracy of driver maneuver detection and the transferability of shallow and deep ML models across diverse datasets.