Integrating Driving Behavior and Traffic Context Through Signal Symbolization for Data Reduction and Risky Lane Change Detection
Integrating Driving Behavior and Traffic Context Through Signal Symbolization for Data Reduction and Risky Lane Change Detection
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DOI：
10.1109/tiv.2018.2843171
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发表时间：
2018-09-01
影响因子：
8.2
通讯作者：
Egawa, Masumi
中科院分区：
文献类型：
--
作者：
Yurtsever, Ekim;Yamazaki, Suguru;Egawa, Masumi
提出了一种通过信号符号化将驾驶行为与交通环境相结合的新方法。这种符号化框架被提出作为自然主义驾驶研究的数据简化方法。连续传感器信号已被转换和减少到序列的符号（块）使用粘性分层狄利克雷过程隐马尔可夫模型和嵌套Pitman-Yor语言模型。然后，共现组块（COOC），提出的集成方法，已被应用到驾驶员行为和交通上下文块。集成后，COOC块已与原型驾驶场景使用潜在的Dirichlet分配。最后，翻译后的组块序列被聚类成组。危险的车道变换检测实验已经进行了符号化的数据进行评估的目的。该过程使用了由988个变道场景组成的数据集。同现分块与聚类提供了最好的危险车道变化检测。
A novel method for integrating driving behavior and traffic context through signal symbolization is presented in this paper. This symbolization framework is proposed as a data reduction method for naturalistic driving studies. Continuous sensor signals have been converted and reduced into sequences of symbols (chunks) using a sticky hierarchical Dirichlet process hidden Markov model and a nested Pitman-Yor language model. Then, co-occurrence chunking (COOC), the proposed integration method, has been applied to the driver behavior and the traffic context chunks. After the integration, COOC chunks have been associated with prototype driving scenes by using latent Dirichlet allocation. Finally, the translated sequence of chunks has been clustered into groups. Risky lane change detection experiments have been conducted with the symbolized data for evaluation purposes. A dataset comprised of 988 lane change scenes has been utilized for this process. Co-occurrence chunking with clustering provided the best risky lane change detection.