Representing Realistic Human Driver Behaviors using a Finite Size Gaussian Process Kernel Bank
Representing Realistic Human Driver Behaviors using a Finite Size Gaussian Process Kernel Bank
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使用有限大小高斯过程内核库表示现实的人类驾驶员行为
DOI：
10.1109/vnc48660.2019.9062828
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
S. Mahmud
中科院分区：
文献类型：
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作者：
Hossein Nourkhiz Mahjoub;Arash Raftari;Rodolfo Valiente;Y. P. Fallah;S. Mahmud
协同车辆应用的性能紧密依赖于底层车对万物（V2X）通信技术的可靠性。V2X标准，如专用短程通信（DSRC）和Cellular-V2X（C-V2X），在美国强制执行之前正在通过其研究阶段，应该作为车辆网络中时间关键信息的可靠循环系统;然而，它们仍然严重遭受真实的交通场景中的可扩展性问题。基于模型的通信（MBC）的技术不可知的概念已被提出在我们以前的作品作为一个有前途的范例，以解决可扩展性问题和它的性能，同时获得不同的建模策略，已被广泛研究。在这项工作中，一个强大的非参数贝叶斯推理方案，即建模能力，高斯过程（GPs），在MBC背景下进行了更详细的研究。我们的观察揭示了基于GP的MBC方案的一个重要的潜在优势，即，它的能力，准确地建模不同的驾驶行为模式，通过利用只有一个有限的大小GP内核银行。将GP推理与MBC框架集成的这个有趣的方面，已经在这项工作中使用现实的驾驶数据集进行了验证，介绍了这种架构作为解决可扩展性挑战的强大而有吸引力的候选者。结果证实，我们所提出的方法在所需的通信速率和GP内核库大小方面超过了最先进的研究。
The performance of cooperative vehicular applications is tightly dependent on the reliability of the underneath Vehicle-to-Everything (V2X) communication technology. V2X standards, such as Dedicated Short-Range Communications (DSRC) and Cellular-V2X (C-V2X), which are passing their research phase before being mandated in the US, are supposed to serve as reliable circulatory systems for the time-critical information in vehicular networks; however, they are still heavily suffering from scalability issues in real traffic scenarios. The technology-agnostic notion of Model-Based Communications (MBC) has been proposed in our previous works as a promising paradigm to address the scalability issue and its performance, while acquiring different modeling strategies, has been vastly studied. In this work, the modeling capabilities of a powerful nonparametric Bayesian inference scheme, i.e., Gaussian Processes (GPs), is investigated within the MBC context with more details. Our observations reveal an important potential strength of GP-based MBC scheme, i.e., its capability of accurately modeling different driving behavioral patterns by utilizing only a limited size GP kernel bank. This interesting aspect of integrating GP inference with MBC framework, which has been verified in this work using realistic driving data sets, introduces this architecture as a strong and appealing candidate to address the scalability challenge. The results confirm that our proposed approach over-performs the state of the art research in terms of the required communication rate and GP kernel bank size.