Abstract The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the detection of abnormalities was manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles that mainly focuses on detecting abnormal situations around the considered agents. Multi-sensory time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models used for future state prediction and the detection of dynamic abnormalities. Moreover, an initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed. The GNG algorithm learns the DBN models’ discrete node variables; probabilistic transition links connect the node variables. A Markov Jump Particle Filter (MJPF) is applied to predict future states and detect when the vehicle is potentially misbehaving using learned DBNs as filter parameters. In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models.

中文翻译：

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智能车辆的自我意识：基于特征的动态贝叶斯模型异常检测

摘要 近年来，智能交通系统的发展使得代理的自我意识的发展成为必要。在大量使用机器学习之前，异常检测是通过检查每个变量并创建难以跟踪的巨大嵌套条件来手动编程的。本文旨在介绍一种在自动驾驶汽车中开发自我意识的新方法，主要侧重于检测所考虑代理周围的异常情况。来自车辆的多感官时间序列数据用于开发数据驱动的动态贝叶斯网络 (DBN) 模型，用于未来状态预测和动态异常检测。此外，提出了一种可以在合作任务中执行联合异常检测的初始级集体意识模型。GNG 算法学习 DBN 模型的离散节点变量；概率转换链接连接节点变量。马尔可夫跳跃粒子滤波器 (MJPF) 用于预测未来状态，并使用学习的 DBN 作为滤波器参数检测车辆何时可能出现异常行为。在本文中，来自自动驾驶汽车执行各种任务的真实实验的数据集用于学习和测试一组切换 DBN 模型。