Mental state and behavior inference using Mirror Neuron System architecture for traffic/driver monitoring

K. Varadarajan, Kai Zhou, M. Vincze
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引用次数: 4

Abstract

Traffic psychology presents interesting avenues towards the development of Intelligent Transportation Systems (ITS). Analysis of driver state, emotion and behavior are important components of traffic psychology. While being comprehensive in terms of theoretical frameworks, these analyses lack neurobiological computational models for evaluation. In this paper, we develop computational models for driver state and behavior, also known as Mental State Inference (MSI) based on the Mirror Neuron System (MNS) architecture. The integrated system combines neurobiological models with computer vision techniques for traffic monitoring from surveillance video leading to MSI and event recognition. Evaluation of the system is carried out in terms of actual, psychophysical as well as neurobiological criteria on both simulated and real data. Results demonstrate event and mental state recognition convergence within 0.5 normalized time units for the designed event models on synthetic and real data. The model is also robust to perturbations and is aligned to behavior expected at the psychophysical level.
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基于镜像神经元系统架构的交通/驾驶员监控心理状态和行为推断
交通心理学为智能交通系统(ITS)的发展提供了有趣的途径。驾驶人的状态、情绪和行为分析是交通心理学的重要组成部分。虽然在理论框架方面是全面的,但这些分析缺乏用于评估的神经生物学计算模型。在本文中,我们开发了基于镜像神经元系统(MNS)架构的驾驶员状态和行为的计算模型,也称为心理状态推断(MSI)。该集成系统将神经生物学模型与计算机视觉技术相结合,用于从监控视频到MSI和事件识别的交通监控。在模拟和真实数据上对系统进行了实际、心理物理和神经生物学标准的评估。结果表明,所设计的事件模型在0.5个归一化时间单位内对事件和心理状态的识别具有收敛性。该模型对扰动也具有鲁棒性,并且与心理物理层面的预期行为一致。
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