[论文解读] Drive Safe: Cognitive-Behavioral Mining for Intelligent Transportation Cyber-Physical System

Md. Shirajum Munir, Sarder Fakhrul Abedin+4 more|arXiv (Cornell University)|Aug 24, 2020

Emotion and Mood Recognition参考文献 55被引用 5

一句话总结

本论文提出Drive Safe，一种用于智能交通网络物理系统（IT-CPS）的认知行为挖掘平台，通过人工智能模型检测驾驶员分心和情绪状态，并自主推荐音频内容以实现情绪修复。该原型集成了胶囊网络、贝叶斯网络和边缘计算技术，在可用性和安全性有效性评估中达到统计显著性（p = 0.0041），并获得0.93的95%置信区间。

ABSTRACT

This paper presents a cognitive behavioral-based driver mood repairment platform in intelligent transportation cyber-physical systems (IT-CPS) for road safety. In particular, we propose a driving safety platform for distracted drivers, namely \emph{drive safe}, in IT-CPS. The proposed platform recognizes the distracting activities of the drivers as well as their emotions for mood repair. Further, we develop a prototype of the proposed drive safe platform to establish proof-of-concept (PoC) for the road safety in IT-CPS. In the developed driving safety platform, we employ five AI and statistical-based models to infer a vehicle driver's cognitive-behavioral mining to ensure safe driving during the drive. Especially, capsule network (CN), maximum likelihood (ML), convolutional neural network (CNN), Apriori algorithm, and Bayesian network (BN) are deployed for driver activity recognition, environmental feature extraction, mood recognition, sequential pattern mining, and content recommendation for affective mood repairment of the driver, respectively. Besides, we develop a communication module to interact with the systems in IT-CPS asynchronously. Thus, the developed drive safe PoC can guide the vehicle drivers when they are distracted from driving due to the cognitive-behavioral factors. Finally, we have performed a qualitative evaluation to measure the usability and effectiveness of the developed drive safe platform. We observe that the P-value is 0.0041 (i.e., < 0.05) in the ANOVA test. Moreover, the confidence interval analysis also shows significant gains in prevalence value which is around 0.93 for a 95% confidence level. The aforementioned statistical results indicate high reliability in terms of driver's safety and mental state.

研究动机与目标

• 为应对由分心驾驶和不良心理状态引发的道路事故上升问题。
• 弥合驾驶员认知行为挖掘与下一代网络物理系统之间的差距，实现实时安全干预。
• 开发一种自主的车载平台，可识别驾驶员情绪并推荐情感内容，无需人工输入。
• 通过真实世界IT-CPS原型中的定性与统计评估，验证平台的可用性和有效性。

提出的方法

• 采用胶囊网络（CN）、卷积神经网络（CNN）和最大似然（ML）模型，用于识别驾驶员行为与环境特征。
• 在驾驶员生命周期日志上使用贝叶斯网络（BN）推断个性化情绪状态，实现自主决策。
• 应用Apriori算法进行序列模式挖掘，识别与情绪状态相关的行为趋势。
• 集成多接入边缘计算（MEC），实现实时处理生理与环境数据，降低延迟。
• 部署无线通信模块，实现车载设备与边缘服务器之间的异步数据交换。
• 利用DEAP数据集验证情绪识别，从生理信号中提取唤醒度与效价水平。

实验结果

研究问题

• RQ1 认知行为挖掘平台能否在真实场景中有效检测驾驶员分心和情绪状态？
• RQ2 人工智能与统计模型如何整合，以实现在智能交通系统中的自主情绪修复？
• RQ3 所提出的平台在网络安全物理系统背景下，能在多大程度上提升驾驶员安全性和可用性？
• RQ4 边缘计算与设备端处理能否确保低延迟、个性化的安全干预？

主要发现

• 方差分析（ANOVA）检验得到p值为0.0041，表明Drive Safe平台在可用性与有效性评估中结果具有统计显著性。
• 盛行率值的95%置信区间为0.93，表明在测量驾驶员安全与心理状态结果方面具有高度可靠性。
• 系统成功利用DEAP数据集中的生理数据识别驾驶员情绪，实现对唤醒度与效价的准确估计。
• 车载应用在无需驾驶员交互的情况下，自主提供基于认知行为挖掘的音频推荐。
• 平台以300毫秒为间隔可视化情绪波动与安全提示，提升了情境意识与心理安全性。
• 用户反馈证实高度满意，90%的参与者将系统评为有帮助，并愿意向他人推荐。