تقرير
Cognitive Internet of Vulnerable Road Users in Traffic: Predictive Neural Modulations of Road Crossing Intention
| العنوان: | Cognitive Internet of Vulnerable Road Users in Traffic: Predictive Neural Modulations of Road Crossing Intention |
|---|---|
| المؤلفون: | Zhou, Xiaoshan, Menassa, Carol C., Kamat, Vineet R. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Human-Computer Interaction, Computer Science - Emerging Technologies |
| الوصف: | Vulnerable Road Users (VRUs) present a significant challenge for road safety due to the frequent unpredictability of their behaviors. In typical Intelligent Transportation Systems, vision-based approaches supported by networked cameras are often used to anticipate VRUs motion intentions and trajectories. However, several limitations posed by occlusions and distractions set a boundary for the efficacy of such methods. To address these challenges, this study introduces a framework that leverages data collected using wearable neurophysiological sensors on VRUs to integrate them seamlessly into the Vehicle-to-Everything communication framework. This integration empowers VRUs to autonomously broadcast their intended movements to other road agents, especially autonomous vehicles, thereby bridging a critical gap in current vehicular communication systems. To validate this concept, we conducted an experiment involving 12 participants, from whom EEG signals were collected as they engaged in road-crossing decisions within simulated environments. Employing Hidden Markov Models, we identified four cognitive stages intrinsic to a pedestrian's decision-making process. Our statistical analysis further revealed significant variations in EEG activities across these stages, shedding light on the neural correlates and cognitive dynamics underpinning pedestrian road-crossing behavior. We then developed a predictive cognitive model using dynamic time warping and K-nearest neighbors algorithms, optimized through a data-driven sliding window approach. This model demonstrated high predictive accuracy, evidenced by an Area Under the Curve of 0.91, indicating its capability to anticipate pedestrian road-crossing actions approximately 1 second in advance of any pedestrian movement. This research paves the way for a novel VRU-Vehicle interaction paradigm and signifies a shift towards a forward-thinking ecosystem. Comment: 33 pages, 15 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.13955 |
| رقم الأكسشن: | edsarx.2405.13955 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |