AI-Assisted Self-Powered Vehicle-Road Integrated Electronics for Intelligent Transportation Collaborative Perception,Advanced Materials

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AI-Assisted Self-Powered Vehicle-Road Integrated Electronics for Intelligent Transportation Collaborative Perception
Advanced Materials ( IF 27.4 ) Pub Date : 2024-07-25 , DOI: 10.1002/adma.202404763
Yafeng Pang _{1,

2,

3} , Xingyi Zhu ₁ , Tianyiyi He _{2,

3,

4} , Shuainian Liu ₁ , Zixuan Zhang _{2,

4} , Qiaoya Lv ₅ , Peng Yi ₆ , Chengkuo Lee _{2,

3}

Affiliation

Collaborative perception between a vehicle and the road has the potential to enhance the limited perception capability of autonomous driving technologies. With this background, self-powered vehicle-road integrated electronics (SVRIE) with a multilevel fractal structure is designed to play a dual role, including a SVRIE device integrated into vehicle tires and a SVRIE array embedded into a road surface. The pressure sensing capability and anti-crosstalk performance of the SVRIE array are characterized separately to validate the feasibility of applying the SVRIE in a cooperative vehicle-infrastructure system. It is demonstrated that the SVRIE based on the multi-layered fractal structure exhibits maximum performance in collaborative sensing and interaction between vehicles and road information, such as vehicle motion, road surface condition, and tire life cycle health monitoring. Traditional data analysis methods are often of questionable accuracy. Therefore, a convolutional neural network is used to classify the vehicle and road conditions with accuracy of at least 88.3%. The transfer learning model is constructed to enhance the road surface identification capabilities with 100% accuracy. The accuracies of the vehicle tire motion recognition and tire health monitoring are 97% and 99%, respectively. This work provides new ideas for collaborative perception between vehicles and roadsides.

中文翻译：

人工智能辅助自供电车路一体化电子，实现智能交通协同感知

车辆和道路之间的协作感知有可能增强自动驾驶技术有限的感知能力。在此背景下，具有多级分形结构的自供电车路集成电子器件（SVRIE）被设计为发挥双重作用，包括集成到车辆轮胎中的SVRIE器件和嵌入路面的SVRIE阵列。分别对SVRIE阵列的压力传感能力和抗串扰性能进行了表征，以验证SVRIE在车辆-基础设施协作系统中应用的可行性。事实证明，基于多层分形结构的SVRIE在车辆与道路信息的协同感知和交互方面表现出最大的性能，例如车辆运动、路面状况和轮胎生命周期健康监测。传统的数据分析方法的准确性常常值得怀疑。因此，使用卷积神经网络对车辆和路况进行分类，准确率至少为88.3%。构建迁移学习模型，增强路面识别能力，准确率100%。车辆轮胎运动识别准确率达97%，轮胎健康监测准确率达99%。这项工作为车辆和路边之间的协作感知提供了新的思路。

更新日期：2024-07-25

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