Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks

Applied Sciences

卷:11

Arshad Jamal¹  Abdolmaged Alkhulaifi²  Irfan Ahmad² 

[1] Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Daman, Saudi Arabia;

[2] Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran 31261, Daman, Saudi Arabia;

关键词: traffic sign retro-reflection;    degradation prediction;    machine learning;    deep learning;   

DOI  :  10.3390/app112411595

来源: DOAJ

【 摘 要 】

Traffic signs are essential for the safe and efficient movement of vehicles through the transportation network. Poor sign visibility can lead to accidents. One of the key properties used to measure the visibility of a traffic sign is retro-reflection, which indicates how much light a traffic sign reflects back to the driver. The retro-reflection of the traffic sign degrades over time until it reaches a point where the traffic sign has to be changed or repaired. Several studies have explored the idea of modeling the sign degradation level to help the authorities in effective scheduling of sign maintenance. However, previous studies utilized simpler models and proposed multiple models for different combinations of the sheeting type and color used for the traffic sign. In this study, we present a neural network based deep learning model for traffic sign retro-reflectivity prediction. Data utilized in this study was collected using a handheld retro-reflectometer GR3 from field surveys of traffic signs. Sign retro-reflective measurements (i.e., the $R_A$ values) were taken for different sign sheeting brands, grades, colors, orientation angles, observation angles, and aging periods. Feature-based sensitivity analysis was conducted to identify variables’ relative importance in determining retro-reflectivity. Results show that the sheeting color and observation angle were the most significant variables, whereas sign orientation was the least important. Considering all the features, $R_A$ prediction results obtained from one-hot encoding outperformed other models reported in the literature. The findings of this study demonstrate the feasibility and robustness of the proposed neural network based deep learning model in predicting the sign retro-reflectivity.

【 授权许可】

Unknown