Traffic signs are essential informative tools for road users, with their quality regulated by international standards. Among the critical parameters for evaluation is retroreflectivity. With the rise of autonomous vehicles (AVs), new tools like LiDAR sensors and cameras have become crucial for assessing traffic signs. However, the relationship between human perception and the physical properties of these signs remains underexplored. This study compares traffic sign evaluations using retroreflectivity measurements, LiDAR data, and human assessment. Approximately 160 traffic signs were analyzed using standardized retroreflectivity measurements, with additional data collected from two different LiDAR systems mounted on an AV. Volunteers conducted human evaluations assessing visibility, legibility, and contrast with surroundings. The handheld reflectometer provided a wide range of retroreflectivity readings, while LiDAR data showed good contrast between sign faces and surroundings, though results varied between the two systems. Human assessments correlated strongly with overall appearance but showed limited correlation with the other methods. This study highlights the strengths and limitations of each evaluation approach, offering insights for improving traffic sign assessment techniques.