# Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach
## 5 Discussion  
Assuming a confidence level of 80%, the results demonstrate a significant association between the presence of advance warning signs and improved compliance rates, with a 9.6% increase in compliance when such signs are used. This finding underscores the pivotal role of visual cues in shaping driver behavior, as advance warning signs provide a preemptive alert, allowing drivers to respond safely and reduce the likelihood of roadside incidents. Additionally, the results highlight the influence of shoulder location, with a 13% lower compliance rate observed when vehicles involved in incidents are positioned on the left shoulder. This may reflect the difficulty drivers face in safely slowing down or changing lanes when responding to left-shoulder scenarios, given the higher traffic speeds in adjacent  
Figure 8: Compliance rate distribution categorized by the approaching vehicle type. On almost every scenario we have both passenger cars and trucks in the scene. There is a significantly higher compliance rate to MO law among truck drivers, which might be caused by the larger size of the trucks—which can cause fear of collision—as well as the possible higher level of scrutiny when it comes to law enforcement’s approach to transport vehicles drivers.  
lanes. In contrast, vehicles on the right shoulder align with lower prevailing speeds, making compliance actions, such as lane changes or speed reductions, more manageable. These findings suggest prioritizing left-shoulder scenarios with advanced warning systems to enhance safety.  
The data also indicate that trucks exhibit a significantly higher compliance rate with the Move Over law compared to passenger cars. This observation correlates with the fact that trucks predominantly travel in slower lanes, where lowering speed or changing lanes to comply is inherently more feasible. This finding highlights how lane usage and vehicle characteristics influence compliance behavior, offering insights for tailored traffic management strategies.  
While the analysis of responder vehicle position and type did not yield statistically significant differences, this is likely due to limited data. A larger dataset is necessary to ensure the robustness of these findings. Similarly, road type showed no significant influence, though the slight 5.9% increase in compliance observed in urban areas compared to rural ones hints that urban drivers might be more familiar with traffic laws or encounter frequent campaigns promoting the \"Move Over Law\". However, with a \(p\)-value of 0.47, this observation warrants further investigation into factors like road design and enforcement levels.  
Lastly, the negative correlation coefficients (\(-0.273\) and \(-0.320\)) between compliance and traffic volume reveal that as traffic density increases, compliance decreases. This indicates that dense traffic limits drivers' ability to change lanes or slow down safely. High-traffic areas might benefit from interventions such as automated enforcement systems or the deployment of additional responder vehicles to facilitate compliance and manage traffic flow effectively.