# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 5 Discussion  
The analysis reveals that a significant majority of CONUS states have engaged in AV-related legislation, underscoring the increasing recognition of AVs' role in the future of transportation. The diverse state-level approaches, particularly regarding the requirement for a human backup driver, reflect the complexity of AV regulation. This variance indicates both the challenges and the opportunities in harmonizing AV policies across different jurisdictions. The application of machine learning (ML) models, particularly the feature engineering process, has uncovered several factors influencing AV legislation. A key finding is the lack of a strong correlation between the target feature (the requirement for a human backup driver) and other variables. This suggests a multifaceted interplay of factors driving legislative decisions, which goes beyond simple linear associations. The chi-squared test reveals a significant association between legislative status and the requirement for a human backup driver. States with enacted legislation are more inclined to consider human drivers optional, reflecting either a growing confidence in AV technology or a legislative move toward advanced AV deployment stages. Interestingly, the chi-squared test also shows that AV legislation transcends traditional political divides, indicating a broader, non-partisan approach to AV regulation. The SHAP analysis highlights critical features influencing legislative outcomes. For instance, the association of features like democratic electoral college votes per million population (VDpPOPM) and port tons per capita (PORTpPOP) with the optional status of a human backup driver hints at the role of economic and demographic factors in shaping AV policies. This could indicate that states with less intense political partisanship and lower dependence on port-related economic activities might be more receptive to innovative AV policies. The association of lower population density (POPpLA) with the positive target class could imply that less densely populated states might be more inclined to adopt AV-friendly legislation. This could be due to fewer traffic complexities or a greater willingness to experiment with new technologies in less congested areas. Interestingly,  
Figure 6: Feature ranking. higher values for fatalities per capita (FATLpPOP) and potential service demand per transit agency (POPKpTRA) are associated with the positive target class. This might indicate that states with higher road fatality rates or higher populations per transit agency are potentially more motivated to seek AV solutions, considering them to enhance road safety and improve public transit efficiency. The associations between these variables and AV legislation underscore the multifaceted nature of policy making in this domain. This finding suggests that a blend of political, economic, demographic, and safety considerations influence the legislative decisions of states. The influence of political factors (VDpPOPM) and economic activities (PORTpPOP) on AV legislation decisions suggests that broader political and economic contexts play a significant role in shaping the openness of a state to AV technology. The impact of population density (POPpLA) indicates that demographic factors, particularly urbanization levels, may affect the readiness or hesitance of a state to embrace AV technology. The correlation with fatalities (FATLpPOP) and public transit demands (POPKpTRA) suggests that states with more pressing road safety concerns or strained public transit systems might view AVs as a potential solution to these challenges. These findings emphasize the need for policymakers to consider broader socio-economic contexts when drafting AV legislation. The diversity in state-level AV legislation suggests an urgent need for a more unified regulatory framework, potentially at the federal level. Such harmonization could prevent a disjointed regulatory landscape that might impede AV development and deployment.