# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 2 Literature Review  
The literature on AVs reveals uncertainties about their impact on traffic, infrastructure, supply chain operations, the environment, and the economy. For instance, some predict that AVs will reduce congestion because they can smooth out traffic flows by eliminating the accordion effect [2]. Another optimistic expectation is that AVs will be safer because they will eliminate errors due to human factors [4]. Contrary views are that AVs will increase congestion because induced demand from lower costs and greater accessibility to non-drivers will add more vehicles to the road, including empty vehicles [5]. Some researchers posit that AVs will never fully eliminate crashes due to human factors so long as they coexist with human-driven vehicles [6]. Research has also highlighted that AVs can exacerbate inequities if policies do not address access for people with low income, people of color, and rural communities [7]. The regulatory landscape allowing AV operation on U.S. public roads is dynamic and involves a complex interplay between federal and state authorities [3]. There is a growing consensus that the federal government should oversee aspects such as accessibility, safety, design, and manufacturing, whereas state and local governments should retain their traditional roles in regulating titling, registration, traffic laws, and deployments [8]. Toward that end, the National Highway Traffic Safety Administration (NHTSA), a federal agency of the U.S. Department of Transportation (USDOT), updated its safety standard to include aspects of AVs such as not mandating steering wheels and driver's seats [9]. AVs cannot become an affordable and robust form of transportation until the legislative landscape matures [10]. However, ensuring safety, liability, and regulatory compliance in an evolving field often faces hurdles in legislative processes [11]. An exhaustive review of all the recent AV bills that failed revealed a complex interplay of factors. These include a lack of support from legislators, a lack of consensus, safety concerns, opposition from influential stakeholders such as labor unions, competing legislative priorities, and concerns about the practicality of enforcement, technology readiness, and policy implications such as potential economic impact. The literature does not currently offer a comparative analysis between states with differing legislative outcomes to provide more depth in understanding why certain regulations passed or failed, so future work could fill that gap. There was a limited amount of research that recently explored the association of features in an authority with its propensity to pass supportive AV regulations. [PERSON] et al. (2023) analyzed panel data from 2011 to 2018 and found that an increase in a city's use of electric vehicles, gross domestic product (GDP) per capita, freeway vehicle miles traveled (VMT), and land use score was positively associated with the allowance of AV testing, whereas an increase in fatality cases had a negative association [12]. [PERSON] et al. (2021) noted that perceived safety issues and public acceptance are predominant barriers to AV adoption [13]. A survey by [PERSON] et al. (2020) revealed that population size and liberal -political ideologies are strongly associated with support for policies that regulate AV use to increase mobility for the low-income and disabled populations, reduce pollution, reduce traffic, increase transit use, and reduce the use of private cars [14]. The study also found no connection between population growth and support for general AV regulations. A survey by [PERSON] et al. (2021) found that, compared with conservative ideologies, moderates and liberals reported higher AV adoption intention based on both higher perceived benefits and lower perceived concerns [15]. A survey by [PERSON] (2021) revealed that the level of fear of AVs increased with an increase in the number of crashes involving AVs [1].