# Route Risk Index for Autonomous Trucks
## 1 Introduction  
Autonomous trucking has the potential to revolutionize freight transport, promising enhanced safety, reduced emissions, and improved efficiency [1]. However, the safe integration of autonomous trucking into the national transportation infrastructure presents a complex challenge. While the literature widely acknowledges the potential safety and efficiency benefits of autonomous vehicles (AVs), the focus on autonomous trucks (ATs) warrants specific attention. ATs will have a disproportionate impact on the freight industry compared with passenger cars due to their larger size, weight, and operational complexity, which introduce distinct challenges in traffic management, infrastructure wear, and accident severity. Additionally, the economic implications of AT deployment, given their role in the supply chain, justify a dedicated analysis of their unique risk factors.  
The safety and efficiency of autonomous trucking are contingent upon myriad factors, including traffic volumes, road quality, accident rates, and weather conditions that span a diverse geography [2]. The heterogeneity of risk factors across the U.S. interstate system presents a considerable obstacle. For example, while the I-95 corridor in the Southeast might be prone to high traffic volumes and severe weather patterns, the I-5 in the Pacific Northwest may contend with road roughness due to heavy vehicle traffic and varied topography and climate conditions. Such variability demands a complex yet refined approach to risk assessment that current methodologies do not sufficiently address. Current models forrisk assessment in transportation are either overly broad, neglecting regional variations, or are too fragmented, lacking a holistic view of interstate networks. The literature lacks a granular yet comprehensive risk analysis to facilitate strategic planning and policymaking for AT deployments across different regions of the United States.  
The goal of this paper is to develop and validate a composite risk index that quantitatively evaluates the safety of U.S. interstate routes for the emerging autonomous trucking industry. The aim is to determine the relative contributions of traffic volumes, road quality, accident rates, and weather conditions to the overall risk profile of interstate segments, and how these contributions vary by state and region across the United States. To achieve this goal, this work employs geographic information systems (GISs) for spatial analysis of traffic and environmental data, integrates data from national databases such as the Highway Performance Monitoring System (HPMS) and the Fatality Analysis Reporting System (FARS), and utilizes statistical methods to normalize and analyze risk factors.  
The contributions of this work are multifold. First, it provides a robust, data-driven methodology for risk assessment specific to autonomous trucking. Second, this paper identifies regional patterns in risk factors, enabling targeted risk mitigation strategies. Finally, it supports the assertion that a uniform national strategy for the deployment of autonomous trucking is less effective than a region-specific approach. This research thus offers a significant value proposition for stakeholders in transportation, logistics, and policy development, aiming to navigate the complexities of autonomous freight transportation safely and efficiently.  
The organization of the rest of this paper is as follows: Section 2 reviews the literature on autonomous trucking but with a focus on deployment strategies and risk evaluation. Section 3 describes the workflow developed to source and process data for the effective visualization of risk patterns. Section 4 discusses the analytical results and implications for stakeholders seeking to benefit from AT deployments. Section 5 concludes the research and suggests future work.