Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions

Abstract

The growing demand for equitable and efficient transportation solutions has positioned autonomous vehicles (AVs) as a transformative technology with significant potential for rural areas. This literature review examines the challenges and opportunities associated with AV deployment in rural environments, characterized by sparse infrastructure, diverse road conditions, and aging populations. Using a systematic analysis of field tests, simulation-based studies, and survey research, key obstacles are identified, including limited lane markings, unpaved roads, digital connectivity gaps, and user acceptance issues. The results highlight the critical role of advancements in sensor technology, localization methods, and edge computing in addressing these barriers. Additionally, strategic infrastructure modifications, such as enhanced road signage and reliable communication systems, are essential for AV integration. This paper emphasizes the need for tailored AV solutions to meet the specific requirements of rural settings, including adaptability to adverse weather conditions and mixed traffic environments. Insights into public perception reveal the importance of trust-building initiatives and community engagement to foster widespread acceptance. The findings provide actionable recommendations for policymakers, industry leaders, and infrastructure operators, focusing on scalable deployment strategies, policy adaptations, and sustainable solutions. By addressing these challenges, AVs enhance mobility, safety, and accessibility, transforming rural transportation networks into more equitable and efficient systems. This review serves as a foundational reference for future research, charting pathways for the integration of AVs in rural contexts.

1. Introduction

Rural areas in the US are home to about 20% of the population (with nearly one-quarter of individuals aged 65 and older) and encompass nearly 68% of the nation’s road network and nearly 50% of traffic facilities, a significant portion dedicated to the transport of agricultural products, goods, and personal travel [1]. However, the geographic isolation, sparse population, and extended travel distances that characterize rural communities often create unique transportation challenges. These issues are exacerbated by the lack of access to essential services such as healthcare, grocery stores, and public transportation, which disproportionately impacts elderly and low-income residents. With 47% of traffic fatalities occurring on rural roads despite lower traffic volumes, the need for enhanced road safety in these areas is critical. A Governors Highway Safety Association analysis, based on data from the National Highway Traffic Safety Administration’s Fatality Analysis Reporting System, revealed that in 2022, the fatality rate in rural areas was 1.5 times higher than in urban areas, with a rate of 1.68 compared with 1.15 [2,3]. Furthermore, emergency response times in rural regions are more than twice as lengthy as those in urban areas, highlighting significant disparities in transportation safety and access to timely emergency services [4].

The presence of autonomous vehicles (AVs) in rural areas is becoming increasingly important, especially with the growing elderly population in the US and many other countries. The proportion of people aged 65 and older is projected to increase significantly, doubling from 8.3% in 2015 to 17% of the global population by 2055 [5]. However, research on connected and autonomous vehicles (CAVs) has largely concentrated on highways and urban settings, often overlooking the importance and risks associated with rural roads [6]. Data on travel behavior in rural areas are quite scarce, and there is significant uncertainty regarding how AVs impact travel patterns in these regions. The potential deployment of emerging technologies in rural areas could provide promising solutions to enhance the quality of life, safety, and accessibility for underserved populations. These technologies can address challenges posed by rural road conditions, such as steep grades, limited sight distances, and unpaved roads, while overcoming the infrastructure limitations typical of these regions. However, integrating AVs into rural settings necessitates targeted investments in infrastructure, policy adjustments, and community engagement, considering the unique demographic and geographic needs of rural communities. This paper explores the role of AVs in rural areas, addressing the technological, infrastructural, economic, and policy adaptations necessary to make AV deployment feasible and beneficial. By examining recent research, encompassing field tests, simulation studies, interviews, and survey-based studies, this paper offers insights into how AVs could become a viable and impactful solution for rural transportation, thereby improving safety, mobility, and access to essential services. The systematic review in this paper addresses the following questions:

• How do AV deployment strategies differ between urban and rural environments? [Section 3.2]
• What mobility, safety, environmental, and economic benefits could the deployment of AVs bring to rural communities? [Section 3.3.1]
• What are the key challenges for AV deployment in rural areas? [Section 3.3.2]
• What infrastructure and technological modifications are necessary for the successful deployment of AVs in rural regions? [Section 3.3.3 and Section 3.3.4]
• What roles do public perception and community engagement play in the adoption of AVs in rural settings? [Section 3.3.5]

The study provides key insights for policymakers, the automotive industry, and infrastructure operators (IOOs). For IOOs, it emphasizes the need for infrastructure upgrades, including better road markings, signage, connectivity, and maintenance strategies to support safe AV deployment. This helps prioritize investments for seamless AV integration and regional mobility. For the automotive industry, it highlights the importance of adapting AVs to rural conditions, ensuring vehicle durability, precise navigation, and user-friendly interfaces tailored to rural demographics, enhancing safety and market opportunities.

2. Materials and Methodology

A systematic review (Figure 1) was conducted using Google Scholar and Google Search for broad and comprehensive coverage. The review included peer-reviewed papers, technical reports, theses, and governmental sources. No timeframe restrictions were applied, but only English-language documents were considered to ensure thorough coverage of AV research in rural areas. The methodology comprised three main phases: the literature retrieval, extended web-based search, and thematic analysis using a large language model. The scholarly Python library version 1.7.11 was used to query Google Scholar for academic publications on AVs in rural areas. Keywords aligned with research objectives were combined using Boolean operators (e.g., autonomous vehicles OR Self-Driving Vehicles AND rural areas). Metadata, including title, abstract, author, year, and publication venue, were extracted and structured for analysis. The data were exported as a CSV file to facilitate future updates and reproducibility. This systematic process not only ensured the inclusion of a wide range of academic perspectives but also established a dynamic framework that can evolve with new findings, providing a continually expanding foundation for further investigation. To supplement academic findings, the search was extended to governmental, industrial, and institutional sources, focusing on state DOTs, industry leaders, and authoritative institutions providing data on AV policies and deployments.

Documents such as reports, policy briefs, and technical papers were collected and categorized based on their methodology, including simulation, field test-, survey-, interview-, and conceptual framework-based studies. Extracted metadata (titles, URLs, publication dates, and source types) was structured for integration with academic sources. A thematic analysis was performed using OpenAI’s API to identify key themes across collected references. Abstracts from academic publications and summaries from web-based documents were analyzed programmatically. To ensure accuracy, 90% of references were manually reviewed for relevance. Documents mentioning rural contexts only briefly or focusing on urban settings were excluded. Metadata, including keywords, titles, and abstracts, were verified.

3. Results

A systematic review was conducted to identify studies that assessed AV deployment in rural areas from 2015 to the present. Over the years, the volume of studies on AV deployment in rural settings has risen (Figure 2), reflecting technological and societal factors. Initial research between 2015 and 2017 was limited [7,8,9,10,11], largely due to AV technology still being in its early stages and fewer real-world applications in non-urban contexts. Interest expanded as the technology advanced and garnered more policy and funding support, particularly around 2018–2020 [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35] when government agencies and private industry began actively investing in rural-focused AV research. Concurrently, real-world pilot programs and heightened public awareness fueled an increase in publications as researchers explored issues such as infrastructure readiness, safety, and community acceptance. A slight plateau around 2021 [27,29,30,31,33,34] may be attributed to pandemic-related slowdowns; however, publications rebounded as conditions improved and new avenues of study emerged. The continued rise in the number of studies from 2022 onward illustrates a growing recognition of rural areas as critical environments for testing and implementing AV technology [6,7,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66].

The review identified eight key themes essential to AV deployment: benefits, challenges, user perception, long-term adoption, cost feasibility, physical and digital infrastructure needs, and technological advancements (Figure 3).

These themes stem from four assessment approaches: field tests, simulations, interviews/surveys, and conceptual evaluations (Figure 4). Field tests provide crucial insights into AV performance under real-world conditions, simulations complement these tests by modeling AV behavior in controlled environments. Combined, these methods highlight deployment challenges, infrastructure needs, and necessary technological upgrades. User perception and acceptance, gathered through interviews and surveys, play an important role in shaping long-term planning and adoption strategies. These insights are tied to studies on economic feasibility, which assess the financial sustainability of AV implementation by evaluating the necessary investments. Together, they provide a holistic understanding of how to optimize AV deployment in rural areas, balancing public acceptance with economic viability.

3.1. Field Test-Based Studies for AVs Deployment in Rural Areas

Table 1. Recent field test studies on AVs in rural areas.

Project Name/Author; Year	Organization	Type *	Route/Road Types
DriveOhio Project; (2023–2024) [69]	Drive Ohio (U.S. DOT)	Demo	Fixed and on-demand
ADS for Rural America Project; (2021–2023) [70]	University of Iowa	Demo	Fixed routes
Mason et al.; (2022) [50]	University of Iowa	Demo	Fixed routes
Joseph George Walters; (2022) [44]	University of Nottingham	Demo	On-Demand routes
goMARTI Project; (2022–2024) [71]	Minnesota’s May Mobility	Deploy	On-Demand routes
DriveMN Project; (2022) [72]	Minnesota DOT	Deploy	Fixed routes
CASSI Project; (2020, 2021, 2023) [67]	North Carolina DOT	Demo	Fixed Blacktop, parking lots
TEDDY Project; (2022) [73]	National Park Service	Demo	Fixed Blacktop, parking lots

* Note: Demo: demonstration; Deploy: deployment; DOT: Department of Transportation.

summarizes the most recent field-based test studies on AVs in rural areas, spanning from 2020 to 2024, with durations ranging from under a year to multi-year projects. The tests fall into two categories: demonstration and deployment. Demonstration projects are exploratory and broad in scope, serving as precursors to deployments, which focus on specific applications and can lead to full services. Two types of AVs are commonly used: low-speed, box-shaped shuttles for short-distance transport and traditional vehicles retrofitted with automation technology, capable of autonomous or manual operation in mixed traffic [54]. Speed capabilities range from 10 mph [67] to 70 mph [68], depending on vehicle design and conditions. High-speed tests focus on highways, while low-speed tests emphasize urban and pedestrian areas. Safety and oversight are prioritized, with most projects using safety drivers or operators to mitigate risks. Hybrid approaches, such as human-driven data gathering, enhance data accuracy.

The ADS for Rural America project (2021–2023) [70], led by the University of Iowa, evaluated autonomous driving systems (ADS) across six phases [74], testing scenarios like controlled-access highways [75], undivided highways [76], intersections [49], unmarked roads [48], V2V communication [49], and parking [77]. A custom Ford Starlite Transit bus with SAE levels 2 and 3 automation, advanced sensors, and V2I communication was used [33,78]. Data collection included kinematic data, operational challenges, passenger trust, stress, weather conditions, and feedback through questionnaires [24,69,79,80]. Mason et al. (2022) [50] explored the impact of ADS on older adults’ mobility in rural areas with limited public transport by conducting shuttle rides on Iowa’s rural routes, including highways and gravel roads. Surveys measured participants’ trust, safety perception, and anxiety, with automation levels progressively increasing from 58% in Phase 1 to 93% in Phase 3. The goMARTI (Minnesota’s Automated Rural Transit Initiative) [70] provided AV services in Grand Rapids, Minnesota, marking the first large-scale rural AV deployment in harsh winter conditions [81]. Using May Mobility-equipped Toyota Sienna Autono-MaaS vans, including three wheelchair-accessible vehicles, the project covered a 17-square-mile area with 75 pick-up/drop-off points [81]. Operating until spring 2024, it addressed rural challenges like severe winters, limited transportation for the aging population, and community engagement. As of May 2024, five autonomous shuttles had completed 24 months of service, covered over 150,000 miles, and connected key locations like the airport, high school, and hospital, offering free, on-demand rides [37,82]. In 2021, the National Park Service (NPS) launched two pilot projects, TEDDY and connected autonomous shuttle supporting innovation (CASSI), as part of its Emerging Mobility Initiative. These projects tested automated shuttles to provide public transportation on recreational lands. The TEDDY project used two Olli shuttles, developed by Local Motors and operated by Beep, to transport visitors between the Canyon Village area in Yellowstone National Park and nearby lodges and campgrounds. The CASSI program, led by the North Carolina Department of Transportation, deployed Navya low-speed, all-electric shuttles on predefined routes at Cary’s Bond Park, UNC Charlotte, and the Wright Brothers National Memorial, integrating V2I communication and first-mile/last-mile transit solutions. The Cary pilot operated on a 1.6-mile loop, covering 250 service miles, while at UNC Charlotte, the shuttle traveled 745 miles along a 2.2-mile route [46,67,83,84]. The Drive MN initiative, led by the Minnesota Department of Transportation, tested two research CAVs across more than 1087 miles on Minnesota’s rural roads to assess roadway readiness for automated driving. The project covered diverse routes, including two-, four-, and six-lane asphalt and concrete roads, with traffic signals, roundabouts, J-turn intersections, and construction zones. Tests were conducted under challenging conditions like fog and glare [43,72]. Joseph George Walters (2022) explored how connected, autonomous, and electric vehicles (CAEVs) could support sustainable rural transport development in the UK [44], addressing issues like accessibility, safety, economic isolation, and limited digital connectivity. The research included field tests assessing GNSS NRTK positioning accuracy and the impact of environmental factors on DSRC signal strength. Logistical constraints and the COVID-19 pandemic limited the scope of these tests. Walters developed the CAEV Rural Transport Index (CARTI) to help transport planners assess rural areas’ needs and readiness by considering factors such as emissions, public transport access, and internet coverage. The DriveOhio Automated Driving Systems (ADS) for Rural America project [69], a USDOT initiative, tests CAVs on rural Ohio roads to improve safety and mobility in challenging environments like hilly terrain, winding roads, and construction zones. Heavy-duty trucks, equipped with Bosch Platoon Controllers and automated emergency braking, and light-duty vehicles, using AutonomouStuff for localization and object detection, were tested with radar, LiDAR, cameras, GPS, and V2V communication. Data on vehicle location, speed, steering, disengagements, and environmental conditions are stored on Amazon Web Services. The project highlights AVs’ potential to enhance rural mobility, particularly for older residents, and refines AV technologies for complex environments [68,85].

3.2. AV Simulation-Based Studies in Rural Areas

Different road environments require specific behavioral models to accurately simulate traffic. Urban and rural areas demand different models, with most microscopic traffic simulations designed for urban or freeway networks. Commonly used models include AIMSUN, VISSIM, Paramics, MITSIMLab, and CORSIM. In contrast, relatively few models focus on simulating two-lane highways with oncoming traffic. Key models for rural networks include the two-lane passing (TWOPAS) model [86], the traffic on rural roads (TRARR) model [87], and the VTISim model [88], which later evolved into the rural road traffic simulator (RuTSim) [89]. A core element across all environments is the car-following model, while speed adaptation models are needed to calculate desired speeds. For urban and freeway settings, lane-changing decision models are essential, simulating driver lane-change decisions based on traffic flow. For two-lane highways, more complex overtaking models are required to simulate lane changes, passing maneuvers, and returns to the original lane. Gap-acceptance models are crucial in both lane-changing and overtaking scenarios, as well as at intersections, lane drops, and on-ramp merges. Desired speed on urban and freeway roads is typically influenced by speed limits, while rural road speeds depend on factors like road width and curves. Speed adaptation models adjust speed preferences based on these characteristics. On rural highways, where overtaking opportunities are limited, vehicle platoons commonly form. Therefore, rural simulations must focus on platoon formation rather than individual vehicle movement [90]. Johan Olstam (2009) [91] focused on enhancing realism in driving simulators for rural areas by developing models that reflected vehicle platooning and overtaking behavior on two-lane highways. The study refined a microscopic traffic simulation model (VTISim) and introduced a moving window approach to simulate nearby vehicles in detail while optimizing computational efficiency. An algorithm managed transitions between autonomous and controlled vehicle behaviors, balancing realism and reproducibility. The simulations emphasized the importance of modeling vehicle platoons, common on rural highways due to limited overtaking opportunities. Experiments confirmed realistic traffic flows, with overtaking behavior and speed adaptation accurately simulated, although some vehicles displayed aggressive tendencies typical of rural roads. Vigne et al. (2024) [6] developed a framework for safe, personalized autonomous overtaking on two-lane rural roads. It integrated perception, navigation, and control layers. The perception layer modeled the environment using real-time sensor data, while the navigation layer employed a finite state machine and a fuzzy inference system to adapt overtaking styles. Relaxed settings prioritized comfort, while sporty settings enabled dynamic maneuvers. The control layer managed braking, acceleration, and steering to ensure collision-free, efficient trajectories. Simulations with Carmaker demonstrated effective performance, with dynamic safety checks optimizing user comfort and road safety. Simulation-based studies on AVs in rural areas (

Table 2. Relevant simulation-based studies on AVs in rural areas.

Study	Year	Country	Software	Main Goal
Johan Olstam [91]	2009	Sweden	VTISim Model	Enhance the realism of driving simulators for rural traffic conditions
Manawadu et al. [11]	2015	Japan	Driving Simulator	Evaluate driving experiences under autonomous and human-driven modes
Joel Norman [14]	2019	Sweden	Discrete-event simulation model	Assess AV integration in rural public transport systems
Schlüter et al. [29]	2021	Germany	MATSim	Evaluate DRT systems’ economic and environmental viability
Walters et al. [44]	2022	England	Agent-based simulation framework	Replace low-utilization train lines with AMoD systems
Cazares et al. [42]	2023	USA	Microscopic traffic simulation framework	Enhance safety and control for AVs and CAVs
Abohassan et al. [41]	2024	Canada	VISTA simulator	Assess AV data processing requirements
Vigne et al. [6]	2024	France	Carmaker realistic simulator	Improve safety and comfort for CAV overtaking
Fujiu et al. [56]	2024	Japan	Aimsun SDK	Evaluate AV impact on traffic flow

) focus on reducing delays, enhancing curve navigation, ensuring safe overtaking, and optimizing environmental and operational benefits for cost-effective rural transport.

3.3. Future of AV Deployment in Rural Areas

3.3.1. Benefits

According to field test-based studies, autonomous transportation systems enhance safety by maintaining reliable control during highway merging, urban navigation, and gravel road travel. AV technology reduces human error through features like lane-keep assist and adaptive cruise control, ensuring consistency in managing safety-critical events in rural areas [46,48,49,75,76,77,92]. Additionally, AVs address rural mobility challenges by improving access to essential services such as healthcare, grocery stores, and education, often requiring long-distance travel [46]. They provide transportation solutions for individuals who cannot drive, including young people, the elderly, those with disabilities, and individuals without access to personal vehicles [37,46,70,93]. AVs also support local tourism, offering novel visitor experiences while fostering awareness and education about technology’s benefits for rural communities [46]. Electric AVs promote sustainable practices by reducing noise and emissions and supporting clean energy goals [46,93]. Progressive project phases demonstrate AV adaptability and continuous improvement through real-world learning [70]. Data collected on vehicle behavior and environmental interactions inform system refinements, leading to future advancements [46]. Furthermore, AV deployment can help with infrastructure upgrades, such as improved road markings and communication networks, benefiting all road users, including human drivers. Additionally, by reducing transportation barriers for goods and services, AVs enhance rural economic growth and accessibility [43]. Simulation-based studies, such as the research conducted by Fujiu et al. (2024) [56], investigated the benefits of AVs in rural areas by analyzing how varying levels of their integration affect traffic flow. The study focused on Suzu City in Ishikawa Prefecture, Japan, a rural area with a high elderly population and limited public transport. Using C++ on the Aimsun traffic simulation SDK platform, the research modeled traffic conditions and AV behavior, incorporating data from person-trip surveys and traffic census reports. Custom algorithms simulated key AV behaviors, including car-following dynamics, vehicle spacing, and reaction times. AV penetration rates between 10% and 100% were tested to evaluate their impact on traffic delays. Field tests were conducted on a 6.6 km route in Suzu City, including urban-like and rural segments, with a fully autonomous 13.2 km round trip. Data from these tests validated and refined the simulation models. The results showed that at low AV penetration (10–45%), delays increased slightly due to mixed traffic inefficiencies. However, once penetration reached 45% to 50%, delays dropped significantly and stabilized, indicating that a critical mass of AVs is necessary to improve traffic efficiency in rural areas. Conceptual assessment-based studies, such as Johnatan Dowds et al. (2021) [34], explored the potential benefits and research needs of AVs in rural America, focusing on safety, mobility, and accessibility. Changes in conceptual assessments and mechanisms for vehicle miles traveled (VMT) were reviewed using travel demand elasticity and preference surveys. Findings indicated that rural areas could see greater safety and mobility benefits due to higher fatal crash rates, longer travel distances, an aging population, and limited transportation options. However, AV sharing models may offer fewer efficiency gains due to low population density. The study emphasized the need for rural-specific policies and research to ensure equitable AV deployment.

3.3.2. Challenges

The deployment of AVs in rural areas faces challenges across environmental, technological, infrastructure, and policy dimensions. Prioleau et al. (2020) [13] identified four key barriers to electric AV adoption in rural areas: financial constraints, infrastructure limitations, policy issues, and demographic factors. The study stressed that integrating AVs with public transit must be cost-effective and include an assessment of current mobility technologies. Community engagement with vulnerable groups, including the elderly, minorities, and individuals with disabilities, is critical for shaping inclusive policies and deployment strategies. Axelrod (2019) [21] examined the challenges of deploying AVs on unpaved roads and in environments with poor weather, limited sight distances, and unpredictable human and animal behavior. The study noted the disparity between high investments in AV technologies and insufficient road infrastructure upgrades, which are essential for safe AV operations. The study proposed a layered infrastructure approach, classifying roads by their AV compatibility, from AV-only roads to shared routes and “last mile” access. The findings of the study highlighted the need for concurrent development of AV technology and infrastructure, with public investments recommended to support upgrades. Similarly, Theoto and Kaminski (2019) [23] analyzed AV readiness across nine countries, showing that strong technological infrastructure, supportive policies, and public acceptance drive progress in regions like Singapore, Germany, Japan, and the U.S., while other countries face setbacks due to limited resources and infrastructure. The study recommended fostering partnerships between governments and industry, creating supportive policies, and investing in infrastructure to enhance safety and accessibility. The challenges of AV deployment are listed in

Table 3. Challenges for AV deployment in rural areas.

Technological	Environmental	Infrastructure	Policy and Demographic
Sensor misdetections, Unreliable responses to unmarked roads	Extreme weather conditions	Faded or low-contrast markings	Financial constraints
GPS and localization failures	Unpaved roads, steep gradients	Inconsistent lane designs	Cultural skepticism toward AV technologies
Battery performance issues	Sharp curves with limited visibility	High infrastructure costs	Limited funding for rural transportation projects
Miscommunication at intersections	Dynamic lighting transitions (shade to sunlight)	Unregulated intersections (roundabouts, stop signs)	Lack of local expertise and operational data
Software malfunctions	Obstacles (animals, farming equipment, pedestrians)	Shortage of charging stations	High deployment costs
Object detection failures	Vegetation near roads	Need for frequent HD mapping updates	Demographic barriers
Difficulty reversing in automated parking	Active construction zones with confusing markings	Limited connectivity-ready roads
Frequent disengagements of automation systems	Roadside variability (curves, hills, dynamic lanes)	Insufficient digital infrastructure

.

Field-test-based studies highlight that sharp curves, unpaved roads, limited sight distances, extreme weather, and obstacles like slow-moving farming equipment and animals present significant challenges for AVs in rural area field tests [43,48,75,76,92,93]. Environmental variability, such as shifts from shaded areas to direct sunlight, limited visibility around curves and hills, and active construction zones, complicates automation. In work zones, visual disturbances, including faded or conflicting lane markings and crack seal lines, can confuse sensors, causing misdetections [68]. Drive MN [43] identified four major issues: poor lane visibility, freeway ramps and turn lanes, construction zones, and low-contrast road markings. Dynamic lanes, faded edge stripes, unclear or inconsistent lane markings, and gravel roads often lead to detection errors and path deviations. Frequent human overrides further stress the need for robust and reliable systems [70]. Additionally, connectivity issues, including poor cellular networks, hamper V2X communication and real-time data sharing in rural areas [49,68].

Besides physical and digital infrastructure limitations, technological issues, including GPS failures, object detection errors, miscommunication at signalized intersections, and software malfunctions, cause disengagements and reduced reliability concerns [67,68,70,74,94]. For example, reversing during automated parking [77], navigating unmarked roads [43,48,94], and inconsistent responses to obstacles highlight the need for system improvements. Operational challenges include battery issues, particularly in hot weather, where air conditioning strains the battery, requiring midday charging breaks and reducing efficiency [94]. Inclement weather and technical issues also cause service interruptions. Interactions with pedestrians and other vehicles further complicate operations [45,94]. High infrastructure costs remain a major barrier to quality AV implementation in rural areas [44,45,83].

Interview-based studies complement these findings by exploring deployment challenges and potential solutions through qualitative analysis. Eddy et al. (2017) [10] aimed to ensure that rural communities benefit from CV advancements in road safety and freight movement. The methodology involved a workshop at the National Rural Intelligent Transportation System meeting, expert interviews, and a literature review on rural transportation needs. Key challenges identified include limited resources, infrastructure constraints, high costs, inadequate communication networks, and cultural skepticism toward new technologies. Despite these barriers, CVs offer potential benefits such as improved road safety, better data collection, and enhanced freight movement. The study recommended sustainability plans, trial deployments in high-impact areas, innovative funding mechanisms, and outreach programs to build awareness and acceptance of CV technologies in rural communities.

3.3.3. Necessary Infrastructure Modifications

While significant resources have advanced onboard AV technologies such as sensors, control systems, and remote-operation capabilities, less focus has been placed on modernizing roadway infrastructure to support these innovations. This disparity has resulted in increasingly complex onboard systems designed to handle inconsistent road conditions and unpredictable behaviors of humans and animals. For seamless AV integration, aligning technological advancements with strategic infrastructure improvements is crucial. Axelrod (2019) [21] proposed a layered infrastructure approach, classifying roads as “self-driving eligible”, “depot-to-depot”, and “last-mile” routes, each requiring tailored modifications. V2I systems and advanced traffic management are critical for real-time data sharing and safe operation in mixed traffic. Safety measures must address interactions with pedestrians, animals, and agricultural vehicles to ensure effective AV deployment in rural areas. The field-test-based studies emphasize the need for robust physical and digital infrastructure to support AV deployment. Advanced ADS-equipped vehicles collected extensive data to identify infrastructure deficiencies, such as poor lane visibility, faded markings, and inconsistent lane designs, which often led to sensor misdetections [43,72]. Physical enhancements include upgraded road surfaces with clear, high-contrast markings, retroreflective and wet-reflective pavement markings for improved visibility under adverse conditions, and connectivity-ready roads [94]. The Drive MN study stressed updating pavement marking guidelines, addressing outdated or confusing markings, implementing dotted line markings for better lane guidance, and incorporating performance standards into traffic engineering manuals [43]. The goMARTI project highlighted optimal stop locations near parking lot entrances or trail crossings and explored rural-adapted mobility hubs for improved accessibility [37]. The CASSI program also recommended temporary modular ramps at stops to accommodate riders with mobility devices [93]. Robust digital infrastructure, including cloud-based data processing, HD mapping, and reliable communication systems, is essential for seamless AV operations [68,70]. In rural areas, frequent HD map updates are necessary to reflect unpredictable road conditions such as unpaved roads, steep gradients, and sharp curves. CASSI pilots at Cary’s Bond Park and UNC Charlotte tested V2I systems with RSU-equipped traffic signals for better integration, highlighting infrastructure readiness as critical for success [93,94]. Modifications, such as adding charging stations, localized GPS with RTK base stations, and accessibility features like modular ramps, were identified as key for successful CAV integration. Drive Ohio [68] stressed upgrading traffic management systems to address rural traffic patterns, including roundabouts, stop signs, and unregulated crossings. Sensors, cameras, and detection systems at intersections are considered essential for safe AV navigation and accommodating road users like agricultural vehicles, freight trucks, and wildlife. Mason et al. (2022) [50] also emphasized refining high-speed maneuvers to improve user comfort and support broader ADS acceptance among transportation-challenged populations. Focusing on a simulation-based approach, Abohassan et al. (2024) [41] investigated AV interactions with static rural environments using a LiDAR-based simulation framework. Digital replicas of rural roads in Alberta, Canada, were created via the VISTA simulator to assess how roadside vegetation, road geometry, and weather conditions affect AV data processing needs. Findings revealed that roadside elements could increase data demands by up to 400%, while heavy rain raised requirements by 240%. Road curves also impacted data rates, with crest curves reducing them by 4.2% and sag curves increasing them by 7%. Minor infrastructure changes, like lane additions, raised data rates by 12% to 16%. These insights offer guidance for optimizing AV navigation and rural infrastructure design. Cazares et al. (2023) [42] addressed safety and control challenges for AVs and CAVs on rural roads, particularly on horizontal curves, where inconsistent road designs often cause crashes due to speed and steering errors. Using a custom microscopic traffic simulation, we tested four configurations: human-driven vehicles, non-connected AVs, AVs following human-driven vehicles, and CAVs receiving road data via inter-vehicle communication. Safety metrics, including jerk, time-to-line crossing, and cross-track error, were analyzed. Results showed CAVs improved longitudinal safety by anticipating road conditions and reducing abrupt deceleration. However, lateral safety improvements were limited due to fixed look-ahead distances. The study recommended adaptive lateral control models to enhance CAV performance on rural curves.

3.3.4. Necessary Technological Modifications

Technological advancements play a crucial role in overcoming AV localization, detection, and navigation challenges in rural areas. To address issues like limited GNSS signals under tree canopies, outdated point cloud maps, and high environmental variability, Matute et al. (2023) [65] introduced a sensor fusion-based localization framework integrating data from GNSS, LiDAR, INS, and vehicle odometry. A loosely coupled extended Kalman filter and weighted gate mechanism improved state estimation, reducing lateral deviations by 71% and ensuring safe localization in complex rural settings. Field tests validated its reliability in maintaining accurate positioning despite GNSS disruptions and outdated maps. While cloud networking and edge computing have enhanced AV functionality in urban areas, rural deployments face challenges due to poor road conditions and limited telecommunications infrastructure for V2I connectivity. Connectivity improvements, including V2I and V2V systems paired with machine learning models, can address challenges such as tight curves and poor lane visibility [43]. Agnello et al. (2024) [66] tackled rural road detection using a lightweight MobileSAM model optimized with Apache TVM on a Jetson Nano. Field tests suggest future optimizations could improve speed and accuracy, enhancing AV navigation in underdeveloped regions. Field test-based studies emphasized that enhanced sensors, such as LiDAR, radar, and cameras, are critical for detecting obstacles like farm equipment, wildlife, and unmarked roads, especially under low-visibility conditions such as fog, snow, and shadows [48,76,93]. HD maps play a key role in navigating areas without traditional lane markings, while virtual speed limits and adaptive throttle adjustments enhance safety on curves and blind hills. For gravel roads, AVs shift closer to the center to avoid loose gravel, and a “nudge” feature adjusts positioning for oncoming traffic [74]. Advanced traction control and adaptive suspension improve stability on uneven surfaces, while decision-making algorithms handle dynamic responses to intersections, stops, and yield-controlled areas [74]. Enhanced parking automation uses Open Space Planner software for angle parking and redefines parallel parking spaces as short additional lanes to accommodate AVs with larger turning radii [77]. Battery efficiency optimization through advanced capacity and thermal management systems ensures extended operational hours during high-demand periods, such as air conditioning usage [45,93]. Weatherproofing and obstacle detection upgrades further enhance performance in diverse conditions.

3.3.5. User Perception and Acceptance

Technology reliability and road safety are key factors influencing rural residents’ acceptance of AVs, alongside accessibility, enjoyment, and ease of use, although privacy concerns negatively impact adoption [64]. Zhong et al. (2024) [59] highlighted key differences between rural and urban areas. Rural residents are less likely to adopt AVs due to affordability issues, poor infrastructure, and low population density, which limits benefits like reduced congestion and pollution. Many prefer conventional vehicles, feel uneasy sharing roads with AVs, and exhibit cautious attitudes, whereas urban residents are more open, driven by greater exposure to congestion and shared mobility. Urban residents value time savings but weigh anticipated benefits against costs and technological uncertainties. According to a study by Kornélia Lazányi (2023) [95], the acceptance of autonomous vehicles (AVs) and the perceived risks associated with them are multifaceted phenomena shaped by the interplay of personal and cultural contexts. The study emphasizes the importance of considering both demographic and cultural factors when designing strategies to promote the acceptance of AVs. Field test-based, simulation-based, and survey-based studies have comprehensively evaluated AV acceptance factors. User perception and acceptance of ADS also vary depending on system performance during different deployment phases, particularly in complex tasks like intersections and parking [73]. Gradual improvements in AV technology have helped build trust, although unpredictable conditions remain a concern. Studies show that direct exposure and experience enhance acceptance, as demonstrated in CASSI pilots, where 83% of users at UNC Charlotte and 92% at Cary expressed positive experiences [93,94]. Users valued safety and convenience but noted issues such as slow speeds, limited accessibility features, and unstable ramps that struggled with varying device sizes and weights. The goMARTI project found strong acceptance among users without reliable transportation, reflecting the potential for AV services to address rural mobility needs [37]. Community engagement and enhanced accessibility features foster user trust, as seen in national park pilots (TEDDY and CASSI pilots), where 98% of users felt safe despite requesting smoother operations and avoiding jerky stops [73]. In the ADS for rural areas project, trust in AVs rose from 60% to 75% post-ride, with perceived reliability increasing from 63% to 81% [77,92]. These findings align with the results of Das et al. (2020) [96], who examined survey data from non-motorists in Pittsburgh. Their study found that AV perceptions varied across stakeholder groups, with those having prior AV interaction expressing greater interest and higher expectations. In contrast, participants skeptical about AV safety were opposed to using the city as a proving ground. The DriveOhio project emphasized rural residents’ concerns about AVs navigating unmarked roads, sharp curves, and limited infrastructure. Older adults and those with lower education initially had less trust but showed increased acceptance after demonstrations [68]. The MN Drive Project [43] and Mason et al. (2022) [50] highlighted rising acceptance through exposure. Surveys from events like the 2022 Drive MN survey and earlier studies (2018 Minnesota State Fair and 2020 Live CAV Minnesota) proved this fact as well. However, issues like slow speeds, indirect routes, and limited accessibility features reduced usability, particularly for individuals with disabilities. Mason et al. found that older adults experienced reduced suspicion and increased trust after riding autonomous shuttles, though anxiety spiked during high-speed maneuvers like highway merging. Over time, participants became more comfortable with AV interactions involving pedestrians and cyclists [50]. Regarding simulation-based studies, Manawadu et al. (2015) [11] evaluated AV performance in simulated rural environments with low traffic and sudden diversions. Novice drivers preferred AVs due to their safety and reliability, while experienced drivers favored manual control, citing greater enjoyment. Li et al. (2023) [38] examined AV adoption in small towns with limited public transit through an interview-based study and found that AV services, such as ENDEAVRide in Texas, improved access to essential services for older adults and those with disabilities. However, safety concerns and job losses in driving-related fields persisted. Chang and Williams (2023) [39] further identified rural concerns about AV reliability on rural roads, safety in various weather conditions, and limited internet infrastructure through an online survey of 1247 rural and non-rural respondents. Trust levels were similar across both groups, though older individuals and those with lower education exhibited less trust. Key concerns among rural residents included technology failures, hacking, and the lack of human interaction in AVs. Hesitation was also linked to unfamiliar terrain and limited internet infrastructure. Hasnat et al. (2023) [58] used the recent household survey data to analyze the impact of CAV adoption on residential location choices, finding that high CAV adoption encourages suburban and rural growth by enhancing travel efficiency, while high AV penetration alone might worsen network conditions, potentially diminishing rural living’s appeal. Gale et al. (2022) [61] surveyed rural and urban participants and found that rural respondents prioritized performance and availability, while urban respondents focused on convenience and travel time. Both groups valued mobility benefits and enjoyment, suggesting AV development and policymaking must address distinct rural needs. A qualitative study by Hilgarter and Granig(2020) [97] in Carinthia, Austria, explored public perceptions of AVs after real-life rides in an autonomous shuttle (SAE Level 3) within mixed traffic. Interviews with 19 participants revealed that AVs are seen more as a complement than a replacement for current transport options, especially in rural areas. Participants generally held positive attitudes, with safety perceptions influenced by AV experience and speed. Copp (2019) [26] and Kaufleitner (2017) [8] highlighted AV solutions to improve mobility for rural seniors and non-drivers, emphasizing infrastructure upgrades like improved road markings, wheelchair ramps, and audible stop announcements. AVs could complement volunteer driver programs, but challenges such as safety concerns, legal liability, and costs persist, especially on poorly structured roads. Tailored AV solutions are essential in rural areas due to their reliance on private vehicles and limited public transport. Legal liability in accidents was identified as a critical factor for successful implementation.

3.3.6. Long-Term Planning and Deployment

Long-term planning and deployment strategies for AVs in rural areas require a phased approach and a layered infrastructure strategy to address unique regional challenges. A phased deployment begins with demonstration projects and low-speed shuttles, allowing AV systems to adapt to rural complexities through iterative testing before transitioning to full-scale services [70,79]. The layered strategy categorizes roadways into AV-only roads, shared roads, and “last-mile” routes, optimizing compatibility and performance [73]. Infrastructure readiness, technological advancements, and user acceptance remain critical pillars for successful adoption. DriveOhio and the CASSI Project emphasized the importance of upgrading unpaved roads, improving road markings and signage, and enhancing telecommunications for reliable V2I communication [68,73]. Collaboration with local policymakers, as highlighted by the Drive MN project, is essential for addressing region-specific challenges, sharing findings with decision-makers, and maintaining standardized traffic engineering standards and pavement markings [43]. Community engagement is key to ensuring AV solutions meet local needs, particularly for the elderly and individuals with disabilities, fostering public trust. Government-private sector partnerships can secure funding and implement scalable, cost-effective solutions, including shared mobility services. Pilot projects enable comprehensive data collection to refine AV technology and create a foundation for future deployments [73,84]. Sustainable deployment examples, such as the National Park Service project, demonstrate how improved battery technology and weather-resilient designs can scale operations and integrate automated shuttles into existing transport systems to support rural mobility [73]. Simulation-based studies, like Joel Norman’s (2019) [14] research on Swedish municipalities, provide further insight into AV applications. By using a discrete-event simulation model, the study found that AV-based first- and last-mile feeder services could reduce driver costs, improve accessibility, and offer flexible transport during low-demand periods. Driverless shuttles capable of serving 100–150 passengers daily were identified as a viable solution for rural areas, with public transport authorities playing a key role in addressing local challenges and optimizing deployment.

3.3.7. Costs and Economic Feasibility

The economic feasibility of AV deployment in rural areas hinges on overcoming high infrastructure and operational costs while leveraging opportunities for enhanced mobility and cost-efficient transport options. As highlighted by field tests, initial investments include upgrading road surfaces, signage, charging facilities, and cellular networks to support V2X communication, along with procuring AV shuttles, creating HD maps, and equipping vehicles for weather resilience [72]. Recurring expenses, such as maintenance of AV software, hardware, sensors, and retroreflective road markings, further add to the financial burden [73,74]. Operational costs remain high due to manual interventions and the need for expanded charging infrastructure, limiting scalability. Rural residents also express concerns about the affordability of infrastructure development and vehicle ownership [72]. Shared mobility services and government-private sector collaborations are vital to enhance scalability and affordability. The ADS for Rural America project highlighted that, despite initial costs, AV deployment could deliver long-term economic benefits by enhancing rural mobility, supporting local economies, improving community connectivity, and reducing reliance on human-driven vehicles [80]. Other benefits, such as improved visitor experiences, reduced traffic congestion in sensitive areas, and better accessibility, could gradually offset costs [73]. Simulation-based studies offer valuable insights into achieving cost efficiency. Schlüter et al. (2021) [29] evaluated autonomous demand-responsive transport (DRT) in Germany’s Bremerhaven region using the MATSim simulation framework. Pooling trips and optimizing fleet sizes reduced operational expenses, vehicle kilometers traveled, and environmental impact, with fleet sizes cut by over 80% in some scenarios, demonstrating substantial cost savings in sparsely populated areas. Sieber et al. (2020) [18] explored replacing low-utilization rural train lines in Switzerland with autonomous mobility-on-demand (AMoD) services. Simulations showed that AMoD reduced travel times and operational costs on three out of four train lines, offering higher service levels compared to trains. However, on the Tösstal line, high passenger demand reduced cost-effectiveness, with human-driven on-demand services outperforming AVs in some cases. Overall, optimizing AV technology to minimize operational costs while ensuring reliable service is crucial for sustainable rural deployment. Simulation studies and field trials demonstrate that AV-based transport systems can improve rural mobility and cost efficiency, but strategic planning and strong collaboration between stakeholders are essential to address economic challenges and realize long-term benefits.

4. Discussions

The deployment of AVs in rural areas offers significant potential to address transportation challenges related to safety, mobility, and accessibility for underserved communities. Rural settings, characterized by sparse infrastructure, diverse road conditions, and limited connectivity, require advancements in sensor accuracy, localization techniques, and robust infrastructure and digital modification solutions. Field tests and simulation- and survey-based studies demonstrate that AVs can navigate complexities in rural areas, providing a foundation for scalable implementation.

This review synthesizes findings from field-based tests, simulation studies, and interview/survey-based research to better understand the deployment of autonomous vehicles (AVs) in rural areas. Each method offers unique insights, and together they reveal both consistent themes and meaningful contrasts that are important for planning future deployments.

Field test studies offer real-world evidence on AV performance in rural conditions, including challenges such as unpaved roads, limited lane markings, steep curves, wildlife crossings, and adverse weather. Projects like ADS for Rural America and goMARTI illustrate how AVs can operate effectively with appropriate infrastructure upgrades and community engagement. However, these studies also report recurring issues such as sensor misdetections, disengagements due to GPS failures, and difficulties in navigating complex road geometries. Simulation-based studies complement these findings by allowing researchers to test AV behavior across a variety of traffic patterns, road types, and AV penetration rates in a controlled setting. These models often support conclusions found in fieldwork, such as the benefits of infrastructure improvements and route standardization, but also highlight theoretical advantages, like the traffic flow efficiency gains once AVs surpass certain market share thresholds (e.g., 45%). Simulations have also revealed how rural-specific features, such as vehicle platooning and overtaking behavior on two-lane highways, require specialized control algorithms and greater sensor precision. While field tests provide operational data and simulations model performance scalability, survey and interview-based research adds the critical dimension of human perception. These studies consistently emphasize the importance of public trust, safety perceptions, affordability, and ease of use, particularly among older adults and underserved groups. Surveys have shown that direct exposure to AVs increases acceptance, a finding echoed by field tests where user satisfaction rose significantly post-ride. There is a strong convergence across all methodologies on several fronts: the need for physical and digital infrastructure improvements, phased deployment through pilot programs, and community outreach to build trust. However, divergences were also noted, particularly between simulated expectations and real-world outcomes. For instance, while simulations can assume optimal sensor performance and road conditions, field tests often expose the variability and unpredictability of rural environments. Similarly, user perception studies reveal nuanced concerns that may not be evident in purely technical assessments.

5. Limitations

Studies on AV deployment in rural areas face key limitations that affect their generalizability, robustness, and applicability. These limitations span field-based, simulation-based, and survey-based methodologies, each struggling to fully capture the complexities of diverse rural settings, varying user needs, and broader societal impacts. A recurring challenge across methodologies is their restricted geographic and environmental scope. Field studies typically focus on specific regions or short road segments, often overlooking diverse rural challenges such as adverse weather, seasonal changes, and construction zones [43,84]. Similarly, simulation studies rely on static or idealized environments that exclude dynamic factors like moving vehicles, pedestrians, and complex road geometries, making their applicability limited in real-world contexts [41]. While several urban-based simulation studies have examined the impact of AVs on network mobility and safety under adverse weather conditions or in the presence of work zones [98,99,100,101], similar studies focusing on rural contexts remain rare. Survey-based studies further suffer from narrow sampling that restricts generalizability across different rural and socioeconomic populations [38,58].

The reliance on oversimplified assumptions in simulation and survey methods further compounds these limitations. Simulation studies often assume fixed service times, ideal sensor configurations, and controlled conditions while neglecting variables such as unreported incidents, emergencies, and adverse weather like rain or fog [41]. Surveys similarly overrepresent younger or tech-savvy groups, overlooking critical demographics such as older adults and individuals with disabilities, thereby reinforcing a digital divide [38,39]. Field studies, on the other hand, face practical constraints, including the need for frequent manual HD map updates, which make scaling and replication resource intensive. Tests of technologies like GNSS and V2X communication are often conducted in isolation, limiting their real-world integration potential [44]. Data quality and comprehensiveness also present significant challenges across all methodologies. Field studies are often hindered by incomplete drives, equipment failures, and researcher errors, compromising the reliability of collected data [44]. Simulation studies suffer from limited setups, such as the absence of motion platforms and restricted fields of view, reducing the realism of driver behavior modeling [11]. Meanwhile, survey methods rely on self-reported data, which introduces biases and inaccuracies due to discrepancies between participants’ perceptions and actual experiences [61].

Additionally, broader societal impacts and long-term outcomes, such as environmental sustainability, economic feasibility, and community dynamics, are often underexplored. Field studies rarely address these long-term aspects, while simulation and survey methods primarily focus on short-term performance metrics like delays or crash rates. As a result, critical interactions with pedestrians, cyclists, and qualitative insights into user experiences are frequently overlooked [8,10,58]. The lack of sensitivity analyses under varied conditions further restricts the depth and applicability of findings [56]. A major recurring limitation across methodologies is the lack of scalability and transferability of findings to other contexts. Field tests often involve route designs tailored to specific locations, limiting their applicability to broader rural settings [40,45,83,93]. Simulation studies are particularly vulnerable to rapid technological advancements, which can render findings outdated during research [44]. Survey methods often fail to consider technological, cultural, and demographic differences, limiting their ability to provide a holistic view of AV adoption [8].

6. Conclusions and Future Works

This review has demonstrated that although autonomous vehicles (AVs) hold significant potential to enhance mobility, safety, and accessibility in rural areas, their deployment is far from straightforward. The unique characteristics of rural environments, such as sparse infrastructure, low population density, adverse weather conditions, and limited digital connectivity, necessitate tailored policies, technologies, and deployment strategies. Addressing persistent challenges, including infrastructure deficiencies, public skepticism, and economic constraints, requires a multifaceted approach that accounts for the distinct needs of rural communities. Successful integration of AVs in rural regions requires coordinated efforts among researchers, policymakers, industry leaders, infrastructure operators, and community stakeholders. To facilitate AV deployment in rural contexts, the following targeted recommendations are proposed:

• Implement AV Services on Fixed Routes with Upgraded Infrastructure: Initial deployments should focus on well-defined fixed routes that connect key community locations such as hospitals, schools, and downtown hubs. These routes should be equipped with reliable physical infrastructure, clear lane markings, signage, and maintained surfaces to provide stable environments for AV operation.
• Define Minimum Operational Criteria for Rural Road Readiness: Establishing standardized guidelines for physical and digital road features (e.g., road geometry, lane visibility, V2X readiness) can guide both public and private investments and create a common benchmark for AV service eligibility.
• Launch Scalable and Diverse Pilot Projects: Pilot programs like goMARTI and ADS for Rural America demonstrate the value of phased AV testing. Future pilots should include more diverse geographic, road condition settings, and seasonal conditions to better capture the range of rural challenges and inform broader, long-term planning.
• Invest in Community Education and Engagement: Public trust is essential for adoption. Community-focused campaigns, including informational sessions, AV demonstrations, and ride-alongs, can familiarize residents with AVs and address concerns around safety, privacy, and job displacement.
• Promote Shared AV Mobility Services: Shared-use AV models, such as on-demand shuttle services, can enhance cost-efficiency and access in sparsely populated areas, especially for older adults and non-drivers. Public–private partnerships and subsidies will be essential to make such services financially sustainable.
• Focus Infrastructure Improvements on Dual Benefits: Investments such as high-contrast lane markings, digital signage, rural charging infrastructure, and improved cellular/V2X communication networks will benefit both AVs and human drivers. These investments enhance safety and reliability within a mixed traffic flow, and their economic viability remains strong even during the early stages of AV integration.
• Equip AVs for Rural-Specific Environmental Challenges: AV performance should be enhanced under conditions like gravel roads, steep gradients, wildlife crossings, and weather events (snow, fog, glare) with features like adaptive suspension, traction control, and thermal battery management.
• Support Open Data and Collaborative Research: Regional and national databases on AV performance in rural areas, rural traffic behavior, and infrastructure conditions should be shared across academia, government, and industry to accelerate innovation and reduce redundant efforts.

Building on these eight actionable strategies, future research should address several critical questions to further guide deployment efforts:

• What infrastructure modifications can be implemented within a 5-year timeframe to accommodate AVs in rural U.S. regions?
• How can AVs be adapted to operate reliably in adverse weather conditions common to rural areas like the Midwest, Great Plains, and Northeast?
• What public engagement strategies are most effective in fostering trust and encouraging adoption among diverse rural populations?
• How can we ensure equitable access to AV technologies across income groups and geographies?
• What are the long-term impacts of AV deployment on rural mobility, safety, local economies, and environmental sustainability?

By pursuing these research directions and adopting the specific recommendations outlined above, stakeholders can ensure that rural communities not only keep pace with the AV revolution but also become leaders in shaping an inclusive, efficient, and resilient future for transportation.