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